RAG & Enterprise Knowledge Assistants
We build retrieval pipelines that combine BM25 keyword search with dense vectors, fuse results with Reciprocal Rank Fusion, rerank and cite sources. Chunking, metadata and permission-aware access are designed for your corpus, and relevance is measured on a golden set before launch.
Deliverables you can hold us to
- Ingestion pipeline with chunking, enrichment and incremental sync
- Hybrid retrieval (BM25 + vectors + RRF) with reranking
- Permission-aware, cited answers with feedback capture
- Relevance evaluation set and dashboards
Typical situations
- 01Assistant over policies, runbooks and product documentation
- 02Log and incident analysis over hundreds of gigabytes a day
- 03Customer-facing help centre that answers from your docs
Part of Generative AI & LLM Engineering
RAG, fine-tuning, evals and LLM apps built for production.
Explore the full offeringHow a typical engagement runs
Scoped up front, shipped weekly, measured against the metric we agreed.
Discover
A 60-minute call and a deep-dive sprint that end in a scored opportunity map and a baseline.
Design
Architecture and stack chosen with a decision matrix and a model bake-off on your data.
Build
Weekly releases into your environment with evals, guardrails and traces from sprint one.
Scale
Hardening, staged rollout, cost controls and handover so your team runs it.
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OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Mistral, DeepSeek and other open-weight models. We pick per task based on measured quality, cost and latency on your data, and we build so you can switch later.
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- Clients worldwide · English
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