The obstacle was waiting at the entrance. Nivi, a digital health company, connected people to information about family planning, vaccinations and maternal health through messaging channels. But first came the demographic questions. Before users could reach anything useful, they had to fill in the blanks. Too many left.
- Jeavio builds software and production AI for companies with complicated existing systems.
- Its customers include private equity-backed businesses, enterprises and health technology companies.
- Its revealing habit: diagnose a specific blockage, test a change, then engineer the delivery around it.
Jeavio’s intervention was disarmingly ordinary. Replace the questionnaire with an invitation: “Tell me a little bit about yourself.” The engineering and product team worked with Nivi on a proof of concept, then a production experience and measurement framework. Jeavio reports an 80–90% increase in onboarding conversion. That is a relative improvement in conversion, not a claim that nine out of ten users completed onboarding.
The case makes a useful introduction to Jeavio because the result begins with a judgment about people. The first thing going wrong was the interaction before the health information. The team chose that point as the fastest place to make a difference.
Jeavio case study · relative improvement
“Tell me a little bit about yourself.”The prompt in Jeavio’s Nivi case
A business built around the blockage
Jeavio sells the expertise and engineering capacity to diagnose technology problems, build products and modernize systems. Its market includes private equity firms, their portfolio companies, enterprises and growing software businesses. These are buyers with an awkward combination of obligations: change the technology, improve delivery, and keep serving the customers already paying for it.
The firm began in 2015 with brothers Yash and Uday Shah. In a September 2025 interview, Yash described an origin less tidy than a pitch deck. While in India caring for his mother, he assembled a business around investing, mentoring entrepreneurs and building products. Venture services came first; larger private equity-backed engagements followed. He said the brothers owned the company without outside investors and had grown it beyond 300 employees.
That history helps explain its position between advice and implementation. Product leadership sits alongside software delivery. The current offer combines US-based product leadership with offshore engineering scale, and describes CTO/CPO-led, fixed-price engagements aligned to outcomes. A buyer is commissioning a defined piece of work, with people responsible for getting it into use.
The meeting is part of the machinery
One of Jeavio’s products addresses a problem that barely looks technical: important explanations disappear. An architect describes an integration. A customer explains an exception. Someone records the call. Three months later, the next team starts asking the same questions.
ADAPT, Jeavio’s proprietary AI knowledge platform, ingests recordings, interviews and documents, organizing their contents into searchable project context. Its knowledge-capture service links insights to stakeholders and original material, identifies gaps, and creates a living knowledge base. The useful promise is continuity: an incoming engineer can inspect the reasoning behind a decision instead of inheriting only its consequences.
Jeavio says ADAPT began as an internal platform in 2023. Today it supports discovery, innovation and modernization work. The diagnostic service includes a risk report, recommendations and executive debrief, with ADAPT access during the engagement and for up to 30 days afterward. Knowledge preservation becomes a specified deliverable.
Six months. A different cloud. Same customers.
Consider another engagement. An unnamed B2B software company received a Microsoft investment that required moving its live application from AWS to Azure. The investment changed the platform decision; customers still expected service while engineers changed the foundations.
Jeavio describes containerizing Java services on Azure Kubernetes Service, modernizing APIs, moving databases and introducing versioned database deployments. The migration proceeded in stages with validation and rollback options. Its account reports completion within six months, zero downtime, capacity for five times the active users, and sprint cycles shortened from three weeks to two.
The sports sponsorship case uses a related precaution: Jeavio says it launched a new cloud-native platform in ten weeks while running it alongside the old system. The transferable idea is practical. Preserve a working path while proving the replacement under the conditions the business actually faces.
What earns a rebuild?
Jeavio’s services give customers several entry points. Discovery and Diagnostics examines architecture, risks and constraints. Innovation Engineering tests ideas against technical feasibility, user demand and market viability. Legacy Re-Platforming covers infrastructure, integrations, containers and migration. Teams can move from assessment into execution and ongoing cloud operations.
Its QA case supplies a small example of changing course. Manual regression testing consumed seven or eight engineers for two weeks per sprint. Jeavio initially used Selenium, then adopted Playwright for speed and parallel execution. The lesson a reader can copy is to measure the delivery constraint and let those requirements influence tool selection.
For buyers, cost means more than the project fee. Jeavio’s production AI material calls out inference spend, latency, evaluation and the responsibility for errors. Fixed-price work still requires an agreed scope. Comparing proposals sensibly means specifying the workflow, expected usage, acceptance tests and the system the client will receive.
The system has to stay useful
Jeavio’s current differentiation rests on client ownership: delivered agents, their data foundations and operating logic remain with the customer. That places it among engineering partners for proprietary workflows. A standardized need that packaged software already handles gives a buyer less reason to commission this work.
Custom AI also needs accessible domain experts, usable data and a team prepared to maintain evaluations. Jeavio emphasizes expert-annotated examples, regression checks and drift monitoring. Those requirements put a limit on the proposition: a clever model cannot supply missing organizational judgment. Nivi’s doorway remains the useful test. Find the moment where work stops helping people, change it, and measure whether they get through.