RAG & augmented retrieval
Plug your LLMs into your private data: internal docs, product base, support history. For reliable, sourced answers.
An LLM alone hallucinates on your business data — it has never seen it. RAG (Retrieval-Augmented Generation) is the technique of fetching the right passages from your documents before asking the AI to answer.
Done well, it turns a generic model into a specialist on your context. Done badly, it gives plausible but wrong answers. The difference plays out in the ingestion pipeline, chunking and eval.
What we deliver
Ingestion pipeline
Connectors to your sources (Notion, Drive, SharePoint, database, helpdesk), suitable chunking, scheduled refresh.
Semantic search
Vector index (pgvector, Pinecone, Qdrant) + hybrid search (semantic + lexical) so we don't miss exact queries.
Augmented generation
Structured prompts with citations, anti-hallucination guardrails, transparent fallback when the answer isn't in sources.
Eval & maintenance
Evaluation dataset, automated quality scoring, monitoring of unanswered queries to enrich the corpus.
How we work
- 01
Corpus framing
Which documents, what volume, what freshness, what access rights. RAG quality depends first on corpus quality.
- 02
Ingestion pipeline
Extraction, cleaning, chunking, embeddings, indexing. We process badly scanned PDFs and Excel tables without crying.
- 03
Generation + eval
Prompt engineering, mandatory citation instructions, eval dataset with real user questions.
- 04
Production & iteration
Deployment, monitoring of unanswered queries, continuous corpus and eval enrichment.
Use cases
Internal doc assistant
Your team queries all onboarding, HR, process docs in natural language — with verifiable citations.
Augmented product support
Your support agents get sourced answer suggestions from KB, code, past tickets.
Sales research
Your reps query product sheets, pricing, technical FAQs without having to memorise everything.
Stack & tools
- Claude / GPT-4o
- pgvector
- Pinecone / Qdrant
- LlamaIndex
- Cohere Rerank
- Unstructured
- Voyage AI