AI & Machine Learning
We ship production AI: RAG copilots, computer vision, forecasting, and workflow automation with measurable ROI.
The gap in AI projects is rarely the model. It is that the demo worked on ten hand-picked examples and nobody built the harness to know whether it works on ten thousand. We treat evaluation as part of the build, because without it you cannot tell an improvement from a regression. As an India-based team, we run AI engagements remotely with daily overlap for UK and European clients.
What you get
- LLM copilots grounded in your private data (RAG) — retrieval, chunking, and ranking tuned against your corpus rather than a tutorial default.
- Vision, NLP, and forecasting models, chosen against a baseline. Sometimes the honest answer is that a well-tuned classical model beats a neural one on your data volume, and we will say so.
- Voice agents and workflow automation wired into the systems where the work actually happens.
- Evaluation, guardrails, and observability — an eval set, regression testing on prompt and model changes, and logging that lets you find out why a given answer happened.
How we work on AI
We start by asking what decision the system is supposed to improve and what happens when it is wrong. Those two answers set everything else: whether a human stays in the loop, how much latency is acceptable, and how confident the output has to be before it is shown at all.
Then we build the evaluation harness — before the feature. A representative set, scored, with a baseline. Without it, every subsequent change is guesswork and "it seems better" becomes the only available metric.
Retrieval quality gets more attention than prompt wording, because that is where most RAG systems actually fail. Chunking strategy, embedding choice, hybrid search, and reranking each get measured against the eval set rather than assumed.
Guardrails are explicit: what the system refuses, what it escalates, what it logs. Cost and latency are tracked per request from the first week, since both tend to be discovered too late — usually in the first month's invoice.
Model choice is deliberately reversible. We keep the provider behind an interface so that switching models is a configuration change, not a rewrite.
Stack we work in
OpenAI, Anthropic, LangChain, PyTorch, Pinecone. Evaluation and observability tooling alongside, because a model in production without either is not finished.
Who this is for
Companies with a document corpus or support load where retrieval genuinely helps. Teams that ran a promising AI pilot and cannot get it to production quality. Businesses with a repetitive judgement-heavy workflow worth automating.
What you receive
The application code, the evaluation set and harness, prompt and model configuration in version control, cost and latency baselines, and documentation of what the system refuses to do.
Frequently asked questions
Will our data be used to train someone else's model?
Not in an architecture we would recommend. Data handling, retention, and provider terms are decided explicitly at the start and written down.
How do you know the AI is accurate enough to ship?
An evaluation set built from your real cases, scored against a baseline, with regression testing on every prompt or model change. "It seems better in the demo" is not a release criterion.
What does it cost to run?
Token and inference cost is tracked per request from the first week and modelled against expected volume before launch, so the running cost is a decision rather than a surprise.
Can you work with our existing data infrastructure?
Yes — that is usually the cheaper path. A rewrite of your data layer is rarely what an AI project actually needs.