Institutional memory
Bring policies, operating procedures, and past decisions into the same conversation, so knowledge remains useful when the people involved change.
Applied AI / Private knowledge
Your knowledge. Put to work.
The answer often exists already. It is spread across documents, systems, and the experience of your team. We build RAG systems that retrieve the relevant evidence and bring it into the work, with its sources and review controls intact.
Euphemios / Knowledge systemsIllustrative workflow
01 / Approved records
Access checked at the source
02 / Relevant evidence
What guidance applies here?
03 / Cited answer
A response grounded in the relevant passages, with the source beside each claim.
Source passage · Evidence attached
Operating policiesResearch & precedentTeam documentation
Illustrative workflow
01 / The system
From documents to a dependable workflow.
A RAG system connects retrieval to generation: it finds relevant passages from your records, then uses them to prepare a response. We design the surrounding pipeline with the same care as the answer itself.
- 01
Start with the source.
We map the documents, systems, and people behind the workflow. Ingestion handles extraction, cleaning, document structure, chunking, and metadata so the system can distinguish a current policy from an old draft.
Ingestion / Document quality / Provenance
- 02
Find the evidence that matters.
Keyword and vector search serve different purposes. We combine retrieval methods where useful, then rerank the candidate passages against the question. The aim is relevant context, rather than simply more context.
Hybrid retrieval / Reranking / Context
- 03
Keep the answer connected.
Responses carry references back to the passages used to prepare them. Your team can inspect the underlying document and judge whether the evidence supports the answer. Missing or conflicting evidence is surfaced for review.
Citations / Source passages / Exceptions
- 04
Respect the boundary.
Access is part of retrieval, rather than a filter applied after an answer is written. We scope source permissions, approved model providers, retention, and deployment around the data boundary agreed with your team.
Permissions / Data boundaries / Retention
- 05
Test against the actual work.
Representative questions and reviewed answers establish a baseline before launch. We evaluate retrieval and answer quality separately, monitor failures, and use reviewer feedback to improve the pipeline as documents and workflows change.
Evaluation / Monitoring / Feedback
02 / In your business
Built around the question your team needs to answer.
We begin with a specific operating need, the people making the decision, and the evidence they should be able to inspect. The model and retrieval method follow from that brief.
Research & document review
Work across a defined collection of reports, agreements, or research with references beside the response and unresolved questions made visible.
Day-to-day operations
Put the relevant guidance inside the workflow where a team is preparing a response, reviewing a case, or deciding what should happen next.
Evidence informs the decision.Your team retains the judgment.
03 / A few practical answers
Before we build.
What does RAG mean in practice?
Retrieval-augmented generation gives a language model relevant excerpts from a defined set of documents before it prepares a response. A useful implementation connects that retrieval step to the business workflow, with source references, access controls, and review. It does not remove the need to verify the answer.
What if our documents are inconsistent or difficult to read?
That is a discovery and ingestion problem first. We assess extraction quality, duplicates, document versions, and missing context before choosing the retrieval method. The initial proof uses representative documents so poor inputs are visible before a wider rollout.
What happens when the evidence does not support an answer?
We design the workflow to identify insufficient or conflicting evidence and route the question for human review. The system should make uncertainty visible. Its handling of unsupported questions is part of the evaluation set, rather than an assumption that a prompt alone will solve it.
How do you approach confidential information?
We agree the data boundary, user permissions, approved providers, and retention requirements before implementation. Deployment and integrations follow those requirements. You do not need to send confidential documents to begin a conversation about the workflow.
Where does an engagement begin, and what is handed over?
We begin with one workflow and a scoped proof on representative material. The production scope defines the integrations, controls, evaluation process, operating documentation, and handover. Ongoing support and quality checks are agreed as part of the engagement.
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