RAG pipelines, agentic workflows, fine-tuned models, and evals — production-grade AI features that do not hallucinate their way into a production incident. We write the eval harness before the product.
Hundreds of labeled cases before a single prompt. Hallucinations get caught in CI, not by your users.
Retrieval scoped to your corpus, with citations your team can audit. No model confabulation in production.
Token and latency budgets enforced in code. A prompt change that triples cost gets caught in review.
High-stakes outputs route to a reviewer; everything else ships direct. You pick the threshold.
Week one: build the labeled eval set and the harness. The product has nothing to test yet — that is the point.
Ingestion, chunking, and embeddings scoped to your corpus. We measure recall@k before touching the model.
Prompts and tool use wired to the eval suite. Every change runs against the full set before staging.
PII redaction, output validation, cost/latency budgets, and observability. Then production, with the harness in CI.
When it earns the cost. We default to prompt + retrieval, and only fine-tune when the eval suite shows prompting cannot get there. We will show you the numbers either way.
You cannot stop them entirely — you catch them. The eval harness runs in CI on every change, and production outputs are grounded in retrieved context with citations.
No. We use provider APIs with training opt-outs, and we will document the data flow so your legal team can sign off.
SaaS dashboards, internal tools, and customer portals built with type-safe stacks and ship…
Native iOS and Android, or cross-platform with React Native — designed to feel native and …
From discovery sprints to design systems — interfaces shaped by user research, not Dribbbl…