Retrieval-augmented generation
Chunking, embedding, and reranking tuned to your document structure, with citations back to source.
Generative AI
LLM Applications & RAG
Retrieval-grounded assistants and document workflows, with an evaluation suite so you can tell whether a change made things better.
Overview
The hard part of an LLM feature is not the first response, it is the hundredth. Without retrieval grounded in your own content and a test set you can run on every change, quality drifts and nobody can say why.
We build the retrieval layer, the prompt and tool scaffolding, and the evaluation harness together, so the system is measurable from the first week rather than assessed on impressions.
What you receive
- Retrieval pipeline with ingestion and refresh jobs
- Application API with streaming responses and citations
- Graded evaluation set and CI reporting
- Cost model with per-request breakdown
- Guardrail and fallback configuration
Typical stack
- Python
- TypeScript
- pgvector
- LangGraph
- Next.js
- Redis
Capabilities
What this covers in practice
Document extraction
Structured fields pulled from contracts, invoices, and statements, with confidence scores and review queues.
Evaluation harness
A graded test set that runs on every prompt or model change, so regressions surface in CI.
Cost and latency control
Caching, model routing, and token budgeting to keep unit economics predictable at volume.
Related
Also under Generative AI
Want to talk through a llm applications & rag project?
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