Retrieval first, generation second
Answer quality is mostly a retrieval problem. We invest in chunking strategy, hybrid search and reranking before touching prompts, because a well-retrieved passage beats a clever instruction every time.
- Structure-aware chunking that respects headings, tables and code blocks
- Hybrid dense + sparse retrieval with a reranking pass
- Permission-aware retrieval so users only see what they may see
- Mandatory citations back to the source passage
Guardrails and cost control
Semantic caching, prompt compression and routing between smaller open models and frontier APIs keep per-query cost predictable. Input and output filters, plus refusal behaviour on unsupported questions, keep the assistant honest.
- Semantic cache on repeated and near-duplicate queries
- Model routing by task difficulty rather than one model for everything
- Token budgets and per-tenant spend caps
- Logged transcripts for review, with PII redaction
What you get
- Ingestion and indexing pipeline for your document sources
- Assistant API and embeddable UI
- Evaluation set with groundedness and citation scoring
- Cost and latency dashboard
How the engagement runs
3–6 weeks to a grounded assistant on a first document corpus.