End-to-end AI engineering

From notebook
to production system.

A small studio of engineers. We design, build, and run the whole thing — data pipeline, model, application, deployment, monitoring.

Who this is for: teams that need an ML system running in production — and someone accountable for keeping it running.

Start a Conversation Why trust a new studio? →
Where We Stand

No client case studies.
We're not going to invent any.

This studio is new. That is the first thing a stranger should know, and dressing it up would be the fastest way to lose the kind of client we want. So here is what we offer instead of a track record we haven't earned: terms you can hold us to, and work you can inspect while it happens rather than after it's sold.

What we put in writing

Every line below is a commitment, not a claim about the past. If we ever can't keep one, it comes off this page.

  • A written scope before any money moves — deliverable, price, and what "done" means, agreed in advance. If we think AI is the wrong tool for your problem, we say so at this stage and you owe us nothing.
  • Your code, your infrastructure, from day one — commits land in your repository and run in your accounts. There is nothing to extract later and no lock-in to negotiate out of.
  • Something runnable every week — not a status update. You can execute it, read the tests, and judge for yourself whether it works.
  • A pilot you can walk away from — if the first piece of work doesn't convince you, you keep everything produced and the engagement stops there.
  • Direct access, no account layer — you talk to the engineer writing the code, in a call, the same day.
Start with a pilot → How we build things →
One link, honestly labelled. When people ask what "engineered properly" means to us, we point at RLAlphaLabs — a live, public research project in Indian equity markets that we follow closely: a test suite gating every merge, a realistic transaction-cost model, walk-forward validation reported on held-out data. To be exact about it: that is a separate personal project which sits on a subdomain of this domain. It is not our product, not our client work, and not a credential we are claiming. It is an example of the standard, and nothing more.
What We Build

Five things we build.
Ask us to prove any of them.

We'll go as deep as you like on any of these — architecture, trade-offs, code on a screen. What we won't do is point at a public artifact as though one existed: this studio is new and there isn't one yet. Judge the reasoning in a call, then judge the pilot.

Decision systems, not just models

Reinforcement learning and simulation for problems where a wrong action costs real money — reward design, risk constraints, honest evaluation.

Reinforcement learning Backtesting & validation

Your pipeline breaks every third night

Ingestion, validation and storage built to run unattended: checkpointed parallel downloads, quality gates, multi-source fallback.

Pipeline engineering Data quality

The model works, the system doesn't exist yet

The application around the model: services, storage, deployment, dashboards, and the test discipline that keeps it releasable.

Full-stack Deployment & monitoring

LLM features that do one job well

Retrieval, tool use and agent workflows built around one specific task, with an evaluation set so you can tell whether a change made it better or worse.

Retrieval Tool use & agents

Models that have to run where the data is

Making a model fit and run on constrained hardware instead of shipping everything to a GPU server — smaller models, measured latency and memory budgets, honest accuracy trade-offs.

Constrained hardware Latency & memory budgets
How We Work

Four steps.
Judge the process.

We're a new studio, so we have no decade of client history to wave at. Here is exactly how an engagement runs instead.

01 Scope honestly If AI is the wrong tool, we say so.
02 Small paid pilot Fixed scope, fixed price, working software early.
03 Build in the open Weekly progress you can see and run.
04 Ship, then support Monitoring, retraining, on-call — the boring half.

What an engagement looks like

  • Pilot — fixed price, fixed scope, one clear deliverable.
  • Ongoing build — project fee or monthly retainer.
  • Written scope first — you know the cost before you commit.

No price list: scope varies too much for a number to mean anything before we've talked.

What you get either way

  • Ownership — your repo, your cloud accounts, from the first commit.
  • Direct access — you talk to the engineer doing the work.
  • No lock-in — walk away after the pilot with everything.

Engineers who have worked on machine learning in demanding production environments. New as a studio, not new to shipping ML.

Get In Touch

Start with a
conversation.

Tell us what you're building. A sentence or two is plenty — we'll reply directly, and we'll tell you honestly if it's outside what we do.

  • Where we are: India · IST (UTC+5:30)
  • Overlap: full European working day, US mornings
  • What happens next: a short call, no pitch deck

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