A grocery order should not need half a day to clear the computer. Yet that was the bottleneck in Peapod’s old system: 12 hours of processing before a delivery operation could get on with its job. Egen’s account of the overhaul ends with that wait reduced to seconds. Somewhere between the shopping basket and the doorstep, a great deal of unglamorous engineering had paid off.
That is a useful introduction to Egen, the Chicago-area technology services company selling cloud, data, platform engineering and artificial intelligence expertise. Its work lives behind familiar customer experiences: ordering dinner, booking travel, maintaining equipment. The customer sees a quicker response. Egen works on the systems that make the response possible.
The company’s current pitch is to accelerate value. The interesting question is what that means once the sales presentation ends. Its customer projects suggest an answer: shorten the wait, lower the operating burden and give the client’s own engineers a system they can keep improving.
The grocery clock was the problem
Peapod’s customers wanted same-day delivery. Its technology had been built around a slower world. Egen describes an aging, on-premises platform struggling with growth and newer ways to shop, collect orders and pay. Customer expectations had moved; the underlying machinery had trouble following.
The engagement lasted 19 months. Egen used Apache Kafka messaging and a microservices architecture, modernized more than 120 legacy applications and rebuilt the cloud foundation. Proofs of concept and an early working product preceded the relaunch. Training, documentation and transitional support accompanied the handoff.
The lesson is refreshingly concrete. Find the delay that blocks the commercial promise, then work backward through the systems causing it. “Same-day” is a promise a shopper understands. Architecture earns its keep when the business can finally make that promise.
Three businesses, one engineering proposition
Raghu Potini founded Egen in 2010. Its early specialization in cloud migration, application modernization and platform engineering supplied the foundations of today’s business. In August 2023, it merged with SpringML, a data, analytics and AI services company founded in 2015. Recognize, the technology services investment firm, led the transaction.
The logic was complementary expertise. One business could modernize the applications and infrastructure; the other brought depth in the data and models running through them. The combined company retained the Egen name. In October 2024, Egen announced the acquisition of Qarik, a Google Cloud specialist whose team included veteran Google leaders, architects and engineers.
These deals help explain Egen’s position in the market. It is an implementation partner for organizations with substantial existing systems and consequential operating problems. Its business model centers on selling consulting and engineering engagements to enterprises and public agencies. The customer is buying the work of designing, integrating and deploying technology, along with the expertise to make it useful.
Google Cloud is the strategic center of gravity, and Egen is a Premier Partner. Salesforce is also an ecosystem partner. A buyer considering Egen is choosing between specialist delivery firms, broader systems integrators and building the capability internally. Egen’s argument rests on combining several technical disciplines with concentrated knowledge of the Google environment.
A burger chain’s smaller operating bill
Wendy’s offers a second view of the work. Its small technology team needed a platform that could support digital growth and faster software delivery. Google Cloud recommended Qarik, now part of Egen, to help with the implementation.
The team migrated systems from on-premises infrastructure and third-party clouds to Google Cloud. Egen reports a 45 percent annual reduction in the new platform’s operational costs, fully automated deployment and a change in feature turnaround from two days to one hour. Those are project-specific operating results, rather than a forecast for every customer.
The work also included coaching and standard development practices. That detail deserves attention: a platform has a life after the consultants leave. An internal team needs to understand how to deploy, maintain and extend it.
Caterpillar’s project had a different scale and division of labor. Egen handled architecture and development while Uptake handled product and sales. The work connected equipment and dealer data, supporting more than 250 predictive maintenance models and 12 billion daily sensor readings, according to Egen.
Reported cloud costs fell 65%. Before = 100; this is an index, not a dollar budget.
Cloud-managed services helped cut Caterpillar’s cloud costs by 65 percent, Egen says. Model building and deployment moved from a quarterly cadence to a weekly one. The economic attraction lies in both measures: a smaller operating bill and less time between having an idea and putting it to work.
The travel agent needs an examiner
At Sabre, the AI assignment was partly about testing. Concierge IQ, Sabre’s travel concierge for airlines, needed to evolve without sacrificing quality and stability. Egen built an automated validation pipeline that checked the agent’s responses against defined scenarios and expected outputs, then fed results into the existing development workflow.
Egen reports that a process requiring hours or days of manual checking now runs in minutes, saving at least eight engineering hours each time the pipeline is used. Prompt engineering was another part of the engagement. The reusable testing infrastructure lets Sabre’s developers keep making changes and seeing their effects.
For anyone building an AI service, this is a useful practice to copy: create the examination alongside the agent. Faster development becomes more valuable when the team has a repeatable way to see whether a change improved the experience. The test scenarios themselves still require judgment; passing an irrelevant test would be a hollow victory.
From patient records to potholes
Egen’s data work also reaches healthcare. Integra Connect, a precision medicine oncology platform company, faced slow customer onboarding and a manually monitored data pipeline. It wanted a more efficient way to acquire and share information, including de-identified patient data for pharmaceutical research.
Egen built a platform using event streaming, microservices and serverless architecture. Here, the practical problem was the movement and preparation of information across a complicated operation. The project illustrates the company’s broader data offering: organize information, connect systems and build the pipelines that applications and AI depend on.
Memphis provides a more visible example. The city wanted to identify potholes and blighted properties without relying so heavily on residents’ reports. Cameras and machine learning expanded what officials could see. Egen’s case study reports pothole detection accuracy above 96 percent as the models learned to distinguish holes from manhole covers and other objects.
The history matters: Google’s January 2021 account credits SpringML, which later merged with Egen. It describes bus-mounted cameras and a project that assisted in repairing 63,000 potholes in a year. This is part of the combined company’s inherited experience. It also puts a sensible boundary around the achievement: detecting a defect helps crews prioritize work; someone still has to repair the road.
Reusable code, a real team and six weeks to prove a point
Egen packages some expertise into accelerators. Its Document Intelligence Accelerator uses a reusable code base to extract useful information from documents. Proposed applications include nurse handoffs, underwriting, manufacturing proposals and retail advertising workflows. Its data sheet describes an initial six-week proof of concept followed by expansion into production.
The starting requirements are unusually useful buying guidance: an executive sponsor, a valuable and defined use case, documents containing the relevant data and a team that can commit to the project. An organization lacking those ingredients should expect preparation work before it can sensibly follow that schedule.
The Voice AI Accelerator addresses conversations, including customer service, shopping assistance and appointment management. These offerings give customers a starting point that Egen can adapt to a workflow. The work still involves integration, evaluation and adoption inside the organization.
That distinction matters when assessing cost. A reported percentage reduction in an operating bill measures one benefit; a buyer also needs to account for implementation effort, cloud consumption and the people who will run the resulting system. A useful business case puts those commitments beside the time saved or new service enabled, then checks the result after deployment.
The newest assignment is changing how people work
Egen’s July 2026 launch of an Agentic Change Enablement Practice makes the human side of that equation explicit. Led by Christy Mayr, the practice covers workflow redesign, training, workforce readiness and adoption measurement. Alim Somani, the company’s current CEO, put the premise plainly.
“Agentic AI will not create value simply because it is deployed.”
Alim Somani, CEO of Egen / July 2026
This is an expansion of what Egen sells, and an acknowledgment of where a deployment can stall. A technically functioning agent needs a place in the working day, rules for its actions and people who understand what to do with its output.
The firm describes its culture in terms of practical delivery, curiosity and collaboration. Its May 2026 partnership announcement with DataStory, a student-run consulting group at UC Berkeley, adds a specific example: students developed and tested a model to predict role allocation and project effort from descriptions and historical data. Egen was applying machine learning to the planning of its own kind of work.
Other efforts extend its access to research. A December 2024 partnership with Carnegie Mellon University and UltronAI aimed to develop industry applications, including computer vision. Everest Group subsequently classified Egen as a Leader in its 2025 assessment of Google Cloud services specialists.
The most persuasive evidence remains closer to the ground: an order moving sooner, a software release taking less time, an engineer getting an afternoon back. For prospective customers, those are useful units of ambition. Pick a problem with a clock or an operating bill attached. Establish the starting point. Make the change. See whether the promised improvement survives an ordinary working day.