, Services
01How we are different
We are an engineering company that is building products, not an agency with a research page.
The distinction is practical, not cosmetic. It means we deliberately limit how many engagements we run, because capacity spent on client delivery is capacity not spent on the lab. It means we will tell you when a piece of work is not interesting enough to justify that trade, and point you elsewhere.
It also means the tooling we bring is tooling we use on our own systems and, in most cases, have published. You are not paying us to learn evaluation methodology on your budget.
We do not bill by headcount, we do not resell licences, and we do not staff engagements with people you have not met.
02Capabilities
Two groups: the applied AI work that is our reason for existing, and the product and platform engineering that any serious system needs underneath it. If a request falls outside both, we will say so rather than learn on your time.
End-to-end design and delivery of LLM-based systems: retrieval, orchestration, tool use, guardrails, human review paths, and the operational plumbing that keeps them alive after launch.
Bringing language models into an existing product or process, model selection on evidence rather than benchmark headlines, prompt and context architecture, cost and latency budgets, fallback behaviour.
Building the evaluation suite for an AI system you already have: defining what correct means in your domain, assembling the hard cases, validating the graders, and wiring it into CI.
Ingestion and retrieval over documents with real structure. Chunking that respects meaning, hybrid search, permissioned retrieval, and citation-faithful generation on top.
Narrow, well-bounded agents for work that is genuinely mechanical, with explicit failure handling and an audit trail. We are sceptical of general autonomy and say so early.
Speech systems for Indian languages and code-switched speech, including the ASR tuning and post-correction work that generic vendors do not do well.
Inspection, document understanding and edge vision, sized to run on the hardware you actually have rather than the GPU you would need to buy.
Taking a product from specification to shipped software, full-stack web and mobile, with the design, testing and release discipline that lets it be maintained by someone other than us.
Internal systems, integrations and workflow tools for organisations where the hard part is the existing landscape, not the greenfield.
AWS, Azure and GCP architecture with cost modelling done before the build rather than discovered in the third invoice. Also on-premise and hybrid, which regulated clients frequently need.
Pipelines, warehousing, lineage and quality checks. Most AI programmes stall here, and it is rarely the part that was budgeted for.
CI/CD, infrastructure as code, observability and incident practice, so the system stays up without the original team watching it.
A stable senior team working inside your process on your roadmap, with a named technical lead accountable for outcomes rather than hours.
03Engagement models
You are not yet sure the problem is tractable.
A fixed-scope investigation ending in a written technical assessment: whether the thing you want is achievable with current methods, what the evaluation criteria should be, what it would cost, and what we would refuse to promise. A recommendation not to proceed is a valid and reasonably common outcome.
You know what you need and want it built properly.
We take responsibility for delivering a working system into your environment, with the evaluation harness, the documentation and the handover built into the schedule rather than promised at the end. Your engineers work alongside ours throughout, because a system nobody on your side understands is a liability we have handed you.
You need sustained senior capacity, not a project.
A small, stable, senior team working inside your development process on your roadmap. Not staff augmentation by headcount, the same people for the length of the engagement, with a technical lead accountable for outcomes rather than hours.
04Method
It is not a proprietary framework and we have not given it a name. It is just the order that works.
Before architecture, before models: what does a right answer look like, who decides, and how will we know at scale? If this cannot be answered, nothing downstream can be trusted.
The evaluation suite is written first and checked into the repository. It becomes the contract for the rest of the engagement.
The narrowest slice that puts real output in front of a real user, measured against the harness from day one.
Scope expands where the numbers support it. Where they do not, we say so in writing and propose something else.
Documentation, runbooks, and your engineers able to operate and extend the system without us. We would rather be re-hired than depended upon.
Not the sanitised brief, the part that has already failed once. That is the conversation worth having, and it is free.