What this service solves
We implement Retrieval-Augmented Generation (RAG) architectures over your company's official documentation so AI agents answer with verifiable information, cite sources and eliminate hallucinations in critical contexts. The knowledge base stays live, current and governed with role-based access control.
Problems we solve
- AI agents that make up answers on sensitive topics.
- Official documentation scattered with no single source of truth.
- The risk of sharing outdated versions with customers.
- The inability to audit where an answer came from.
What's included
- An inventory and normalization of source documents.
- An automated ingestion pipeline.
- A chunking, embeddings and reranking strategy.
- A vector database with role-based permissions.
- Implementation of the RAG agent with verifiable citations.
- Accuracy and fidelity metrics.
How we work
- Discovery of critical knowledge.
- Technical design of the pipeline.
- Implementation and initial load.
- Quality evaluation and retrieval tuning.
- Production deployment and maintenance.
How we work: service methodology
- Phase 1 · Document Audit and Ingestion Curation (Week 1) — Inventorying file formats and defining classification metadata.
- Phase 2 · Chunking and Embeddings Pipeline Architecture (Weeks 1-2) — Preprocessing, splitting by logical sections, and vectorizing the documents.
- Phase 3 · Hybrid Search and Reranking (Weeks 2-3) — Ranking the most relevant fragments to reduce noise in responses.
- Phase 4 · Interface Development with Evidence Highlighting (Weeks 3-4) — An interface showing the exact source of each answer and per-user permission controls.
- Phase 5 · Anti-Hallucination Stress Testing and Deployment (Weeks 4-5) — Testing trick questions, calibrating relevance thresholds and going live.
What we need from your team
- A folder of corporate documents to index (PDFs, manuals, policies, contracts).
- A list of frequently asked questions and the most complex query cases.
- A permissions matrix: access defined by role or department.
- A server or cloud environment to deploy the system to.
Deliverables
- A production RAG system.
- Technical pipeline documentation.
- An evaluation and accuracy dashboard.
- A permissions and versioning policy.
- An operations runbook.
Who it's for
- Companies in regulated sectors (healthcare, financial, legal).
- Organizations with living, critical documentation.
- Support and compliance teams that need citations.
- Legal, HR and operations departments.
Signs you need it
- Employees lose hours searching for the same information across scattered internal manuals and PDFs.
- You've already tried an AI assistant and worry it confidently answers with something that isn't actually documented.
- You need every answer to cite its exact source because you operate under strict traceability requirements.
- The company's critical knowledge depends on a few people and gets lost when someone leaves.
Measurable KPIs
- Retrieval accuracy (top-k accuracy).
- Rate of answers with valid citations.
- Reduction in repetitive tickets.
- Agent latency.
Expected outcome
The company has AI agents that answer with official knowledge, cite auditable sources and eliminate hallucination risk in critical flows. Documentation stops being a dead archive and becomes a living, governed conversational asset.
Frequently asked questions about this service
What does the enterprise RAG system include?
A production query platform with a web interface, verifiable citations to the source document, a document-update pipeline, and an admin panel.
How long does implementation take?
Between 3 and 5 weeks depending on document volume and the number of integrations.
What do I need to provide to get started?
The folder of documents to index, a list of frequently asked questions, the role-based permissions matrix, and access to the environment where the system will be deployed.
How is it guaranteed that the AI won't make up answers?
With hybrid search, semantic reranking, and an interface that shows the exact document fragment backing each answer; cases with no documentary support are flagged as uncertain instead of being answered blindly.
Can it be deployed on my own servers?
Yes — for companies with strict confidentiality requirements, it can be deployed with local models on your own infrastructure.
Technology stack
- LlamaIndex
- LangChain
- Pinecone
- Weaviate
- Supabase pgvector
- OpenAI
- Claude
- Cohere Rerank
- n8n