Specialised · under AI Development
RAG System Development Services
Most RAG demos retrieve well but generate badly. We build production RAG with hybrid retrieval, evaluation harnesses, source citations, hallucination guardrails and cost-aware generation.
What we build
- Document ingestion pipeline (PDFs, web, structured)
- Embeddings + vector store (pgvector, Pinecone, Weaviate, Qdrant)
- Hybrid retrieval (vector + keyword + reranking)
- Citation-grounded generation
- Evaluation harness with golden Q&A pairs
- Hallucination guardrails
- Admin tooling for content updates
- Cost / latency / quality observability
- Per-tenant document isolation (for SaaS RAG)
- Streaming UI with citations
What you receive
- Production RAG system
- Document ingestion pipeline
- Evaluation harness
- Cost / quality dashboards
- Retainer for ongoing tuning
Why custom over off-the-shelf
Most RAGs are mostly retrieval
The retrieval step is what makes or breaks the answer. Pure vector search alone is not enough — hybrid retrieval + reranking typically lifts quality 20-30%.
Evaluator harnesses are mandatory
Without a golden Q&A set and regression evals, you can't tell when prompt or model changes broke things. We build the evaluator first.
Pricing and timeline
Price range
$30,000 – $90,000
USD, fixed-cost after written scope
Timeline
10 – 14 weeks
From kickoff to production
FAQ
pgvector or a dedicated vector DB?
For most teams, pgvector on a Postgres instance you already operate beats a separate vector DB until scale forces a split. Avoid premature operational complexity.
How do we evaluate RAG quality?
Golden Q&A datasets, retrieval-quality metrics (precision@k, recall@k), generation evaluators (LLM-as-judge with rubric, human eval where stakes are high).
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See details →Ready to scope this?
Fixed-cost proposal and delivery plan within 48 hours of a 30-minute discovery call.
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