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Ever notice how some engagement processes just seem to be littered with friction? What if you could whip up a solution, as tailored as a custom-made suit, to tackle those issues head-on?

When friction is foe

Picture those little friction points along your customer and partner engagement journeys. They're like mischievous troublemakers, snatching away efficiency and turning satisfaction into a game of hide-and-seek.

This can really put a damper on the experience for your dear constituents. And no matter how important the task is, getting them to act can sometimes feel like trying to move a mountain. And even when they do, they're taking the scenic route – not the most straightaway path you had in mind. Your customers and partners end up resorting to old-school ways. Although it’s not the smoothest ride, it’s simply what they know best.

If these exchanges contain sensitive files or Personally Identifiable Information (PII), the chance of unexpected detours increases, with data wandering into places it has no business being in.

Communication doesn’t need to jigsaw through these confusing twists and turns. You're doing your best to smooth out the snags with the tech tools you've got. But either those tools can't shape-shift into the solutions you need, or getting them to do so requires serious magic from internal and external technical teams. And let's face it, we'd all rather spend less time pulling rabbits out of hats.

The salient sidekick

Imagine waving goodbye to fitting square pegs in round holes. Instead, say hello to a composable application that serves as your engagement sidekick – amplifying all investments and pushing productivity throughout.

Your apps, portals, websites, phone lines, chat channels – all your interaction channels – stay right where they belong. But now, they're turbocharged gateways to a secure digital world, finely tuned and purpose-built by you to bring every task to completion.
Whether reaching out or fielding requests, you meet everyone on their turf. Then, with an empathetic nudge, usher them through a series of dynamic prompts, straight lined towards resolution. It's like offering VIP self-service to your customers and partners for every touchpoint, from short micro-engagements to multi-phase journeys.

Better days ahead

Customer Experience Automation is like a gust of fresh tech air – it's the rapidly emerging category of solutions designed to light up real-time engagement. Imagine connecting those ever-changing digital front ends with super-smart automated backends, creating a seamless end-to-end approach that benefits all humans involved.

And here's the kicker: this shouldn’t be some complex code wizardry. It works best as a no-code zone, intended for your enterprise to swiftly adapt to shifting customer demands by avoiding long lead times and extra hands on deck.

All this jazz leads to one powerhouse goal: nailing operational excellence through the pure magic of transformative customer experiences.


Customer Experience Automation™ (CXA) is the application of an AI-powered platform that is purpose-built to automate, scale, and remove the friction from the interactions between a company and its customers; from the beginning of a conversation through its resolution. CXA brings together knowledge work automation and conversation automation, to not only intelligently interact with customers, but also interface with backend systems to complete all necessary tasks.

Often the application of automation technology is focused either on handling only front-end customer service inquiries or managing back-office processes. CXA, by comparison, is able to provide the digital consumer self-service options that customers want both wherever the customer reaches in seeking service, and whenever a company reaches out to share or request information.

For insurance carriers, Customer Experience Automation™ offers opportunities to simplify conversations and automate repetitive tasks. Tasks like data collection, and multi-step business processes are faster, easier, and more accurate using CXA. It injects agility and versatility into historically sluggish processes; everything from quotes and onboarding to benefits enrollment, billing, and claims.

Intelligent Process Automation is the next evolution for automation technology, where RPA, NLP, and AI intersect and become something better.
Read more on IPA

Examples of Customer Experience Automation™ for Insurance

Customer Experience Automation™ speeds communication between the carrier and the customer and decreases wait times for real-time service delivery and information requests.

It used to be that consumers would have to wait for an item they ordered with only the original estimate of how many days it would take to receive it. Likewise, they’d have to call their insurer and wait to speak with a representative to ask if their preferred care provider was covered in the insurer's network, or when their next payment was due. In contrast, if you as a consumer today have ever received proactive tracking updates letting you know the number of stops your purchase is from your home, or if you’ve ever texted your insurance company to find the nearest in-network auto repair shop and received the answer immediately, then you’ve experienced the kind of ease-of-experience that CXA and its effort-saving capabilities afford. True CXA for insurance is only a possibility thanks to the capabilities of Ushur’s platform, that includes InvisibleApp™.

Customer Experience Automation™ (CXA) is the application of an AI-powered platform that is purpose-built to automate, scale, and remove the friction from the interactions between a company and its customers; from the beginning of a conversation through its resolution.

Customer Experience Automation™ is essential to modernizing your service delivery and operations because it allows customers to skip the long wait to talk to a person when they would prefer self-service, bypassing unpopular IVR systems, and solve inquiries or complete tasks, through robust Conversational AI. It also affords customers choice and empowers them in their interactions with your brand. Customers who are concerned that their urgent questions will slip through the cracks without immediate attention, will often forgo their preferred digital messaging channel and introduce another point of friction. By offering an artificial intelligence (AI) powered digital channel of their choice as soon as they call in, a company provides service without forcing an employee conversation that the customer does not prefer. This both elevates the customer’s experience and offers the carrier operational efficiency gains.

Ushur’s Invisible App™ is a 1-to-1, secure communication channel for automating customer interactions. It delivers an app-like experience, without the friction of having to download an app, or the cost to the carrier to build, maintain, and support. It also offers customers an easy alternative to logging into a portal and streamlines complex conversations. Invisible App™ enables carriers to provide their customers with an intuitive and consistent brand experience, regardless of the service request involving their policy, bill, or claim. And because Invisible App™ is omnichannel, this consistent experience is offered regardless of where the customer interaction begins, including phone, text, email, web, or popular messaging apps.

In other words, Invisible App™ is what lets an insurer build an automation flow for the reporting of a new claim and meet the customer where they are to begin that conversation, when they begin with a phone call, text, email, website, or chatbot. Invisible App™ affords carriers both flexibility and scalability, eliminating the need to create and maintain channel-specific solutions. Invisible App™ future-proofs your investments in your communication and automation solutions.


CXA Drives Easy, Timely Gathering of Missing Documentation or Data from Customers

If you experienced any part of processing a claim for a car accident, from a minor fender bender to a total wreck, you know that the task of gathering information, data, and documentation from multiple parties is difficult, time-consuming, and fraught with tension. Customers want to know where the process stands, what happens next, and when. And delays that result from missing data are frustrating while also easily remedied by automation.

With Customer Experience Automation™, any missing information can be requested or shared between the many participants in the claim journey. CXA facilitates data gathering and sharing between parties including the customer, service providers, the auto repair shop or a medical facility, agent, and the insurance carrier. Automated outreach via SMS or email eliminates the back and forth of phone calls and voicemail tag, and creates vital carrier capacity for their people to focus on the interactions and complex decisions where human involvement is key. Customers are offered easier ways to respond to information requests, from a quick SMS reply, to photo uploads of an ID or document instead of the time-consuming task of manual data entry after logging into portals.

Customers are offered easier ways to respond to information requests, from a quick SMS reply, to photo uploads of an ID or document instead of the time-consuming task of manual data entry after logging into portals."

Customer Experience Automation™ provides customers their expected modern digital consumer experience.

Technological familiarity, proficiency, and preference is increasing steadily across all generations. The “digital only” world during the pandemic lockdown created a larger, wider audience that expects the ability of omnichannel self-service. Customer expectations for speed and transparency are already high and continue to increase.

What’s more, consumers expect real time information, and Customer Experience Automation™ allows carriers to ensure their sales distribution channels have the most current product information at their fingertips; including service and pricing information. It introduces speed into the quote and RFP intake process by automatically recognizing incomplete submissions and by reaching back to the sender instantly. Finally, CXA also allows customers to review their benefits, confirm their next premium payment, understand the status of a claim, inquire about updating their policy, and more.

Ushur’s Conversational AI and Machine Learning (ML) combine to provide automation solutions that interact with customers easily, quickly, and on their channel of choice. When a customer initiates a transaction or request for service, whether that is filing a claim or a request for information about their policy, Ushur’s Conversational AI is able to automatically detect relevant information from the inquiry and take the appropriate next step.


Benefits of Customer Experience Automation™

Improved Net Promoter Score

NPS is directly related to customer satisfaction, retention, and loyalty. And whether it’s measured within your company or outside of it, NPS reflects customer experience which “correlates with increased revenue growth, retention growth, and referral rates.”

With Customer Experience Automation™ introducing the ease of sending and receiving information, insurance carriers offer a modern solution brand experience that is inviting and at the same time, reassuring to both new and current customers. And that positive customer experience translates to customer confidence and loyalty that is then reflected in higher NPS.

Ushur's Customer Experience Automation™: Where Automation Works for Everyone

Ushur’s Customer Experience Automation™ is a robust platform that focuses on the automation of one high-value customer interaction (what we call a Microengagement™) at a time. Ushur increases a carrier’s agility to realize their digital transformation vision, enabling business employees to create new automated 2-way conversations in hours, or less, without adding to the demands on their IT partners.  Ushur CXA platform is a system of intelligence that complements the wide array of internally facing core and CRM systems carriers use today, along with their contact center solutions. Ushur makes it possible for carriers to offer their customers, agents, and brokers a consistent brand experience regardless of where across the full insurance lifecycle they are interacting, and what technologies support those functions. Purpose-built with best-in-class insurance pre-trained AI with ML, Computer Vision and Intelligent Document Automation (IDA), it doesn’t simply sit at the front end of customer reaching processes. It works within every part of a carrier’s customer experience, optimized for knowledge work and customer engagement automation with a zero-code approach.

Ushur brings frictionless customer experience to the enterprise. Talk to a specialist today.

A few weeks back I was speaking with a colleague who had just bought a new laptop. Unsurprisingly, a lot had changed about laptops since his last purchase. They’re slimmer and faster, the screens brighter, the battery life longer. They hold more data, can connect to your wireless headphones with ease, and built-in disc drives are long gone. Most everything about a laptop purchased today is better than one purchased in 2016. 

But my colleague did have one complaint that felt fairly unexpected — the machine’s built-in spell check function was markedly worse. Not only was it missing mistakes, it was actively recommending spelling, punctuation and tenses that would have rendered the content grammatically incorrect. 

Now, spell check may very well be the first automation software most people over the age of 30 ever interacted with. We were seeing the industry’s first consumer-focused foray into Artificial Intelligence, the simple recognition of repeating patterns based on historical data. It’s incredibly useful and difficult to improve upon, so it has remained part of our personal computers ever since and continues to today. But we’re used to a reality in which ubiquitous and long-lasting technologies get better as time goes on. The camera continues to get better and better with each iPhone generation. Gas mileage continues to improve in cars. Home security systems have become more accessible and user-friendly. Even most basic toaster ovens now have air fryers built-in. Consumers expect the technologies they use to improve over time. So why then would a technology as basic and foundational as spell check take steps backward, particularly at a time when technological innovation is advancing significantly across the board? 

The answer is simple —Data, data, and more data. 

If we can’t develop the intelligence to glean the right insights and act on them effectively, then the data itself is meaningless.

On its surface, this assertion might not make a whole ton of sense. We live in the most data-rich era in human history. Shouldn’t more data mean more insight? Shouldn’t it mean smarter decisions, and better outcomes? Sure, in theory. But not if it’s the wrong data – or worse, bad data. Artificial Intelligence is only as good as the data on which it was trained. 

Which brings us back to spellcheck. The earliest iterations were trained on a wide array of published texts that had passed through a stringent set of editorial standards simply to be published in the first place. Today, nearly every human being in the world has their own personal publishing platform — the Internet. Even publications with strict editorial standards have had to let them relax in the name of the 24-hour news cycle and consumer demand for information delivered quickly. In short, there is infinitely more written text available in the world today than there was 30 years ago and the editorial quality of that text is objectively lower. The result?  Legacy software that comes standard with every personal computer in the world no longer performs like it once did. 

Why is data integrity important?

When people think of data integrity, they’re typically envisioning one of two things. The first is privacy — everything it entails to ensure that customer information is safe and secure. No bad actors getting access or using that data for illegitimate purposes. This has become table stakes across the industry, and anyone who aspires to be a provider of modern software has built enough internal controls both in terms of the product and practices to ensure that data is governed in accordance with industry best practices. 

Data Integrity is critical to more accurate and useful outcomes in AI
Figure 1. Data integrity is critical in producing accurate outcomes.

The second is about the visualization of data, what are most often referred to as insights. This is where both the challenge and the opportunity lie. We need to know how and why a customer is consuming a product or service in order to deliver a continually improving experience. Again, these insights are only as good as the data being analyzed. Accuracy of that insight is paramount to building legitimacy and trust with the user. 

As a runner, I’ve always found it helpful to have a Garmin watch with me that can track my vitals. I’ve invested in six or seven over the years, upgrading to new models as they’ve become available. I find the insights the watch provides about my health to be invaluable; long distance running is hard on the body, and monitoring one’s own health is critical. Recently, I bought the latest and greatest watch. But on my next run, I was both surprised and concerned to find that my heart rate was unusually high. This continued for several more runs, and I had long business travel on the horizon. It made me worry enough that I drove myself to urgent care to get checked out before I got on a plane and flew thousands of miles away from home. The doctor gave me a clean bill of health, and I later came to find that my new watch simply had a software glitch. Inaccurate insights erode trust and confidence in the technology. At least for me, it was enough to make me consider abandoning a brand I’d been loyal to for nearly 20 years. 

What has changed, and why does that matter? 

In the early days of data analysis the incoming data was very structured. It was a simple matter of math and computation that would spit out a chart or insight. As the world has evolved to produce more unstructured data, the statistical modeling around that data can sometimes lead to anomalies in the interpretation of that data. For example, I was driving with my co-founder Henry in his Tesla recently and he expressed concern that a few of the new software updates seemed to have regressed the Full Self-Driving (FSD) experience in his car. It was enough to make him worry about using the feature entirely. A lot of engineering goes into improving the algorithms and models, but if the accuracy of the data on which they’re built gets compromised then instead of being helpful, automation simply creates new pain points. 

The influx of data from numerous devices is only useful with proper analysis
Figure 2. The influx of data from more inputs: computers, phones, tablets, cars, and IoT devices. However, gleaning the right insights from this data is what makes the collection of data valuable.

There is no such thing as too much data in today’s world – if we can harness it in a meaningful way. Otherwise, it can have an adverse impact on our ability to analyze and act on that data in the right way. Every connected device in use spits out tons of data. There are millions of sensors, industrial and IoT devices generating more data than we can possibly care about. If we can’t develop the intelligence to glean the right insights and act on them effectively, then the data itself is meaningless. 

The world of statistical models and machine learning is rife with experimentation and innovation that is consistently improving algorithms and outcomes. I’m optimistic that over time, they will continue to get better. The challenge is ingesting new information. Oftentimes it’s those incoming data sets that are outside the boundaries of what the model is trained for that lead to erroneous behavior. It feels regressive, and customers become upset. Trust erodes. 

At Ushur, we get a firsthand look at the importance of maintaining trust every day. Customers in the finance, healthcare and insurance spaces are incredibly risk averse. For the vast majority of professionals working in these industries, it is better not to take any action than to take an action that might lead to business risk. It’s one of the most commonly referenced barriers against the adoption of new technology within these highly regulated industries. Attention to detail, taking great care and providing accurate results are a must. If a hospital is applying AI to read a CT scan that will determine someone’s health diagnosis, that result simply cannot be anything less than 100 percent accurate. 

But fascinating and life-altering advancements have been made in the way machines are being trained to detect diseases or conditions that the human eye cannot. As long as the training models are incorporated correctly, the outcomes we can drive for real people will be both predictable and transformative. But 80% accuracy won’t do the trick. 95% accuracy won’t even cut it. Nobody wants to be the victim of that 5%. 

The way we leverage data, how we analyze and present it to others, and the actions we take on those insights represent a massive opportunity for the world. But it also poses a huge risk if done incorrectly, sloppily or thoughtlessly. These are powerful tools and applications being put into the hands of the general public. If the data on which our assumptions are based lacks integrity, the results will be ineffectual at best and catastrophic at worst. Which is why the next frontier of innovation in AI revolves around eliminating bias, hallucinations and defining guard rails for LLMs, along with considerations for data privacy and data security. It’s all about data!

If you think that Deep Learning (DL) and Machine Learning (ML) have a lot in common, you’re right. Both terms are related and fall under the category of Artificial Intelligence (AI).

But if you heard someone using Deep Learning as a synonym for Machine Learning, it’s not quite accurate. Deep Learning is a subset of Machine Learning, but they’re not the same thing.

What is Machine Learning?

Machine Learning is the use of historical troves of data, and statistical algorithms trained on that data, to build systems that identify patterns and infer future predictions when given new observations. Machine learning solutions can automatically adapt over time based on the new and natural patterns within the data, rather than having to programmatically account for every possibility with logic and routing. There are 3 types of Machine Learning:

The most common form, supervised learning, refers to algorithms that learn from data labeled by humans. The most common supervised machine learning techniques include linear regression, decision trees, or random forests. 

In order for ML algorithms to make decisions, predict something, or recognize a pattern, data scientists have to train them with properly collected, cleaned, engineered, and labeled data. Based on a data team’s data pipeline, model accessibility, and the model’s subsequent exposure to more data, the effectiveness of the algorithm can continue to improve.

Ultimately, a trained machine learning model is composed of the parameters that inform how much each variable affects the predicted value (for example, when predicting home prices, how much does the number of baths sway a home price).

The higher-quality the data the ML algorithm gets, and the more useful the features engineered within the dataset, the better, and more accurate the output will be.  

How does Machine Learning work?

Machine learning is the parent category for artificial intelligence methodologies that include deep learning, so the principles by which machine learning works gives some understanding as to how all forms of artificial intelligence products work. 

Machine learning relies on examples of past experiences in the form of data to offer predictions with varying levels of confidence in the future. If an experience in the future very closely resembles an example seen by a ML model in the past, it can likely predict an outcome (also referred to as a target) with fair confidence. That space of ML with an object to predict is called supervised learning.

Machine Learning and Deep Learning derives results on cleaned data sets.
Figure 1. "Machine learning relies on examples of past experiences in the form of data to offer predictions with varying levels of confidence in the future."

In some more detail now, supervised machine learning works by finding a function (just a combination of inputs and tuning parameters) to fit a dataset where the experiences (data) have a label. The right parameters for the function are the ones that minimize the function’s error rate against that particular set of data; you can think of the error as the distance between a prediction and the actual value. The process of creating that function with minimized error is also referred to as training.

As an example, if a data scientist is trying to train a model to predict home prices, they may use a linear regression model whose parameters like the number of baths, the square footage of the house, and the proximity to a mass transit center affect the label: the home price. 

By comparison, when machine learning is not deployed to help predict an outcome, it’s usually used for the purposes of identification or segmentation. Those approaches that lack a variable, or target to predict, are a part of the sphere of unsupervised learning. That brand of machine learning works by grouping similar data points together during training and then determining which group a new and previously unseen sample should fall into. 

As an example of unsupervised ML, if a data scientist is trying to train a model for anomaly detection, they may use a clustering model (like K-Means) to see which data points make sense to be clumped together. If there are data points that don’t fit into a cluster, those are the anomalies. 

Machine Learning is a part of AI and the goal of using it is to create automated capabilities that can enhance, scale, and replicate the kinds of thinking people are so capable of on their own; specifically AI is used to provide the flexibility that programmatic/deterministic software solutions can’t deliver.

What is Deep Learning?

Deep Learning is a Machine Learning technique that draws its inspiration from the human brain and how it thinks and extracts information. Deep Learning models are often viewed as synonymous with Artificial Neural Networks (ANN) which resemble brain structure and function. 

As compared to more traditional models, you can think about Neural Networks as models whose parameters are contained in different layers and nodes. Those layers are set in an order. When the first layer receives raw data (input), each layer can learn something new from the previous layer’s output. 

While neural networks are seen as a black box model (it’s not 100% clear what parameters ultimately produce the output predicted) it’s fair to say that certain regions of weights and nodes activate in a principle similar to the parameters of a traditional machine learning model.

Deep learning models are especially good at using data to recognize patterns in forms like images or documents and can identify abstract objects. 

How does Deep Learning work?

Deep learning works to recognize patterns and relationships in data and is often used for processing data formats like documents, photos, videos, or audio. “ Deep” is to indicate that learning happens in several layers. Say the first layer learns to identify an “orange” and its basic features in an image. By going through the next layers, the model may add information about more features that make an orange an orange (texture, color, shape, etc). Each new layer has more information about the features of the orange based on the previous layers’ knowledge so it can better detect the fruit and differentiate it, for example from an orange ball. 

How accurate it is at categorizing each item it sees (the orange), and therefore how quickly it’s learning is determined by comparing the predicted value to the correct value. Comparing the two values and then feeding the results back into the training process gives the neural network the chance to experiment with another set of weights and biases.

For another scenario, say you’d like to build an ML algorithm that can distinguish between an image of a cookie and an image of a dog. To do this you’d need a lot of training data, in this case, images that present either a dog or a cookie and are labeled appropriately. 

By training the ML algorithm on what features have dogs and what features belong to cookies, the algorithm learns to recognize whether it’s a dog or a cookie and uses this information to predict the correct label for the new image. 

Key differences between Machine Learning and Deep Learning 

Machine Learning
Deep Learning
Algorithms are linear
Less complex and abstract
Needs a substantial but smaller amount of data to produce accurate results
Machine Learning algorithms are simpler and thus easier to understand
Requires powerful hardware but less specialized computational power 
Use cases: Medical diagnoses, customer churn rates prediction, basic natural language processing (NLP)
Algorithms are non-linear, stacked in a hierarchy
More complex and abstract
Needs a bigger quantity of well-labeled data to train deep neural networks
Deep Learning algorithms are “black boxes” of complex neural networks
Require great computational power and benefits from specialized hardware
Use cases: Image and speech recognition, translation of text from one language to others

After all, it is all Artificial Intelligence

Although the concepts have their own nuances that delineate where and when to apply each, don’t forget that Deep Learning is actually part of Machine Learning. They each represent specific approaches to  automating tasks with specialties based on type of data, but both are intended to make life easier.

Despite popular usage of platforms like ChatGPT, the utmost benefit of AI is in making software applications more intelligent and finding patterns where humans wouldn’t be able to in huge troves of data. 

So, what’s better: Deep Learning or Machine Learning?

The truth is, your use case should dictate whether you’ll use Deep Learning or simpler Machine Learning models. More traditional machine learning models are comparatively cheap and easy to train and deploy, while deep learning models help automate use cases for data types like image.

Say you have well-structured, clean numerical data and you’d like to predict customer churn in your insurance company or classify your customers and their lifetime value. In this case, building and training a simpler Machine Learning model like a logistic regression is a better choice.

But if you’re dealing with unstructured data types and need to do image recognition, Deep Learning will do the job here. Say you’re giving a DL algorithm an image of a dog but you’re not telling the model what the picture presents. Here, the neural network will recognize, step-by-step, features of a dog, until it classifies the image as a dog image. 

Ushur’s AI: the Best of Both Worlds

Ushur’s Customer Experience Automation (CXA) Platform is designed to support the operations of complex businesses in the insurance, healthcare, and financial industries. Ushur and  Ushur AI Labs have already thought through the decisions an enterprise business needs to consider when evaluating AI projects in highly regulated industries. Using proprietary language and document services, Ushur’s AI solutions help businesses understand user intent, evaluate document content, and drive seamless customer experiences so that customer support functions and business owners can focus on the highest value projects in their queues.

If you want more information on the machine learning and deep learning capabilities in the ushur platform, and to see how Ushur blends AI with customer experience technologies visit

Columnar data files are the dominant form used for exchanging information across industries like FMCG, Shipping, Finance, Insurance and others. The row-column format creates an  understandable and recognized mechanism for sharing information  which also happens to be easy-to-digest for digital processing. A lot of financial decisions and estimations are made off of information stored in rows and columns and colloquially the files are referred to as CSV’s (comma separated values). However, Microsoft Excel has quickly become the industry standard for visually processing CSV’s and it provides added user functionality like embedded tables, and the concept of “Sheets” where information can be logically partitioned. 

There are many business conversations where data is exchanged via Excel/CSV files, and a classic example in the insurance industry is the dialogue between insurance brokers and insurance carriers. Here, CSV files are used to exchange information during the RFP (Request for Proposal) phase. The US has many insurance brokers. Brokers vary from individuals doing part-time work to small companies that employ a few people all the way to large brokers that employ hundreds of brokers.

When brokers meet prospective companies and need to quote them for their insurance needs, each broker communicates in their own styles and lexicon to represent the information they’ve gathered. For example, a simple column to represent “Date of Birth” can appear as “DOB”, “Birth date”, “Employee dob” and so on. Now, multiply that variability with about 100 columns of data for each customer and you understand why automating the process is so complex. 

Adding Efficiency in Insurance

Automation and Efficiency are the primary goals of many carriers in the insurance industry in 2023. As more and more carriers automate portions of their backend and quote processes, discontinuity in data format and structure are exceedingly difficult to manage and thus need a lot of manual back-and-forth. It is estimated that in many cases, it may take anywhere from 1 to 5 days to clean and structure all information in formats necessary to correctly persist information in their System of Records (SOR’s).

This is where the Ushur Data Transformation Engine comes in and makes an immediate impact – adding automation to an intractable problem which was dominated by manual operations, and providing a high degree of efficiency by dramatically reducing the time taken to process the entire workflow.

Email word cloud
Figure 1: Categorization occurs with the help of weighted words

Key features of the Ushur solution

Key implications of the Ushur solution

RFP Automation in Action

In the RFP process,  an insurance carrier typically receives hundreds of RFP requests per day. These RFP requests usually arrive in the form of emails being sent by the brokers to the sales executives. Among the numerous attachments, there is an excel file containing census information. This information generally includes: names of employees, birth dates, classes of employment, products required, premiums, specific clauses such as Cobra, eligibility for each member and so on. 

This extraordinarily manual process invites automation to save on time and expense, and prevent overworked employees from introducing errors in data. Automating it, however, requires intimate knowledge of the process, a data standardization and cleaning routine, and user-friendly tools.

Document normalization
Figure 2: Document normalization from varying inputs

As Ushur went about solving this problem, we overcame many engineering challenges, some of which are listed below: 

The Ushur Approach

Ushur begins with table extraction. The data users want usually resides in tables within these excel files. Surrounding the tables are huge chunks of irrelevant data such as legends, demography info, huge headers, titles and other noise. The extraneous noise affects the performance of downstream tasks such as classification and transformation. The Ushur novel table extraction algorithm helps us to effectively customize table extraction for multiple use-cases. We use a combination of NLP and Vision Techniques to solve this problem.

The next step is column classification. Ushur recognises different column headers and normalizes them into CRM accepted headers. Since these excel files are sent by multiple brokers from around the world, the variation in representation of the data is immense. Ushur’s domain specific models help to cater to use-cases per domain.

The final steps are transformation and validation. A lot of transformations and validations are required to be performed on these normalized input tables. This ensures data consistency and easy feed into the customer’s system of record. Since, this is a highly customizable problem depending on various use-cases, it’s imperative that we enable citizen developers to perform these operations at their convenience. We enable this by the mechanism of “rules”. We have created our own rule language that end users can use to write their rules. 

Once we have executed the above steps, we now have a clean, normalized and consistent excel file. We send back this asset as an excel file or as a JSON to be fed into the customer’s CRM. 


Ushur’s columnar data transformation engine is a part of the patented Ushur Document Intelligence Services Architecture (DISA) and deployed within Ushur Intelligent Document Automation™ (IDA). DISA applications have led to significant improvements in business metrics for our customers – in one case, one of Ushur’s clients was able to reduce manual labor from 30-36 hours to about 3 minutes, and see many examples where there was no human intervention of any kind, freeing up agents to focus on high-touch, more meaningful interactions, rather than back office tasks. Best of all, the ability to create new rules very quickly via the Ushur no-code flowbuilder enables Ushur to provide ROI to customers in days.

There’s been a significant amount of buzz and excitement around generative AI and ChatGPT lately, and for good reason. Not only is it a potentially transformative technology for just about every industry under the sun, it’s one that is rapidly opening up to a growing number of people.

One of the better qualities we humans have is a capacity for true creative genius. When transformative technologies are opened up, that ability to create is unlocked for a larger portion of the population. Think about GPS capabilities, for example, which started as a niche technology designed for and by the military. It was ultimately opened to everyone, and soon we had GPS capabilities built into every new car. Applications like Google Maps and Waze were created, eventually paving the way for companies like Uber and Lyft to disrupt the entire taxi industry. Today, I can go for a 15-mile run through the woods and know exactly where I am, where the trail markers are, every detail about every turn and elevation gain, all by simply wearing a touch-screen watch on my wrist. Touch screen technology has existed for a long time. GPS has existed for a long time, too. It’s the assembling of these pieces to create better human experiences that is truly profound, which is why technologies like ChatGPT are so compelling.  

Natural language generation technology has been around for nearly a decade. Many companies have created Machine Learning models known as Large Language Models or LLMs. Essentially, this boils down to using massive amounts of data to train a model that can generate answers to questions. In its simplest form, think auto completion when writing an email or sending a text. The underlying technological principles are not new, but that level of accessibility is in its infancy. Today, we are just beginning to scratch the surface when it comes to the potential applications.

Think about the explosion of innovation that has followed our shift away from on-prem data centers to cloud infrastructure. Before Amazon, GCP or Azure the process of building, deploying and running software was an onerous one for founders. Startups needed massive early stage financing purely to run servers and storage racks. Now, the cost of starting a company is minuscule in comparison, largely thanks to the availability of cloud infrastructure. If you’re building application software today you don’t have to worry about all of the hardware investments and overhead you did a decade or two ago. Now that the cloud has become commoditized and is accessible to everyone, it has made it easier for more startups to get their innovations off the ground — and quickly. In turn, more innovations follow at an increasingly rapid pace.

ChatGPT and the rise of AI Innovation
Figure 1: ChatGPT on mobile. Does ChatGPT mean the end of Google, Facebook, or their revenue models?

A lot of the recent chatter around ChatGPT has been what generative AI will mean for long-standing incumbents. There have been significant layoffs across big tech, enough that most of us in the industry know someone impacted. Does ChatGPT mean the end of Google, Facebook, or their revenue models? While that may be the newsier angle, I don’t think it’s the right place to focus at all. These companies will regroup, and remain powerful. They have leveraged their incumbency well, and many have invested in LLMs and generative AI themselves. They have enormous assets that they can invest in creating new technologies based on generative AI, or fund smaller companies already doing the work. I don’t believe they’ll be disrupted nearly as much as people might think, they’re well governed companies with strong strategic vision. Going back to the example of Uber and Lyft, many pundits and experts predicted they’d mean the death of the auto industry. Those companies – and their considerable resources – were able to adjust and adapt, and today demand for personal vehicles often outstrips supply.

But when we talk about disruptive technologies, it’s an absolute certainty that eventually a giant or two will be knocked from their perch. Where a status quo that no longer serves the customer exists, the potential for disruption is ripe. Google has enough data banked to continue innovating for multiple generations. They are famous for moving on from projects and products that don’t work. They wrote the book on making adjustments. But if we look at other big companies that have crumbled quickly, they share some clear commonalities. Monopolistic practices and processes had set into the way they operated, making it impossible for them to leapfrog into the next generation. Blockbuster, Circuit City — these were companies in the good-to-great category that weren’t quick enough to adapt and adopt when a newer, better way of serving the customer came along. Today, this is the same challenge healthcare and insurance companies face. In these regulated industries, incumbency is no longer an advantage. How do they adapt and survive in the face of increasingly sophisticated consumer expectations? 

If there’s one clear obstacle between regulated industries and the wider adoption of technologies like ChatGPT, it’s the inherent bias within them. Many people think of AI like it’s something with a personality that can imitate or replace a human. But at the end of the day, it’s just a machine. Even its ability to generate language is a very statistical, mathematical, science-driven outcome. Its ability to learn is based entirely on the data it is fed, and so it might not be representative of all populations but rather the populations contributing most actively to the data set. Those anomalies and biases, the false negatives and false positives, have to be weeded out. For some applications of generative AI, like ChatGPT’s simple search function, 70-80% accuracy is good enough for the user. For others, 98% might fall short. 

For some applications of generative AI, like ChatGPT’s simple search function, 70-80% accuracy is good enough for the user. For others, 98% might fall short.

For example, let’s say you’ve applied for a life insurance policy. On the other end, a team of underwriters looks at your application information — age, habits, personal data, income, lab results, blood tests, a physician’s summary — and manually identifies your risk against a predetermined set of markers. That level of risk then dictates how high a premium you’ll have to pay. Some applications are rejected outright. Today, it costs most life insurance companies upwards of $500 just to reject a life insurance application. A technology like generative AI can come to the same conclusion regarding those risk factors based on the biomarkers in your application at a fraction of the cost. Of course, an approved application carries a higher financial risk for the provider than a rejected application. So while a company may be able to use technology that is only 70% accurate to reject applications, that rate won’t cut it for approving them. Regardless, even if just half the manual work is eliminated customers will be served more quickly and effectively at a lower cost to the provider. The immediate opportunity for industries like healthcare and insurance is to identify which applications they can accept under 100%, and begin to leverage generative AI for those tasks. 

Applied to the world of healthcare and insurance, generative AI has enormous potential capabilities. For customers of these industries, costs continue to go up and access to care is dwindling. In the United States especially, going to the doctor is an experience full of friction. Recently, I started to feel a bit unwell the day before a long flight. It wasn’t an emergency situation, but a time sensitive one. Though I’ve been seeing my primary care physician for over 20 years, they couldn’t see me on short notice and sent me off to urgent care. All I really needed was a quick diagnosis to give me peace of mind before I sat on a plane for 16 hours. A technology like ChatGPT has the potential to provide a diagnosis like that, without requiring a trip to the doctor. Or worse, urgent care. 

Access to healthcare is critical. I have better than average insurance, but still couldn’t get the help I needed when I needed it. Think of how it must be for one of the millions of people who don’t have the same quality of insurance or care. It shouldn’t be like that, nor does it need to be. Technologies like ChatGPT have the potential to democratize healthcare, to make it more available and accessible in a way that betters our collective quality of life. The big question for today’s incumbents is whether they’ll be the ones providing that improved experience, or whether they’ll go the way of Blockbuster. The opportunity to adapt and improve is at their fingertips, but action is imperative.

For those who are not familiar, Interactive Voice Response (IVR) is an automated system that uses technology-enabled triggers to triage customers within a phone support pipeline. The concept of IVR itself dates back to 1962, and throughout the last two decades, IVR technology has become a staple in complex professional industries like healthcare, telecoms, finance, insurance, and education. 

IVR is so penetrative, in fact, that most customers have strong feelings about the technology — and most of these feelings aren’t good. In a 2019 study, 61% of surveyed customers reported a negative association with IVR, leading many companies to drop IVR systems from their customer experience (CX) strategy completely. 

Yet global IVR adoption is still growing and the market is set to reach a $6.7 billion valuation by 2026, representing a 7.9% year-over-year increase. So why are some businesses continuing to prioritize IVR while others are disregarding it as a valid component of customer support? 

Negative associations with general IVR solutions are reasonable. Most IVR solutions push customers into a predefined set of options that offer poor experiences and usually don’t solve their needs. However, many companies continuing to implement IVR solutions do so because their support departments are inundated with requests and often can’t offer service to customers in a reasonable time–or can’t get to all customers that need assistance. Paired with potential cost savings, companies are forced to implement far from imperfect IVR solutions as a best attempt to help customers.

The reality is that today’s IVR is not the same IVR that’s infamous for creating unhappy customers – and while the companies who are keeping IVR know this, they are also refining their strategy for using it . Those that know how to leverage AI-powered IVR are not turning their backs on the technology, and are instead welcoming it into their CX strategy with a twist. 

How Does Interactive Voice Response Work?

After hearing a prerecorded message, which includes a courteous greeting personalized for the company, IVR customers are given a number of options to choose from that will direct them to the right customer service option for them. 

In some cases, the IVR system can ask additional questions to further narrow down options and ensure proper redirection through multi-level menu functionality. IVRs are often supported by automatic call distribution (ACD) solutions. They place callers in a queue based on the information collected from IVRs, where higher-priority calls are answered first. As pointed out by IDC, they also traditionally rely automatic speech recognition (ASR) to hear what is being said to them, but usually lack natural language understanding (NLU) capabilities to “truly understand what is being said.”

Figure 1: IVR call deflection from voice to the Ushur Invisible App™.

IVR was created with the goal  of understanding the intention customers are calling with and responding accordingly. Not only would it make the conversation more personable, but it would also shorten wait times and improve call resolution rates–both having a positive impact on the overall customer experience. 

In practice, these automated phone systems can be frustrating due to their limited ability to provide tangible support. Customers may resort to using foul language or other creative methods to bypass the system altogether. 

What is AI-enabled IVR?

Humans like choice – some prefer digital messaging channels, while others lean more towards traditional calls. However, the more urgent the situation, the more likely they are to give your business a call, hoping that it’ll be the fastest way to solve their issue. You can imagine their frustration when their urgent phone call is met with an automated voice response and a 30-minute hold time (or longer). 

IVR call deflection is a  new approach to handling calls— urgent or otherwise. IVR call deflection empowers callers by giving them the opportunity to switch to a different communication channel like an AI-powered chat if they’ve heard their wait time is too long for their purposes. Their query will be resolved quickly through an automated solution, freeing up agents to handle more pressing issues.

Here’s how it works. When customers are met with long wait times, an IVR call deflection system will step in and give them the option to continue the conversation over a digital channel instead. It would sound something like this:

“Call volumes are unusually high. If you’d like to continue this conversation over text, press four or say ‘let’s talk’.”

IVR call deflection solutions benefit from recent advances in artificial intelligence (AI).  Conversational AI models use Natural Language Processing (NLP) to understand customer issues and concerns, and guide them through the final steps for self-service and resolution. Particularly as large language models (LLMs) become more available for use, conversational AI deployments will only sound more and more like an empathetic and capable support agent to customers. 

IVR use cases and benefits 

Better customer service and experience

The more complex the industry, the harder it is to maintain a positive customer experience. Take the Insurance industry, for example. When a severe weather event strikes an area, insurers that cover the affected area may experience extreme surges in call volumes. Offering an IVR solution that still allows customers to submit a claim and schedule a phone call with an agent can expedite claim closure and ensure customers are satisfied more quickly. 

IVR for digital transformation
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For customers whose preferred mode of communication is not by phone, chances are they’ll reach for their phones and dial in, convinced that they’ll only get their request processed quickly if they choose a voice-based medium. These customers are surprised and delighted to discover a digital channel integrated with an IVR that is capable of handling their request, and may opt to deflect their call. 

Happier support agents

IVR deflection solutions for call centers mean more than just a faster resolution time. They also result in a more satisfactory employee experience. When many queries are handled automatically, your agents aren’t overwhelmed by the number of calls they have to answer. More importantly though, the conversations they’re assigned to are always within their area of expertise and of the severity that merits their involvement. This increases the chances of closing tickets, leads to higher productivity, and higher levels of employee satisfaction. Support teams want to help customers, but they prefer working on important cases that can make an impact. 

24/7 Support

Automated support is available 24/7 – customers can call anytime, day or night, and still have an option for self-service with call deflection. While customer service agents can only focus on one call at a time, call deflection integrated with an IVR can handle multiple calls simultaneously. IVR technology allows companies to provide a frictionless experience by resolving consumer queries in real time,irrespective of whether it involves providing information on travel insurance or updating financial forms. 

Better data management

Another benefit of intelligent IVR technology is the integration possibilities within each company’s data and Business Intelligence (BI) systems. For example, IVR systems allow enterprises to quantify exactly how many times each client has reached out with a request, or which types of queries are brought up most often. As a consequence of having that data, creating complete, up-to-date customer profiles empowers you to make better business decisions – after all, you’ll be basing them around reliable data.

Faster resolutions

One of the greatest advantages of IVR technology is its ability to effectively route calls. For example, when a customer contacts an insurance company, they’re connected to an agent who is qualified to resolve their issue. Gone are the days when a caller was transferred repetitively from one agent to another in the hopes that their request will finally be tackled. Effective call routing reduces handle time and increases first-call resolution. 

IVR Deflection, the best of both worlds

Support has come a long way in the last 60 years, and the businesses that take advantage of AI-enabled solutions now have solutions to help their customers and solve their issues more quickly. When integrated into an IVR solution, Ushur Invisible App™️ is the only product of its kind designed to intelligently automate workflows, resulting in a better customer experience and higher retention rates. 

With Ushur Invisible App™, organizations can implement a call deflection solution over chat or SMS that guarantees safety, security, and compliance while also helping triage a slower and expensive voice-only customer queue. Plus, Invisible App doesn’t require a name and password like a usual app so customers don’t suffer from login fatigue.
Learn more about how Ushur can help you with call deflection.

Ushur, the leading no-code digital Customer Experience Automation (CXA) platform, and Virtusa, a global leader in guiding clients through the challenges of digital disruption, are excited to launch a new partnership.

Virtusa’s appetite for disruption comes from the teams and technology it has centralized in its business. The unique expertise that Virtusa has acquired helps their teams guide insurance carriers through turbulent transformations and leverage digital capabilities like those from Ushur’s AI-powered, no-code platform. Across Property and Casualty, Life and Annuity, and Group insurance, Virtusa prepares clients for the next revolution of technology in their core businesses — no matter what it may be.

The complex and regulated ecosystem of carrier technology solutions make digital transformation projects burdensome without technology and service providers like those brought to bear with this new partnership. The Virtusa and Ushur partnership launches digital transformation projects for insurance carriers into a new class of accelerated delivery because they are already enterprise-grade to the core.

Digital Transformation as a strategy for customer experience

Digital transformation replaces the unscalable and manual versions of insurance carrier processes with more-repeatable, technologically-driven solutions. Leaders in these industries are constantly looking for ways to reduce journey friction, accelerate development cycles, and cut operational expenses with digital innovation because time kills customer experiences.

Virtusa clients going through digital transformation efforts can now bank extra time by using the flexibility of machine learning (ML) thanks to the Ushur platform. When speaking with claimants, partners, customers, and other stakeholders, Virtusa clients need Ushur’s state-of-the-art engagement capabilities. With just a couple of clicks, Ushur and Virtusa clients can rely on conversational AI and machine learning so they no longer need to programmatically account for every possible path and route a customer could go.

CX often suffers from technology underinvestment, and both customers and employees already have elevated expectations of what technology-first solutions should look like – it should be easy and it should be fast. When it comes to making digital transformation real, customer experience excellence is a bigger hurdle than many others.

Enterprises already understand that digitization projects are essential if they currently rely on people-first processes for customer service. Scale of customer interactions is increasing, not decreasing. Even in the best of times, it can be hard to find enough of the right people to help triage at that scale. Additionally, people leave jobs, or switch companies, and competing for that customer service expert is a difficult path for achieving growth.

Building a practice around Customer Experience Automation™️ is a new and emerging strategy for adding automation to customer service functions, and can truly help enterprises accomplish their digital transformation goals.

What is Customer Experience Automation?

Customer Experience Automation™ (CXA) is the interdisciplinary intersection of artificial intelligence, process automation, and conversational interfaces blended to optimize the customer experience and engagement.

Automation removes procedural barriers that prevent expedient resolution. Artificial intelligence (AI) uses historical data to understand and predict future behaviors. Customer experience is the product of two-way conversations where action matches the intent.

How The Partnership Benefits Carriers

Insurance carriers, and financial services organizations are already using digital-first strategies for interactions with members, customers, patients, agents, brokers, and providers, and they rely on partnerships like the one between Ushur and Virtusa.

Virtusa establishes a practice at each client for tackling customer experience projects and brings in the best-in-class technology needed to make delivering those a reality. Virtusa will use the Ushur CXA platform to engage directly with business users to better understand customer experiences, and then build out the digital representation of those customer experiences. The partnership brings the experts in digital transformation the kind of technological capabilities they need to make those conversations seamless

We want to say that Virtusa gets experts on digital transformation and Ushur gives them the tools to build customer experiences. The no-code capabilities mean they can easily get agreement from Virtusa clients.

Insurance Automation

Everyone involved in the insurance journey, from market development to service and administration, expects digital-first self-service to be available. Carriers with self-service-first principles will naturally rely on intelligent automation in the insurance journeys to engage with customers, claimants, brokers, adjusters, and providers.

Brands can improve digital customer engagement by leveraging the API-driven customer experience automation platform via integrations to make it the ideal technological bridge between consumers and the back office claims management, policy administration, underwriting, and billing systems.


The new partnership between Virtusa and Ushur highlights the capabilities of Ushur’s Customer Experience AutomationTM platform and puts it in the hands of the insurance carriers that Virtusa is guiding through digital transformation projects. Ushur applications can deploy in days, are easy to task for reuse, and are simple to deploy.

Virtusa expertise and technology, partnered with the Ushur customer experience automation platform, will help the industry's leading carriers tackle the challenges of new customer experience programs in the coming years. Together, this partnership will design and deploy the unique value-creating customer experiences that carrier customers have been waiting for.

Insurance companies receive numerous emails, which contain requests for proposal (RFP) quotes from brokers. These emails have valuable information that can help insurers to make informed decisions about coverage and pricing. However, extracting data with the desired accuracy from emails can be a significant challenge even with the help of AI. Here are some of the challenges while using AI to extract data from RFP quotes.

Unstructured data word cloud image

Using AI to automate the RFP quote intake process can help insurance companies reduce the time to respond to requests, giving them a competitive edge over those that process quotes manually. There are also potential benefits such as increased efficiency, monetary gains, and so on, without losing sight of the numerous challenges of using AI to extract unstructured data from emails in the insurance industry.

Ushur has overcome these obstacles and developed a data extraction framework for a Fortune 500 company. Using our intelligent data extraction capabilities and the Ushur Invisible App, our customer is now able to respond to incoming quote requests in 10 minutes rather than 5 to 6 days. Our AI pipeline can accurately identify and extract around 170+ entities from unstructured emails and provide highly structured information within a few minutes.

After conducting an extensive evaluation of various techniques, Ushur concluded that an ensemble of multiple methods was necessary to effectively extract the various entities from the email.

Ushur established a hierarchical relationship between the entities to accurately associate them with specific insurance concepts and devised mechanisms to narrow down the region of interest for each entity. An efficient modeling approach, which could capture these hierarchical relationships and select appropriate extractors for each entity was required to implement this. After careful consideration, Ushur determined that an ontology-guided method was the optimal choice.

This approach enables a structured and comprehensive framework to create the data extraction pipeline that ensures accurate and consistent results across large volumes of incoming emails from our customers. The approach is validated by accuracy rates exceeding 90% on a corpus of over 10k emails across a period of about 12 months. Ushur accomplished this by combining a novel ontology-guided extraction approach with an ensemble of NLP techniques.

Our approach

AI Labs Steps involved image
An ontology is a formal description of a set of concepts in a domain that depicts their properties and the relationships between them.

The steps involved are:

  1. Define Ontology: The ontology provides a framework for understanding email structure and content and can guide the information extraction process. For example, an ontology for the insurance industry might include concepts such as “broker,” “insurance policy quote,” and “group,” and relationships such as “group has an insurance policy” with properties such as “quote’s due date”, “broker’s name”, etc.
  2. Pre-processed Email: It is easy to work with pre-processed emails. This involves using NLP techniques such as tokenization, sentence segmentation, and part-of-speech tagging to identify and label relevant text components.
  3. Extract Data: The Ushur data extraction approach allows each node in the ontology to be associated with an extractor. These extractors could be simple regular expressions, NER-based (Named entity recognition), complex pointer networks, or an ensemble of extractors chained together.
  4. Store Data: Extracted data needs validation to ensure that it is accurate and complete. The extracted data is stored or presented in a structured format.

To conclude, an ontology-based approach provides a consistent framework for data extraction from unstructured content. When combined with the power of AI, this can assist businesses in automating and increasing the efficiency of their RFP intake process.

Ushur platform, an Introduction

People who first hear about the Ushur platform often ask what all can one do with the full suite of its capabilities. For better or for worse, people often have to deal with the answer that one can do pretty much anything. It’s a platform designed to let non-technical users build customer experiences that can be run once, twice, or a million times, and that leverage the pre-built capabilities that make those experiences cutting-edge and representative of a modern consumers experience.

The platform centralizes around a no-code flowbuilder by which users drag and drop each step of the experience. Users can use any number of pre-built modules, but altogether the small blocks of capabilities turn into a thorough experience representative of the quality enterprise customers demand.

A platform alone offers only so much value, which is why an API-centric architecture provides so much value to Ushur customers. The Ushur platform integrates with services like Salesforce, Zendesk, Twilio, or Amazon Connect. Those integrations extend the pre-built capabilities within the Ushur platform and make the experiences even more feature-rich and personalized. Ushur customers can serve up information from back-end systems, as well as persist data back to those same systems from customers. Altogether, that makes data exchange and gathering a hassle-free building experience.

Ushur platform integrations

Products on top of the Ushur platform represent pre-built technological capabilities to emulate well-known experiences like mobile applications and customer portals. Invisible App™, Invisible Portal™, Conversational Apps, and SmartMail package essential components to make solution-design with Ushur partners and your internal stakeholders easy, repeatable, and quicker.

Customer Experience Automation™

Ushur leads the category of customer experience automation (CXA) and has an opinionated point of view on the components and trajectory of the space. CXA, from our point of view, is the automation practice by which customers converse with brands in their natural language and all appropriate resulting process implications execute behind the scenes. Consider the scenario of reaching out to a customer and asking them how they are doing, and how easily they have access to food and shelter (a social determinants of health use case). With Ushur, customers (patients) can communicate in a natural dialogue, and Ushur can connect them to the local resources they need to achiever more stable housing and food. Ushur can also persist that data into the customer relationship management (CRM) system, so brands can track their past conversations and results.

Customer experience automation bridges the gaps opened by point or spot solutions. The bridges are created to cover siloes in businesses who all own different portions of their technology stacks. CXA differs from other spaces like robotic process automation in that it is designed to be end-to-end, natively.

Who do we speak to?

Ushur is designed with compliance and security in mind to service our customers handling sensitive data and transactions in the most intimate moments of their relationships with their customers, partners, and internal stakeholders. Our clients span insurance, healthcare, and financial services and we help them optimize their customer experience strategies in a time frame that make sense for them — projects that usually have to wait quarters or years can be started in weeks or months.

Participate in the Partner Program

Ushur partners with some of the most established technology providers in the enterprise space that have already realized that they need a customer-facing (consumer, partner, internal stakeholder) interface and automation solution. Across the insurance, financial services, and healthcare sectors, Ushur helps technology providers who are serving their industries expand their capabilities to include conversational AI, prepackaged app-like interfaces, and email triaging capabilities.

Ushur’s partner program is designed to make it easy for technology providers who need to move quickly with building customer experiences in an affordable and maintainable design. Help your customers create a CX strategy that reflects the profile and brand experience they want their customers to enjoy when interacting with them on a day-to-day basis.