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AI Development

Production AI, not demos

Most LLM features die in pilot. We build AI systems that survive evaluation, latency, cost, safety review and a year of drift.

Retrieval, agents and pipelines

RAG with hybrid retrieval, structured output, tool-use agents and human-in-the-loop checkpoints. Built on OpenAI, Anthropic, open-source models or a mix.

Evaluation

Offline evals, online experiments, prompt regression suites and red-team exercises. No feature ships without measurable quality bars.

Cost & latency

Token budgeting, model routing, caching, batching and on-device inference where it makes sense.

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Frequently asked questions

Can you fine-tune models on our data?

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Yes — fine-tuning, LoRA adapters, and distillation. We start with prompt and retrieval engineering first because it is usually enough.

How do you handle hallucinations and PII?

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Structured output, retrieval grounding, PII redaction at ingest, and audit trails on every model call.

Ready to engineer what's next?

Book a free consultation with our senior engineering team. No sales theatre — just a frank technical conversation.