Retrieval-augmented systems and LLM features with evaluation harnesses, guardrails, and real cost control.
(The problem)
Everyone wants an AI feature, but prototypes do not survive contact with production. Accuracy is unpredictable, costs are unbounded, and nobody can prove whether the system actually works.
A prototype takes an afternoon. Production takes an evaluation suite, a retrieval pipeline that survives messy documents, guardrails that fail safely, observability across multi-step chains, and unit economics that still work at ten thousand queries a day.
We build the unglamorous infrastructure that makes AI features dependable — and we are honest when a deterministic approach would beat an LLM.
LLM features are a new attack surface. We defend against prompt injection, treat retrieved content as untrusted, redact PII where required, and keep a human in the loop for consequential actions. Failures degrade to a safe, deterministic path rather than an unchecked model response.
Naive implementations send every request to the largest model. We route by complexity, cache aggressively, deduplicate retrieval work, and stream responses so perceived latency stays low. Clients typically see cost per query fall by 60–80% after tuning — without sacrificing measured quality.
(Deliverables)
A labelled test set and automated scoring so every prompt or model change is measured against real outcomes.
Ingestion, chunking, embeddings, hybrid search, and reranking tuned against your own documents.
Input and output validation, injection defence, and safe fallbacks for when confidence is low.
Routing, caching, and model selection that keep unit economics viable at production volume.
(Outcomes)
Targets are agreed with your team at the start of an engagement. These are the levers we optimise.
(FAQ)
You cannot eliminate it, so we design for it. Answers are grounded in retrieved sources, confidence is measured, low-confidence responses fall back to a deterministic path, and every output is logged for review. Groundedness is tracked as a metric with a regression suite, not a hope.
Usually RAG. It is cheaper, updates instantly when your data changes, and cites its sources. Fine-tuning is worth considering for tone, format, or narrow classification tasks — not for injecting frequently changing knowledge.
Yes. We deploy against hosted APIs or self-hosted open-weight models depending on your data-residency, privacy, and cost requirements. The architecture is designed so the model provider is a swappable component.
(Start here)
A 30-minute call with a senior engineer. We'll give you a straight answer on scope, sequence, and whether we're the right fit.
Book a call