LLM features that survive
contact with production.

Eval-driven development.

Hold-out test set before we write a single prompt. Prompts get versioned, scored, and rolled back like code.

Hallucination budgets.

Per-feature production thresholds. If citation accuracy drops below budget, the feature flags off.

Citation grounding.

Every answer links to source. No claim goes unverified. The UI shows the evidence, not just the output.

Cost monitoring.

Token spend tracked per feature, per user, per tenant. Anomalies page the on-call before the invoice does.

Both. Anthropic Claude for long-context reasoning and code. OpenAI for vision, multimodal, and certain pricing tiers. We mix and match per feature.

Rarely. Fine-tuning almost always loses to good prompts + RAG + evals in production. We'll tell you when it doesn't.

Citation grounding on every generation. Eval suites on every prompt change. Hallucination budgets per feature. If you can't afford a hallucination, we build it deterministically.

API-only with zero-retention contracts where possible. No model training on your data. GDPR-aware. SOC 2 paths available via partners.