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Model choices made on your task and your data, not a leaderboard. We fine-tune where it pays for itself and route the rest to the cheapest model that passes.
Model selection based on measured task performance rather than benchmarks. Fine-tunes where they earn their cost, and routing that keeps quality up while spend comes down.
We build the test before the model: labelled examples from your own work, scored on what matters to you, such as field accuracy, tone or refusal rate.
Hosted and open-weight models are run against the same suite, with quality, latency and cost per thousand calls compared side by side.
Where prompting falls short, we fine-tune on cleaned examples using LoRA adapters, which keeps training cost low and makes versions easy to swap.
A large model labels your data and a small model learns from it, giving similar quality on a narrow task at a fraction of the inference cost.
Easy requests go to a small, cheap model and hard ones escalate to a larger model, with the routing rule tested against the same eval suite.
Open-weight models served on vLLM in your own cloud region, sized for your traffic, for teams whose data policy rules out external APIs.
Fine-tuning is the last resort rather than the first step, and every candidate model is judged by the same tests before anyone spends on GPUs.
We agree what a correct output looks like, collect real examples from your systems and have your experts label a gold set that becomes the yardstick.
How custom models & fine-tuning shows up across India, the Gulf and the US. Pick one to see what changes in the process.
Processors key fields from hospital bills and discharge summaries in mixed English and Hindi into the claims system.
A fine-tuned small model extracts the fields with confidence scores, and low-confidence documents go to a processor for review.
Code, documentation and the tests that prove it — yours outright — and the limits it runs inside, agreed before anything goes live.
Each stage ends in something you can hold — a document, a demo, a passing eval, a dashboard. Nothing carries over on trust alone.
A paid two-week audit of your processes, data and systems. We come back with a ranked list of what AI should touch — and what it should not.
Audit report and ranked backlog
Model selection, retrieval design, guardrails and the integration surface. You get a written architecture with a cost model attached to it.
Architecture doc with cost model
Two-week sprints to implement agents, integrate with your systems and run internal evals. You see working software early and often.
A working demo in your environment
We run your real use cases, measure accuracy, latency and cost, and pressure-test edge cases with your team before go-live.
Evaluation report with KPIs
We help you launch, monitor and continuously improve. You get playbooks, dashboards and regular reviews to scale safely.
Live dashboards and runbooks
Still deciding?
Thirty minutes with an engineer who builds custom models & fine-tuning. No deck, no discovery form.
Talk to an engineerOften not. Many tasks reach the quality you need with a well-chosen hosted model and good prompting. We test that first and only recommend fine-tuning when it clearly beats the baseline, cuts running costs enough to repay training, or is needed to keep data in your own environment.
Most engagements combine two or three of these. Discovery is where we tell you which.
Agents that take a task, use your tools and finish it.
ExploreAnswers from your own documents, with the source cited.
ExploreProcesses that run end to end, people only on exceptions.
ExploreGuardrails, tracing and access control around every model.
ExploreA costed plan of what to build, buy or leave alone.
ExploreBring one process you think an agent could run.
We'll tell you straight whether it's worth building — and what it would cost if it is.