The scanner is ready. The patient is waiting. Somewhere in the paperwork is the answer to a small, consequential question: can this person’s implant safely go into this MRI machine? Before an image exists, somebody has to read the instructions. In NextGen Invent’s account of one imaging provider, that research took three to four hours.
It is an unglamorous place to look for an AI business. It is also a useful one. The delay has an owner, a consequence and a clock. NextGen Invent, the technology services company founded by Deepak Mittal, builds around problems like this: the awkward joins between information, software and the people trying to get something done.
- Builds custom AI, data systems and digital products for business clients.
- Works across healthcare, life sciences, logistics, manufacturing and finance.
- Joined Straive in June 2026, after making the 2022 Inc. 5000.
The bottleneck before the scan
Its ImplantIQ case study describes an agentic AI system that extracts implant information, compares device specifications with MRI parameters and presents recommendations alongside color-coded evidence. A dashboard tracks clearance status. The company reports that manual research fell to about ten minutes within weeks of deployment.
That is a claim about an operational task. It should be read at that scale. Faster document work can help the department move; it does not establish clinical accuracy across every implant, patient or scanner. The useful design detail is the evidence beside the recommendation. A technician needs something to inspect before acting.
One client engagement. Research time, not total appointment time. Bars use the three-hour baseline.
A company that sells the joining-up
NextGen Invent sits in the professional services market. Customers hire it for strategy, engineering and implementation: deciding what to build, preparing data, developing applications, connecting systems and keeping software running. It offers web and mobile development, cloud work, testing and DevOps alongside AI. The business extends well beyond selling access to a model.
Its named customer testimonials include medical imaging company Elio AI, mobility equipment maker Permobil and Integrated Logistics Solutions. Many published project accounts leave the customer unnamed. That mix places NGI between specialist AI consultancies, broader engineering firms and the internal teams a client might otherwise recruit.
The practical distinction it offers is proximity to the problem. Straive’s acquisition announcement describes engineers working close to client challenges, backed by industry expertise. That is a positioning claim worth testing in procurement: ask who will work with the operations team, who owns integration and who stays when the prototype meets production.
Sixty-three ideas walk into a warehouse
A warehouse engagement offers a sharper picture of the method. The client had static rules for picking and task assignments, while operational information was scattered across logs, equipment telemetry and other datasets. Before promising intelligence, NGI had to establish which information could support a decision.
The team assessed more than 40 datasets across eight dimensions, including accuracy, timeliness and completeness. It identified 63 AI opportunities, narrowed them to 13 and helped leadership approve five initiatives. That documented move from uncertainty to approval came through workflow analysis and feasibility assessment.
NGI reports a 15% improvement in warehouse throughput and a 35% reduction in pick-path inefficiencies. Those figures describe this engagement. The transferable lesson is the selection process: score opportunities against actual data and operational value, then give a manageable number permission to proceed. A long list of possibilities makes a poor work order.
The foundations get a vote
Another published engagement, involving a digital health platform, starts with a less photogenic inventory: disconnected datasets, static credentials, missing audit logging and tightly coupled software. The platform wanted to develop its data and AI capabilities. Its foundations needed attention first.
NGI’s response included a data strategy, security controls, architectural recommendations and an audit-readiness blueprint. The sequence matters. If access cannot be traced and information cannot be matched reliably, adding an AI interface gives the organization another surface to manage. The underlying difficulties still need owners and deadlines.
“Our success lies in our ability to give a competitive edge to our customers.”Deepak Mittal, 2022 Inc. 5000 announcement
Healthcare also tests whether software fits its surroundings. NGI describes a breast-lump triage project combining a handheld GE Vscan ultrasound device with an AI-supported mobile app. Designed for use by non-radiologist healthcare workers, it addresses access to assessment. The company reports use in evaluating more than 530 women. Triage and a definitive diagnosis remain different jobs.
The commercial question is equally practical. Ask for a proposal that separates discovery, data preparation, engineering and ongoing support. Compare the full cost with the delay being removed, including the time employees spend checking outputs. A shorter task is valuable only if the surrounding operation can use the saved time.
NGI describes its own culture in terms of getting things done. The buyer’s version of that promise should be observable: a named delivery team, agreed acceptance criteria and a working handover. Those details help distinguish an engineering engagement from an interesting series of meetings.
A larger company, the same small delays
In 2022, NextGen Invent ranked No. 3,280 on the Inc. 5000. On June 19, 2026, Straive announced its acquisition of the company. The buyer emphasized AI engineering, life sciences and manufacturing expertise, and the ability to deploy solutions inside complex workflows.
Two relationships broaden the picture. A partnership with CMR Institute connects AI implementation with life sciences learning. In September 2026, NGI joined the American Heart Association’s health technology Innovators’ Network, opening opportunities for collaboration and access to evidence-based resources. Network membership is distinct from approval of a particular product.
For a prospective customer, the sensible opening move is modest: choose one costly delay, measure it and ask what information a person needs to resolve it. Bring the process owner into the room. A project like this needs accessible data, domain review and a budget for integration. Without those conditions, even a persuasive demonstration can leave the original queue untouched.
That is the appeal of the implant manual as a starting point. Nobody needs to be persuaded that waiting has a cost. The engineering question is wonderfully concrete: what would let the next decision happen sooner, with enough evidence for someone to trust it?