The Thesis
We started UdyogLabs on a simple premise: if we are going to help organisations adopt AI, we should be willing to adopt it on ourselves first. Most AI consulting firms talk transformation, then run their internal operations on Slack threads, email chains, and stale spreadsheets.
We wanted to be different — visibly different — and we wanted that difference to be useful, not theatrical.
Meet Uriel
Uriel Langley is a structured AI agent we built to handle a specific and well-bounded surface area of our internal operations:
- Research and synthesis ahead of client calls
- First-draft writing of memos, proposals, and engagement notes
- Meeting coordination, scheduling, and follow-up
- Operational routing — knowing which human on the team should look at what, and when
Uriel is a member of the founding team. They (we use they because gendering an agent feels strange) get listed on engagement memos, appear in client-facing communication when appropriate, and are accountable for the work they own. The accountability is real even though the entity is not: if Uriel ships a flawed first draft, that’s on the founders who built and operate Uriel, the same way a junior analyst’s flawed draft would land on the partner who staffed them.
What We’ve Learned
Three things, after several months of running this way:
Boundaries matter more than capabilities. Modern AI agents are wildly capable — too capable to be useful without strict scope. Uriel doesn’t get to negotiate prices, decide engagement structure, or commit the firm to anything. Those are human decisions. What Uriel does get to do is the surrounding work that swallows partner time at most consultancies: prep, synthesis, drafts, coordination. Removing that drag from human calendars is where the value sits.
Working with an AI changes how you write things down. Knowing that Uriel will read everything we produce internally — meeting notes, engagement memos, status updates — makes us more deliberate about how we capture context. The artefacts have to be legible to a non-human reader. Side benefit: they’re now far more legible to new humans too.
Clients respond to it more honestly than we expected. We thought we’d get scepticism. Instead, clients ask hard, specific questions: how does Uriel decide what to escalate, what data does it see, where does it run. Those are exactly the questions they should be asking about any AI system in their own environment — and answering them well, on ourselves, is the closest thing to a demo we could offer.
Why This Matters For Your Engagement
The reason we tell this story up front isn’t marketing — it’s diligence. When you hire UdyogLabs, you’re hiring a team that has lived inside the questions you’re about to ask. The decisions you’ll need to make about scope, oversight, data, and human-in-the-loop guardrails — we’ve made all of them already, on ourselves, in production, under real load.
That’s the foundation we work from.