Product & Technology Delivery
Solve It Once
How a recurring client problem became empwr.ai
- Client
- Envorso — internal innovation, since spun out
- Sector
- Consumer Technology
- Headline result
- 100+
The challenge
Across engagements with automotive OEMs, Tier 1s, industrial manufacturers, consumer goods companies and silicon providers, we kept finding the same failure. The knowledge needed to run a large program existed, but only in fragments — in meetings nobody captured, in tools that did not talk to each other, and in the heads of whoever happened to be in the room. Consultants could compensate by hand for the length of an engagement. Nobody could sustain it afterwards. Diagnosing the same structural failure at a sixth Fortune 100 client makes the conclusion hard to avoid: this was not a client problem to be solved repeatedly. It was a product problem that had not been solved once.
What we did
- Started it as an internal innovation project inside the consultancy, using live client programs as the design input rather than a market hypothesis.
- Staffed it from Envorso’s own bench — engineering and product leaders who had built platforms at Meta, Microsoft, Atlassian, and Xbox.
- Built an AI-native program assistant that listens across meetings, workflows, and engineering systems, turning unstructured conversation into structured outcomes: summaries, decisions, action items, risks, issues, and open questions.
- Connected it to the systems programs actually run on — Jira, Confluence, GitHub, Linear, Slack, Microsoft Teams, Asana, Notion, SharePoint, and calendars.
- Put it into beta with external companies in April 2024, well outside the founding client base.
- Spun it out as an independent company in September 2024, once it had momentum of its own.
“We co-created empwr.ai to solve exactly that. It’s not just a dashboard; it’s a knowledge layer that helps our clients deliver faster, align teams, and sustain value.”
The practice behind this
Product & Technology Delivery
End-to-end delivery from concept to launch — the product operating model, cross-functional teams, CI/CD, Agile enablement, and a toolchain configured to carry it.
Read about this practiceWhere AI fits here
Includes AI in the development toolchain and in the product itself, and the agentic-first way of working that the teams above are built for.
Related
Similar problems, other organizations.
Automotive
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Delivery dates were missed often enough that customer confidence had become a commercial problem rather than an engineering one. Defects were surfacing in late-stage testing, contractual penalties were being paid annually, and the organization was competing for a major new contract it was not, on its record, credible to win.
A major automotive engineering organizationAutomotive
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Digital parts content ran on a legacy system that was inefficient, error-prone, and — critically — could not produce an audit trail. That made compliance expensive, scaling impossible, and supplier billing effectively unverifiable.
A global OEMEnergy
Power to the People
Mapping EV charging clarity
EV drivers could not reliably find a working charger. Station maps were incomplete or out of date, live availability was not exposed anywhere consistent, and there was no route for a driver to report a broken charger so that the next driver benefited.
An innovation initiative — see engagement noteTwo hours to find out whether we recognise your problem.
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