Language models are easy. Making them trustworthy is not.
Retrieval-augmented generation, assistants and document automation built on your own data — with evaluation, citations and guardrails in the estimate rather than bolted on once the pilot has impressed somebody.
Overview
A demo that answers a handful of questions well is not a system
The gap between an impressive prototype and something a regulated team will rely on is almost entirely unglamorous: chunking that respects document structure, retrieval you can inspect, citations a user can click, an evaluation set that catches regressions, and a refusal path for questions the corpus cannot answer.
So we build the evaluation harness before the feature. If we cannot measure whether an answer is grounded, we will not ship it. We will also tell you when a good search index or a rewritten FAQ would do the same job for a fraction of the cost.
Scope of work
What the service covers, in this order
Document ingestion, structure-aware chunking, embeddings and retrieval over your own corpus, with a citation on every answer.
Assistants, drafting tools and internal copilots, with prompt versioning, cost controls and audit logs.
Extraction, classification and summarisation for invoices, contracts, claims and onboarding packs.
Golden datasets, groundedness scoring, PII redaction, refusal behaviour and a human review queue.
Outcomes
What separates a shipped system from a stalled pilot
Every response cites the passage it came from. Users click through and check, which is what earns trust once the novelty has worn off.
A golden evaluation set runs in the pipeline, so a prompt or model change that degrades accuracy fails before release rather than after it.
Token budgets, caching and model routing per use case, so you know the monthly figure before you commit rather than after the first invoice.
A question outside the corpus gets an honest "not in the documents" rather than a confident invention.
Why AI pilots impress everyone and ship nothing
Every item in the left-hand column has been demonstrated to us as finished work. The right-hand column is what it takes to put the same thing in front of real users and leave it there.
Answers that look plausible in a live demo
Groundedness scored against a golden set of real questions
Documents pasted into a single prompt
Structure-aware chunking, hybrid retrieval and reranking
One model, chosen because it was in the news
Model routing per task, with cost and latency measured
Confident answers to questions outside the corpus
An explicit refusal path, plus a human review queue for edge cases
Where your data goes, written down before we start
Enterprise endpoints with training disabled, confirmed contractually with each provider.
Detected and stripped before retrieval, and before any request leaves your boundary.
Inference and storage in the region your obligations require, including logs and embeddings.
Every request, retrieved passage and response logged and queryable for review.
Which of these apply to you is written into the engagement before work begins, not decided once the system is running.
Delivery process
Evaluation harness first, feature second
Use-case triage, corpus audit, and an honest answer on whether this needs a model at all.
Retrieval architecture, an evaluation set built with your own subject experts, and guardrail and data boundary design.
Ingestion pipeline, retrieval, interface and citations, scored against the evaluation set every sprint.
A human review period with real users, accuracy reporting, then a controlled release team by team.
Monitoring for drift and cost, corpus refresh, and re-evaluation each quarter as models change underneath you.
Tools and platforms
Provider-neutral, and honest about when you need none of it
A significant share of the AI enquiries we take turn out to need a good search index or a rewritten FAQ instead, and we say so before quoting rather than after. Where the work is genuinely suited to a model, the choice of model follows the data boundary and the task, not the news cycle.
We avoid: building retrieval where search would answer the question
See the full technology page →In practice
A pilot that answers the real question
The paid pilot named in the hero produces an evaluation set built with your experts, a scored prototype, and a written recommendation — including the recommendation not to proceed. That is honest, and for a buyer who has been burned by a pilot that shipped nothing, it is more persuasive than a case study written backwards from the metrics.
pilot
No. We use enterprise endpoints with training disabled, confirmed contractually with each provider. Where the data cannot leave your boundary at all, we deploy open-weight models inside your own environment instead. Which of the two applies to you is written into the engagement before any work starts.
Retrieval over your documents rather than the model's own recall, a citation on every answer so a user can check it, and an explicit refusal path for questions the corpus cannot support. Behind that, a golden evaluation set built with your subject experts runs in the pipeline, so a change that degrades accuracy fails before release. No system of this kind is perfect, which is why the ability to verify an answer matters more than the claimed accuracy of one.
We set token budgets, cache what repeats and route each task to the cheapest model that passes evaluation for it, so the monthly figure is known before you commit. Running cost is quoted alongside build cost, because a system that is affordable to build and expensive to operate is a problem you inherit rather than one we solved.
Often not, and discovery is where that gets answered. If a search index, a better-organised document store or a rewritten FAQ would do the job, we will say so and price that instead. It is a smaller engagement for us and the right answer for you, and finding out costs a discovery phase rather than a failed pilot.
Find out whether this is retrieval, automation, or neither
Bring three questions your team wastes time answering. One conversation is usually enough to tell you which of the three you are dealing with, and we would rather tell you it is the third than sell you a pilot that impresses everyone and ships nothing.