Generative AI
Generative AI that is grounded in your business
A model on its own knows the internet. It does not know your products, your policies or your customers. We close that gap and build GenAI systems worth putting in front of real users.
The gap between a demo and a product
It takes an afternoon to build a generative AI demo that impresses a boardroom. It takes considerably longer to build one that behaves correctly on the thousandth query, cites where its answer came from, refuses gracefully when it does not know, and costs a predictable amount to run. The distance between those two things is the actual work.
Almost always, the deciding factor is grounding. A language model that answers from general training will be confidently wrong about your refund window, your product catalogue and your internal policy. Retrieval-augmented generation fixes this by putting your own documents and data in front of the model at answer time — and doing that well is an engineering problem involving chunking, embeddings, ranking and evaluation, not a prompt-writing exercise.
We build these systems with evaluation from day one. Before anything is exposed to users we assemble a test set of real questions with known good answers, so changes to prompts, models or retrieval can be measured rather than argued about. That is what makes it safe to improve the system later instead of freezing it out of fear.
What we build with GenAI
Enterprise copilots and assistants
Internal assistants that answer from your policies, documentation and data, with citations so staff can verify what they are told.
RAG and knowledge systems
Retrieval pipelines over your document estate — ingestion, chunking, embeddings, hybrid search and re-ranking — tuned against real questions.
Content and document generation
Drafting proposals, reports, summaries and structured documents in your format and tone, with a human review step in the loop.
Extraction and classification
Turning unstructured text — email, contracts, forms, transcripts — into clean structured data your systems can act on.
Customer-facing GenAI features
Search, recommendation, personalisation and conversational features built into your product, with the safety and latency requirements that implies.
Evaluation and guardrails
Test suites, hallucination checks, prompt-injection defences, PII handling and cost controls so quality is monitored rather than assumed.
How we work
We pick use cases where a wrong answer is recoverable, and we prove quality before scale.
Use-case selection
We assess candidate use cases on value, tolerance for error and data availability, then start with one where the model’s strengths line up with the task and mistakes are cheap.
Ground the model in your data
We build the retrieval layer over your real content and tune it until the right source appears for the right question. Retrieval quality sets the ceiling for everything downstream.
Evaluate before exposure
We build a test set from real user questions and measure accuracy, groundedness and refusal behaviour, plus latency and cost per query.
Ship with monitoring
Release to a limited group first, log real queries and failures, and feed those back into the evaluation set so quality improves with usage instead of drifting.
Good and poor fits for GenAI
Being straightforward about where this technology does not belong saves a great deal of money. These are the conditions under which GenAI reliably earns its place.
- The task involves reading, summarising or drafting language at volume
- The knowledge needed exists in documents nobody has time to search
- A human reviews the output, or a wrong answer is cheap to correct
- Inputs are unstructured and rule-based approaches have already failed
- Poor fit: arithmetic, ledgers and anything requiring exact numeric guarantees
- Poor fit: irreversible decisions with no human review and no audit trail
Frequently asked questions
Will our data be used to train someone else’s model?
Not if the deployment is set up correctly. Enterprise API tiers from the major providers contractually exclude your inputs from training, and where the requirement is stricter we deploy open-weight models inside your own cloud tenancy so data never leaves your boundary. Which route fits depends on your regulatory position, and we work that out before any data moves.
How do you deal with hallucination?
By constraining what the model is allowed to answer from. Retrieval puts your source documents in the context, prompts require citations, and the system is instructed to say it does not know rather than guess. We then measure groundedness on a test set — hallucination becomes a number you can track rather than an anecdote.
Which model should we use?
It depends on the task, and the honest answer is that this changes every few months. We benchmark shortlisted models against your own evaluation set rather than against public leaderboards, and we build the system so the model can be swapped without a rewrite when something better or cheaper appears.
What does it cost to run?
Ongoing cost is driven by query volume, how much context each answer needs and which model tier you use. We estimate this during design, and we build in controls — caching, smaller models for simple queries, context trimming — so cost scales predictably rather than surprising you in month three.
Can it work in languages other than English?
Yes. Current frontier models handle major Indian languages reasonably well, though quality varies by language and by task. Where multilingual support matters we test it explicitly on your content instead of assuming it works.
Related services
Have a GenAI idea you want assessed honestly?
Describe the use case. We will tell you whether generative AI is the right tool, what grounding it would need, and what could go wrong.