Retrieval architecture
Hybrid search, re-ranking, metadata filters and permission-aware retrieval so users only ever see what they may see.
All services
Build & Engineering
Generative AI & RAG Engineering
Answers your experts would sign their name to.
- +35pt
- Typical groundedness gain
- 60%
- Inference cost reduction
- 500+
- Golden test cases per system
The problem we solve
Retrieval quality, not model choice, decides whether a generative system is trusted. We engineer the whole chain — chunking, indexing, ranking, grounding, evaluation — and prove quality with numbers before anything reaches users.
What the work includes
Fine-tuning & distillation
Task-specific tuning and smaller distilled models where latency, cost or residency rule out frontier APIs.
Evaluation harness
Golden datasets, LLM-as-judge with human calibration, regression gates in CI and drift monitoring in production.
Grounding & citations
Answer-level provenance, abstention behaviour and confidence signals users can act on.
Inference optimisation
Model routing, caching, quantisation and batching tuned against a published cost per resolved task.
How the engagement runs
A sequence you can plan a quarter around.
- 01
Baseline
Corpus assessment, golden dataset build, current-state quality measurement.
- 02
Engineer
Iterative retrieval and prompt work against the evaluation suite until targets are met.
- 03
Operate
Monitoring, drift alerts and a quarterly quality and cost review.
Common questions
Before you commit.
- Do we need fine-tuning?
- Usually not first. Retrieval and prompt engineering close most of the gap; we tune when evaluation shows it is the remaining lever.
- How do you handle confidential documents?
- Permission-aware indexing, tenant isolation and residency controls agreed before ingestion begins.
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ViewTalk through generative ai & rag engineering.
A 45-minute briefing with the people who would run the work — scope, timeline and a straight answer on whether it is the right next step.