Computer Vision for Healthcare: A New Era of Intelligent Diagnostics

Healthcare organizations produce thousands of medical images every day. What if every routine medical image could reveal life-changing insights? This computer vision for healthcare POC demonstrates how AI can transform routine fundus images into intelligent clinical decision support. This solution combines advanced image analysis with explainable AI. It integrates with existing healthcare workflows and enables faster and more reliable diagnostic screening. 

AI-Powered Retinal Screening for Faster, Smarter Care

Built as an AI-powered retinal screening solution, the proof of concept showcases the potential of AI in healthcare to support early detection of diabetic retinopathy, glaucoma, and cataract. Using fundus image analysis AI and automated disease assessment, it helps healthcare providers improve screening efficiency. It reduces manual effort and scales preventive care without increasing specialist workload. 

Discover the Full AI Diagnostic Screening Solution

This overview only scratches the surface. The full case study reveals the solution from AI diagnostic screening to real-world performance to business impact, and more. Learn how this framework can be adapted to broader computer vision applications across healthcare and other industries. 

What if routine medical images could deliver faster, more consistent, and explainable insights? Explore this computer vision for healthcare proof of concept to see how AI in healthcare is enabling intelligent disease screening through advanced fundus image analysis AI.

CV casestudy banner.png

A Healthcare Case Study

Building Enterprise-Ready Computer Vision Solutions

Many organizations still use systems built with Visual Basic 6 (VB6). These applications still support business-critical processes even decades after they were first deployed. 

Legacy application modernization is often delayed because the system still works. But the longer the delay, the riskier the project becomes. This leads to limited scalability, high maintenance costs, and slow digital transformation initiatives. 

To explore a more reliable modernization path, our team conducted a proof of concept (POC) to migrate a mature VB6 application to a modern, cloud-ready architecture. This legacy system modernization case study highlights the approach, outcomes, and insights gained during the evaluation.

The POC focused on a production-grade VB6 application supported by SQL Server and reporting components. Using our modernization platform, our team analysed the application's structure, identified modernization opportunities, and assessed the feasibility of a VB6 to cloud migration.

•	Validating whether business rules, workflows, and application logic could be accurately preserved.

•	Creating a verified understanding of the existing application’s functionality

•	Assessing cloud readiness and establishing a trusted foundation for future modernization efforts

Our team followed a structured legacy modernization workflow designed to extract and validate application intelligence directly from the source system. We analysed the VB6 codebase, database components, and reporting assets to develop a comprehensive understanding of the application's behaviour.  This enabled the team to assess modernization readiness, verify requirements, and create a roadmap for cloud migration without disrupting existing operations. The resulting framework provided a transparent and measurable path toward modernization.

Key findings from the VB6 modernization: 

•	Clear idea of potential legacy modernization ROI

•	Readiness for cloud migration and digital transformation initiatives with minimal risk

The full case study outlines the methodology, validation process, metrics, and practical lessons to guide your own legacy application modernization strategies.

Before committing to redevelopment, we asked a critical question: Can a legacy modernization strategy be proven? Explore the findings from our VB6 modernization PoC.

Legacy Modernization.png

Unlocking Business Value Through  Legacy Application Modernization:  VB6 to Cloud Conversion Case Study 

Legacy Application Modernization

In most product design workflows, everything begins with wireframes. Designers usually open Figma, sketch layouts, define user flows, and slowly refine the structure of the product before a single line of code is written. Weeks can go into polishing interactions, visual hierarchy, and micro details before development even starts. That process exists for a reason. It helps teams think through the user journey and avoid costly mistakes later in development.

But for our latest internal project, Zenith, an enterprise-grade admin console, we wanted to try something different. Zenith is an internal admin console designed to help administrators manage e-commerce operations such as orders, payments, and workflows from a single interface. Instead of following the usual design-to-development pipeline, we explored whether we could move directly from product vision to a working interface using AI-assisted software development and structured prompting. This was driven by a member of our UX team who led the entire process end to end. With a strong understanding of both product and technical fundamentals, the work moved forward quickly without relying on traditional handoffs.

The goal was not just speed. We wanted to understand what happens when design thinking, technical understanding, and modern AI tools come together in a tightly integrated workflow. This approach reflects a shift toward more intelligent software development workflows powered by generative AI. 

Could we skip parts of the traditional process and still end up with a solid, usable product? Zenith became our way of finding out.

How AI orchestration helped structure the workflow

One of the biggest challenges when working with AI in development is maintaining consistency. AI tools are powerful, but they can also be unpredictable when instructions are not structured clearly. If you simply prompt an AI builder repeatedly, small changes in one area can accidentally break something else. A button might get fixed on one screen while the navigation quietly breaks somewhere else.

To avoid this problem, we adopted a two-tier prompting system. Instead of treating AI as a single assistant doing everything, we separated the workflow into two distinct roles. One focused on maintaining the architecture of the product, while the other focused on executing the instructions needed to build the application.

Role of the architect in AI powered software development

Gemini acted as the architectural brain of the project.

Rather than using it only as a casual assistant, we used it as the place where the core logic and structure of the application lived. Gemini held the overall source of truth for the system. It understood the grid layout, the design language, the product goals, and the expected user behavior across the application.

By centralizing these elements inside Gemini, we created a stable reference point for the entire project. Every instruction generated later in the process could refer back to this foundation. This helped ensure that the product evolved in a consistent direction rather than drifting into disconnected pieces.

Role of the builder in AI assisted software development

AI Studio handled the execution part of the workflow. Instead of writing prompts for code ourselves, we asked Gemini to generate the prompts that AI Studio would run. This added a layer of structure to the process. The instructions given to the builder were not random or written on the spot. They were created with the system architecture and design rules already in mind.

In simple terms, Gemini acted like the architect while AI Studio did the building. Gemini understood the overall structure of the application, and AI Studio focused on implementing the specific parts. This separation helped avoid many common problems in AI-driven development. It reduced the chances of changes breaking other parts of the app and made the process more stable overall.

How generative AI helped build the application faster

Even with the structured prompting system in place, we wanted to push the experiment further. In our usual workflow, we create written product requirements along with visual wireframes. These wireframes act as a blueprint that developers follow while building the interface. For Zenith, we intentionally skipped that step.

Instead of designing screens in Figma, we focused on clearly describing the product. We discussed how administrators would use the tool, what tasks they needed to complete, and what information they should see on each screen. These discussions became the starting point for our conversations with Gemini.

Step by step, we defined the structure of the application through this dialogue. As the discussions continued, Gemini turned those ideas into structured prompts that AI Studio could use to build the app. The process felt different from traditional design work. Instead of designing screens first and thinking about functionality later, we started with the purpose of the product and how users would interact with it. Over time, the interface took shape from those conversations. In the end, the process felt less like designing screens and more like building a system.

Human oversight in AI software engineering workflow

Even with a strong prompting system and a clear product vision, AI-generated workflows are not always perfect. AI tends to rely on default patterns. Those patterns may work technically, but they do not always reflect the most efficient or intuitive experience for real users. This is where human oversight played an important role.

Throughout the process, we constantly evaluated the flows that were being generated. Whenever something felt unnecessarily complex or unintuitive, we stepped in and adjusted the logic guiding the system. 

One example was the payment workflow. In its early form, the process involved too many steps. While each step made sense individually, the overall experience felt cumbersome. For an administrator who might need to complete several transactions in a day, those extra clicks would quickly become frustrating.

We reviewed the logic and worked with Gemini to restructure the process. The goal was to remove unnecessary steps while still preserving the accuracy and control required in an enterprise environment. After several iterations, the workflow became far more streamlined and intuitive.

Another important challenge involved designing the core user journey.  Enterprise applications often involve more complexity than consumer apps. Creating a B2B order, for example, is not as simple as placing an item in a cart and checking out. There are validations, approvals, and multiple data inputs that need to be handled correctly. Because of this, the goal was not simply to make the interface minimal. It was to make sure users could complete their tasks with the least possible friction.

We reviewed the AI-generated flows carefully and refined them to create what we considered the most efficient path through the application. Every step had to serve a purpose.

Debugging in AI assisted software development

No development process is perfectly smooth, and this project was no different. There were times when screens did not load properly or parts of the interface behaved unexpectedly. Instead of seeing these issues as major setbacks, we treated them as part of the process and worked through them step by step.

One helpful method we used was visual debugging. Whenever something broke in the interface, we took a screenshot of the problem and shared it with Gemini. Since Gemini already understood the structure of the application, it could often identify the issue and suggest a fix. Sometimes we also opened the browser’s Inspect Element console and copied the error messages we found there. Even when the technical details were not fully clear to us, sharing those errors with Gemini helped it understand the issue and suggest the right solution.

Over time, Gemini started acting like a bridge between design ideas and technical fixes. It helped us understand what was going wrong and how to correct it.

The most surprising outcome of the project was the speed at which everything came together. What would normally involve weeks or months of back and forth between designers and developers was completed in roughly 18 hours. Since this was driven by a single UX team member as an experiment, the timeline also reflects a learning curve. With a more engineering-heavy setup, this process could be even faster. Most of those hours were not even consecutive. They were spread out between other responsibilities during the week.

Despite the unconventional process, the final result was a fully functional and responsive web application built from scratch. More importantly, the experience gave us a glimpse into how product development may evolve in the coming years. Designers are no longer limited to producing static layouts that are later handed off to developers. With the right tools and workflows, they can take a much more active role in shaping how software is actually built.

, experiments like Zenith show how AI-assisted software development can reshape the way products are designed and built. By combining structured thinking, strong product design, and modern AI tools, we are constantly looking for better ways to turn ideas into working software faster and more efficiently. Zenith showed us that the real skill is not just designing screens. It is understanding systems, defining clear product logic, and guiding intelligent tools in the right direction. When those elements come together, the gap between idea and implementation becomes much smaller. And sometimes, work that once took months can begin to take shape in hours.

Discover how Expeed Software built an enterprise-grade e-commerce admin app in just 18 hours using AI—without wireframes. Learn about AI orchestration, structured prompting, and faster product development workflows.

HowBuiltE-commerceApp2.png

How We Built an E-commerce App in 18 Hours Using AI | Expeed Software

Technology and Architecture

How We Built an E-commerce App in 18 Hours Using AI Without a Single Wireframe

shopgrid challenge.jpg

As it grew, Shopgrid, a technology company that creates cloud apps and customized solutions, sought to fortify its digital systems. Developer participation was necessary for even minor adjustments like adding a page or a menu. Although this strategy had previously been effective, as the company expanded and the need for new features increased, it became more and more time-consuming.

Shopgrid's management began to realize that the company's next phase of expansion required more adaptability. They wanted their internal team to be able to handle updates on their own, lessen the need for technical support for regular adjustments, and ensure that the systems could grow with the company.

To meet these demands, Expeed Software suggested a two-stage strategy. The first stage was designed to give Shopgrid a solid online presence and a platform for digital interaction. In addition to building the foundation for more sophisticated capabilities, this established the brand in the marketplace.

The creation of a dynamic admin portal was the main goal of the second phase. This metadata-driven framework allowed the system to quickly adjust to new needs. The framework could dynamically generate modules, menus, pages, and controls. Expeed used this same metadata structure to store data in the database and to render pages based on APIs. To maintain uniform layouts across all modules and make management easier for the client's team, reusable user interface controls were implemented.

Scalability was considered when designing the framework. In addition to meeting immediate needs, it provided a framework for upcoming improvements and project reuse.

Before: 3–5 days (developer dependency) → After: < 1 day (self-managed)

Approximately 70% fewer developer hours required for routine updates such as menus and pages

● Number of Updates Managed by Business Team

On average, 25–30 updates are now managed independently by the business team each month

Before: 2–3 weeks → After: 3–5 days with the metadata-driven framework

Module rollout became 3x faster compared to the earlier process

The average cycle reduced from ~18 days to ~5 days from idea to deployment

95% compliance with design standards achieved through reusable UI controls

Inconsistencies across pages dropped by ~60% after standardization

● Modules/Pages Added Without Redevelopment

More than 20 new modules and pages were added in the first year without major redevelopment

Year-over-year growth in business demands supports zero redesigns of the core system

Approx. 40% fewer developer hours spent on maintenance tasks

Estimated savings of $35K–$40K annually by reducing repetitive coding and support needs

More than 50% of developer effort was redirected from routine maintenance to strategic projects

Internal feedback showed ~90% satisfaction among business users due to increased independence

All new business demands in the past year were met without delays caused by technical bottlenecks

An estimated 200+ man-hours saved per month through business self-service and faster rollouts

 worked with Shopgrid to move from a developer-dependent model to a metadata-driven framework that empowered the internal team. The two-phase approach delivered both a robust online presence and a dynamic portal that combined independence, speed, scalability, consistency, and reduced maintenance effort.

By aligning technology with Shopgrid’s growth objectives, 

provided a solution that delivered immediate business value while establishing a foundation for long-term expansion.

This case study examines how Shopgrid modernized its internal systems with the assistance of Expeed Software to reduce reliance on developers and boost business expansion. The solution streamlined feature rollouts, increased operational efficiency, and provided Shopgrid with the flexibility to scale without technical bottlenecks by combining a metadata-driven framework with a phased strategy.

shopgrid.jpg

See how Shopgrid built a scalable website and a metadata-driven admin portal with dynamic pages, reusable controls, and faster rollouts.

Shopgrid Case Study | Scalable Metadata-Driven Admin Portal

Expeed's Shopgrid Two-Phase Transition

Skillflow is a plug-and-play AI chat layer that lets users type simple prompts like “create a lead” or “find a user,” and handles the rest behind the scenes. It interprets intent, executes the necessary API calls, and returns results instantly without changing the core product. For Saaslogic, whose CRM already has a solid backend, Skillflow provides the missing piece: a conversational interface that makes everyday tasks quicker, simpler, and more intuitive.

Saaslogic’s CRM already worked well. It was structured, reliable, and supported all key sales operations. But it was built around clicks and forms. Creating a lead involves multiple steps. Searching for a user meant knowing exactly where to look.

Saaslogic wanted to remove that friction. They wanted users to simply type what they needed, like talking to an assistant, and let the system figure it out. The idea was to make the CRM more intuitive without changing its core functionality.

But the product wasn’t designed to interpret natural language or trigger backend logic through simple prompts. In this context, Skillflow emerged as the crucial component.

skillflow integration

Skillflow is Expeed’s plug-and-play platform that brings a conversational interface to existing systems. Skillflow establishes a connection with any product's backend and translates user prompts into actual API operations, all without necessitating alterations to the frontend or the underlying architecture.

The integration began with a simple setup. Saaslogic was onboarded into the Skillflow backend and provided with credentials to log in.

From there, they added their CRM server details—name, description, and base URL. Then came the Swagger file: the document that defined all available APIs in the CRM.

Once uploaded, Skillflow parsed the file and generated a full list of actions the system could perform, such as retrieving users, listing lead sources, or creating records.

With that in place, We worked with Saaslogic to define the tasks—called skills—that users could trigger with a prompt.

A skill represents a complete task the system can perform based on a user’s input. 

Say a user wants to create a lead. The CRM expects a source ID—but the user only knows the source name. Skillflow handles that. It first calls the API to fetch all sources, then finds the right one, retrieves its ID, and passes it to the API that creates the lead.

All of that happens in the background as part of a single skill.

Skillflow doesn’t just map inputs to one API. It handles multi-step sequences, where one API’s output becomes another’s input. This makes even complex actions feel simple for the end user.

Every skill can be monitored in real time. Skillflow logs user prompts, tracks which APIs were used, and shows what was returned—giving Saaslogic full visibility into how the chat is being used and where to improve.

The results helped the team understand what users were trying to do—and which skills to prioritize next.

Bringing the chat into the CRM was straightforward.

Skillflow provides a ready-to-use chat interface, hosted externally but embeddable via a unique URL. Saaslogic dropped this into their CRM using an iframe and passed user authentication tokens as parameters.

No redesign. No frontend work. The chat icon appeared, and users could start typing.

Skillflow handled everything in the background. The UI came built-in. The logic was already wired. From the user’s perspective, it felt native.

•	Create new leads without navigating menus

•	Receive structured responses—like tables or clean summaries

•	Simple, natural prompts trigger everything.

The experience is swift. The outputs are clear. The interface feels effortless. 

 Saaslogic wanted to enhance the user experience without touching their existing workflows. No redesign. No frontend rewrites. There was no additional complexity involved.

By integrating Skillflow, Expeed gave them exactly that—a conversational interface layered over the systems they already had.

Now, Saaslogic’s CRM responds to human language. It performs complex operations through chained API calls. And it feels lighter, faster, and more intuitive to use.

Adding new skills takes just a few minutes. Updating APIs is as simple as uploading a new Swagger file. More features—like deal tracking, filtering, and task management—are already in the pipeline.

Each tenant has their credentials, chat URL, and configuration. Onboarding is clean and swift.

The change isn’t just a UI improvement. It’s a shift in how the product works—without changing what already works.

’s CRM evolved into a more dynamic, conversational system. Routine actions became quicker to complete, workflows felt smoother, and navigating the platform required fewer steps. The core capabilities stayed powerful, but the experience became lighter, faster, and more intuitive, helping the CRM deliver more value with less effort.

Modern CRMs offer powerful features, but their complex form-based workflows often slow users down. Saaslogic, a billing and subscription management platform, wanted to change that by making it possible to manage tasks through conversation instead of clicks. That is where Skillflow stepped in

skillflow

How Skillflow Helped Saaslogic Bring a Chat Interface to Life 

This project addresses the overwhelming requirement for automation in the key fields of the recruitment process by developing AI agents to handle various tasks. This intelligent tool efficiently manages the entire job recruitment process, which previously required the HR team's direct involvement. 

The ultimate objective of the project was to develop specialized AI agents to handle various steps involved in candidate recruitment, thereby making the entire process flawless and easier. 

Upload resumes to the software to avoid the hassles of managing them manually. 

 We use a powerful AI agent to evaluate resumes and extract candidate information to accelerate candidate matching. 

 We use an AI agent to shortlist resumes by matching them with the provided job description, followed by a scoring process. 

 The system automatically generates customized interview questions to efficiently evaluate candidates. 

 Handle bulk amounts of resumes seamlessly, ensuring precision and accuracy while filtering and scoring the suitable candidates. 

We built our tool using CrewAI’s agent-based framework to handle specific tasks throughout the recruitment workflow. CrewAI is a multi-agent platform that utilizes powerful AI agents and helps streamline multiple processes within an organization in an efficient manner. 

 You can use the software to automatically upload resumes and store them in a database. 

Our modular system utilizes specialized CrewAI agents for a variety of tasks: 

 The system assigns scores to each candidate based on skills, years of experience, and other criteria. The system automatically analyzes the response transcripts after the interview to identify the top candidates.

The Angular framework provides a dynamic user interface.  

Structured data management uses the Postgres Relational Database. 

By automating critical tasks such as resume checks and interview assessments, the system ensures a highly efficient process that significantly reduces hiring time. 

Unmatched Accuracy in Candidate Screening 

By carefully matching candidate profiles with job descriptions, the AI-powered scoring tool ensures accuracy and decreases the likelihood of overlooking potential talent. 

Unlike assessors, the system is an unbiased resume and interview process, promoting fairness and equity throughout the hiring process. 

Running labor-intensive steps like resume classification and query generation increases productivity, allowing recruiters to focus on strategic tasks. 

AI agents accurately analyze candidates’ comments and responses, eliminating discrepancies often found in manual assessments. 

The tool can consider candidates solely on merit, ensuring unbiased decision-making and reducing the impact of unconscious bias. 

Our AI agent-based recruitment software is a testament to Expeed’s commitment to innovate in the digital space. Leveraging state-of-the-art technologies, along with CrewAI's modular framework, we have built a scalable, efficient, and intelligent solution that solves the real challenges of recruitment. With its ability to automate processes from screening resumes to reviewing interviews, this tool saves time significantly and improves the recruitment results. 

Expeed’s AI-driven recruitment platform revolutionizes hiring processes by automating tasks such as resume screening, candidate scoring, and interview evaluation. Built on CrewAI’s agent-based framework, the tool simplifies recruitment workflows, ensuring scalability, precision, and efficiency.

CS- Banner - 498 X 515.jpg

Transform your recruitment process with Expeed Software's AI-driven application: experience smarter hiring & improved efficiency with our innovative solutions. 

AI-BASED APPLICATION | AI-Driven Recruitment Software 

AI-BASED APPLICATION

AI-Driven Recruitment Software

We developed a platform that used data analytics to identify suspicious claims and reduce wrong claims.

With over 50 years of experience providing health insurance options to commercial businesses, individuals, and Medicare/Medicaid patients, our client was looking to update their platform and digitize their processes in order to increase efficiency, reduce computational errors, and identify fraud.

As a major player in the health insurance industry, our client was following business processes, practices and government regulations that had been in place for decades, thus resulting in more operational and resource costs. Additionally, their processes failed to capture misinformation or fraud thus resulting in losses through wrong insurance claims.

The Expeed team analyzed the problem areas and came up with multiple analytical solutions using Data Science, Machine Learning. We also developed a Web and Mobile application that allowed the new features to capture data and present them for evaluation in a much easier way.

The client started saving thousands of dollars every month which was otherwise lost on payments to wrong claims. The process also helped in better resource utilization resulting in long term patients getting better service. Improved customer satisfaction helped boost the brand value of the company.

We developed a solution using multiple Machine Learning techniques to identify all the suspicious claims. This prevented wrong claims from happening in the first place and also reduced the cost and effort spent on recovering wrong claims each year.

 The insurance industry in general, and our client in particular, receives a number of claims from healthcare providers with wrong amounts and wrong procedure codes.

Our client’s claims processing system failed to catch these invalid claims, resulting in hundreds of millions of dollars of payments towards these invalid claims. This had been a known problem and our client spent close to 200 million dollars in efforts towards recovering only a portion of these wrong payments.

We developed a solution using multiple Machine Learning techniques to identify all the suspicious claims based on the recent amendments to the underlying contracts, comparison of the distribution of claims across procedure codes between different pay periods, and comparison of price distribution for procedure codes between different pay periods. This gave a much smaller set of all claims with potential issues and the recovery team was able to act on all of them instead of randomly picking a few of the claims from the whole set. This reduced the cost of the effort to recover and prevented wrong claims from happening after a month. It resulted in significant financial savings to our client.

Our client’s existing system was unable to meet the SLAs that were required in order to respond to customer complaints, resulting in penalties. To overcome this problem, our client outsourced the activity of manually reviewing each of the complaints and assigning them to the appropriate department. This manual process was costing a significant amount of money and was sometimes error-prone resulting in further penalties.

Our team stepped in to resolve this problem through process automation where we used the techniques of text mining, sentiment analysis and machine learning algorithms. With this new automated system, our client was able to eliminate the cost of a 40-member outsourced team and significantly improve the chances of meeting SLAs through more accurate prediction of the department that can address the issue based on the text of the complaint.

Long Term Medicaid Services Process Optimization: 

As part of Medicaid program, our client provides long term care services to Medicaid patients. As part of the program our client employs a number of care coaches and third-party service providers to attend to the needs of Medicaid patients. But there were inefficiencies in the system of care coach assignment and need-evaluation of the patients, which was resulting in revenue loss and fraud.

Our team developed an application to recommend the assignment of care coaches to patients with the goal of minimizing the distance they travel and maximizing the time they spent with the patients, with a near-uniform distribution of workload among care coaches. This resulted in multi-million-dollar travel-related savings, better results for patients, and work-life balance to care coaches.

We also used Machine Learning techniques to better evaluate the individual needs of every patient. We took the patient’s medication history and predicted services they would benefit from. Care coaches started using those recommendations while offering services to the patients. This resulted in cost savings, happy patients, and reduced the scope for fraud that could be involved in offering services.

The system uses advanced data analytics and machine learning to prevent fraudulent insurance claims. The system automates complaint responses and data analysis of claims, minimizing computational errors and enhancing efficiency to reduce operational costs. 

Fraud Detection.png

Fraud Detection and Claims Optimization System

We designed an NLP model that could quickly identify and assign treatments to patients based on the seriousness of their medical conditions.

As one of the leading healthcare solutions and treatment providers in the country, our client and their employed doctors and care administrators were finding themselves time-stricken to evaluate the ever-increasing number of patient files in order to assign treatment based on urgency. All the extra resource hours being spent on the review of patient files was adding to the lack of healthcare support staff which was becoming a growing problem in light of the COVID-19 situation.

The client was facing issues with its healthcare staff being overworked and unable to adequately focus attention on patients in urgent need of care. There needed to be a system that would help identify critical care patients and assign them immediate attention.

Our team employed natural language processing techniques on a sample of ~5,000 medical dictations to build a classification model, determining how urgently a patient needed treatment.

The artificial intelligence solution we created advised the healthcare practitioners and helped them to identify urgent care requirements, with over 95% accuracy. The model ensured that the client’s healthcare staff was not over-burdened with the task of identifying the most critical patient case files.

The model performs with greater than 90% accuracy, automatically identifying urgent cases in the vast majority of instances and augmenting our client’s ability to rapidly and accurately perform triage.

 We used Azure Machine Learning services to quickly train the model using a sample size of over 5,000 medical dictations. Through this exercise that was monitored closely for 100% accuracy in diagnosis, our model was able to scale quickly and expand effectively to review and identify critical cases on a much broader sample in a very short period of time. In order to reduce risks and situations where patient cases get identified wrongly as non-critical, the model was trained to be very conservative while drawing conclusions.

The model also allowed for quick deployment into a production environment.

 The fully developed model now works with greater than 95% accuracy and uses our Artificial Intelligence solution to advise healthcare practitioners about the urgency associated with each case. Our solution has eliminated the need for additional time being spent by the already overburdened healthcare staff.

The NLP-based model improves healthcare efficiency, underlining and prioritizing urgent cases among patients with over 90% accuracy. Powered by Azure Machine Learning, this system analyzes medical dictations to make automatic treatment appointments.

68AI_nlp.png

The NLP-based model improves healthcare efficiency, underlining and prioritizing urgent cases among patients with over 95% accuracy. Powered by Azure Machine Learning, this system analyzes medical dictations to make automatic treatment appointments'

NLP-Based Treatment Urgency Identifier

We offered a versatile payment gateway integration solution designed for subscription businesses.

Our team at Expeed recently implemented a transformative payment integration project with saaslogic, a subscription management platform, and billing automation software. Keeping in mind, the demands of a rapidly growing subscription economy and booming SaaS businesses, we put together a robust integration solution with multiple payment gateways and payment methods that offered businesses the flexibility to sell products and services through subscription models.

saaslogic needed seamless integration of multiple payment gateways so that it could serve a broad spectrum of B2B and B2C businesses. Our team made sure that the integrated payment solutions allowed saaslogic merchants to sell their products all across the US as well as internationally. This also meant handling varying transaction charges across different payment methods, card types, and regions while keeping transaction costs reasonable for both saaslogic merchants and their customers. 

From providing connected account solutions for pre-existing Stripe account merchants to offering saaslogicpay accounts with some of the lowest transaction charges to new merchants, our team carried out multiple payment integrations that allowed flexibility, ease of access, and scalability to subscription businesses.

: Integration with all the major payment gateways and methods were the first and the most important task we took up for saaslogic. Stripe being a popular payment gateway in the US, a basic Stripe integration was the primary ask for the platform. Our team set it up so that merchants could seamlessly link their existing Stripe accounts for processing payments from customers. Alternatively, we also created provisions to create new Stripe accounts exclusively for saaslogic. All payments initiated through the saaslogic platform were channeled through this dedicated Stripe account, ensuring efficiency and ease of use. Our team also integrated Authorize.net as an alternative payment gateway for the platform.

: Since saaslogic had already established markets in India and Africa, the platform needed to have other payment gateways integrated apart from Stripe and Authorize.net. For instance, we chose to integrate the platform with Razorpay for its Indian customers. This provided a location-specific payment gateway based on the business addresses. Configurations were set up to enable the platform to auto-select the payment gateway to be used based on the nearest selling address of the merchant (or process with default address in case of other locations). This allowed the end customer access to all the payment methods offered by the selected payment gateway. 

It also allowed merchants to scale their businesses globally without having to deal with the complexities involved in setting up payment collection methods specific to different regions around the world. Similar configurations were made on mPesa integration for saaslogic’s African merchants.

: For merchants who had no prior payment gateway accounts, we set up provisions to offer a saaslogicpay account – a built-in payment gateway solution by saaslogic. This way, not only did the merchants benefit from some of the lowest transaction fees, but they were also able to provide their users with a continued user experience and transaction efficiency.

Surcharge Flexibility and Different Transaction Fees

: One of the key features of saaslogic as a payment platform was the ability to offer surcharge flexibility. The team set up configurations that allowed merchants to easily pass the entire transaction fee directly to the customer or share a percentage of that cost. This included the ability to add surcharges, either as fixed amounts or as a percentage of the transaction. This is an especially important feature for merchants who want to allow multiple invoice settlements in single transactions. 

Another key integration challenge was enabling merchants to choose the transaction fees for different payment methods. For example, businesses often have to pay different charges for transactions conducted via ACH bank transfers, debit cards, corporate cards, personal credit cards, etc. Our development team made sure that saaslogic customers were able to easily select the transaction charges that applied to their businesses in different regions based on the payment method selected.

Multiple Payment Gateways for Diverse Operations

: Another use case we had to keep in mind while working on the payment integrations was businesses maintaining separate accounting books for different service lines or locations. In such cases, we created provisions to allow multiple payment gateway integrations for the same merchant but with different bank accounts. This feature enables businesses to keep their books clean and easy to track.

Payment Options for Recurring Customers & Data Compliance

We developed a robust payment integration system for Saaslogic, enabling seamless multi-gateway support and advanced payment flexibility tailored for subscription-based businesses.

Technology with a Greater Purpose

Building Enterprise-Ready Computer Vision Solutions

Unlocking Business Value Through Legacy Application Modernization: VB6 to Cloud Conversion Case Study

How We Built an E-commerce App in 18 Hours Using AI Without a Single Wireframe

Expeed's Shopgrid Two-Phase Transition