The board wants AI. The pressure is real. But the work stalls in the same place every time. Nobody inside the company can say where AI actually pays off, prototype it, and steer the team to adopt it. So it stays a pile of experiments that never reach production.
They don’t advise from the outside and leave a deck. They sit where the work happens and build working solutions themselves. What they ship runs secured and auditable, in your cloud, on your models.
No drawn-out discovery phase that ends in a report. The engineer runs a tight weekly loop, and a working prototype is in your team’s hands inside the first week.
Sit with your functional leaders and their teams. Map the real workflows, the pain points, and the steps nobody ever wrote down.
Rank every use case by ROI with a structured framework, then decide how each one should be solved: simple automation, an AI agent, or a custom ops platform.
Present the top problems and proposed solutions to the functional leader, and agree on what to tackle first.
Build a working prototype with AI-assisted development, put it in front of the team, and iterate until it fits. The target is a working version in under a week.
Validated prototypes go into production. Meanwhile, discovery starts on the next problem with the next leader.
Nine years of shipping production software, since 2016. Built for Interakt, CashFlo, Manipal and Haptik. The same senior team is behind every embedded engineer.
A consultant advises from the outside and hands you a recommendation. An AI Transformation Engineer embeds inside your business, prototypes working solutions themselves, and stays accountable for what reaches production.
No. The engineer works with your people, advises them, and trains them to run what gets built. The goal is that your team is more capable when the engagement ends, not dependent on us.
No. It is one engagement and one accountable engineer. They prototype the solutions hands-on, and our platform takes the validated ones into production. You are not paying for a separate build team on top.
Yours. Systems run secured and auditable, on public or private cloud, with frontier or open-source models. AI without giving up control of your data.
Days for a first prototype on your highest-ROI use case, and a system in production within weeks, not a discovery phase that ends in a quarter-long report.
It starts with a 2-month trial to lower the risk of beginning. The right number depends on your operation, and the simplest next step is a short call.