Answers grounded in your own documents.
Document intelligence and knowledge assistants built on retrieval, not guesswork. Every extracted field and every answer traces back to the paragraph it came from.
A fluent wrong answer is worse than none.
Regulated organisations run on documents: policies, contracts, case files, procedures, correspondence. Staff spend hours finding the right clause, and the answer often depends on which version applied on which date.
General-purpose chat tools help with drafting, but they cannot be trusted with these questions. They do not know your documents, they do not know which source wins when two disagree, and they cannot show their working.
We build retrieval pipelines that understand your document estate (its structure, versions and access rules) and answer only from what they retrieve, with citations a reviewer can open.
What we build and run for you.
Document extraction
Applications, invoices, contracts and forms turned into validated fields with confidence scores and an exceptions queue.
Knowledge assistants
Question answering over policies, manuals and case history, scoped to what each user is cleared to see.
Drafting with sources
First drafts of letters, reports and responses that cite the clauses and records they rely on.
Search that works
Hybrid keyword and semantic search with reranking, tuned on real queries from your teams.
Production, not pilots. Built to run inside your operations.
Every agent we ship has an owner, an evaluation set and an audit trail before it touches a live case.
Retrieval first, generation second.
Ingest
estateDocuments are parsed with their structure, version and permissions intact, including scans and tables.
Index
hybridChunks are embedded and indexed alongside keywords and metadata, so dates and document types can filter results.
Retrieve
rerankEach question pulls candidate passages, which a reranker orders before anything is generated.
Answer
citeThe model answers only from retrieved passages and cites them; if nothing relevant is found, it says so.
- Answers restricted to retrieved sources
- Citations on every answer
- Document-level access control carried into search
- Evaluation set of real questions with agreed answers
- Refusal when evidence is missing
Where it earns its keep.
Illustrative patterns from the sectors we work in. Client details stay anonymised.
Policy and procedure assistant
Staff ask how a rule applies to a case and get the governing clause, its effective date and related precedents.
Contract review support
Clauses classified against a playbook, deviations highlighted and obligations extracted with owners and dates.
Regulatory document handling
Submissions and safety reports classified and summarised with every statement linked to its source page.
Inspection report intake
Field reports read, key findings extracted and checked against the applicable regulation before filing.
Chosen for the job, not the vendor.
Work this builds on.
Real-time event streaming feeding AI and ML pipelines across business units
A governed digital platform and partner API layer for a national education authority
Client details anonymised per delivery agreements.
Questions we get asked.
How do you handle hallucinations and grounding in regulated outputs?
Three layers. First, RAG architecture with proper chunking, embeddings, and retrieval evaluation — a measured retrieval quality score, not vibes. Second, guardrails on the output: content-safety filters, citation enforcement, schema validation. Third, human-in-the-loop on anything that can leave the building unreviewed. We design the architecture so that a regulator can trace every claim back to a source document. That trace is the deliverable, not a side-effect.
Do you do model fine-tuning or stick to RAG and prompting?
RAG and well-instrumented prompting solve the majority of enterprise problems we see. Fine-tuning is the right tool for narrow domain-language adaptation, classification at scale, or tasks where retrieval is not the bottleneck. We assess fit before recommending fine-tuning; the operating cost and evaluation overhead are not trivial and the payback is workload-specific.
Which LLM should we use?
It depends on the workload, the residency requirements, and the existing cloud relationship. We routinely deliver on OpenAI (via Azure OpenAI for enterprise residency), Anthropic Claude (often the strongest reasoning model for agentic workloads), Google Gemini, and open-weight models like Mistral or Llama for on-prem or air-gapped deployment. Model choice is an architectural decision, not a brand-loyalty exercise. We have moved clients between models mid-engagement when the data warranted it.
What would you hand to an agent first?
Bring one workflow. In 60 minutes an AI architect maps where an agent helps, what it needs to reach, and what a first working version would take.