AI Experiences
Assistants, retrieval and AI-driven features, designed and built as product rather than bolted on as a chat bubble. The hard part is not calling a model — it is the experience around it: what it may do, how it fails, and why anyone would trust it twice.
The model is a commodity. The experience around it is the product.
AI product strategy
Where a model genuinely earns its keep in your product — and the features it would only make slower, costlier or less trustworthy.
Assistants and copilots
Conversational and in-context help with the states chat demos skip: interruption, correction, memory, and a graceful no.
Retrieval and search
Answers grounded in your own content with citations to check, not a model improvising about your business.
Agent workflows
Multi-step automation with human checkpoints where it matters. Model-agnostic, so a better or cheaper model is a config change, not a rebuild.
Evaluation and guardrails
Eval suites, output constraints and monitoring, so quality is measured rather than vibes — and regressions are caught before your users find them.
Scope
The jobs worth automating, the data that grounds them, and the failure cost of each. Honest nos included.
Prototype
A working slice on your real data within weeks, tested with the people who will live with it.
Harden
Evals, guardrails, latency and cost budgets, and the fallback for when the model is wrong or down.
Ship and measure
Deployed behind flags, measured against the baseline, and tuned on real usage rather than launch-day optimism.
- AI opportunity map
- Working prototype on your data
- Production feature or assistant
- Evaluation suite and guardrails
- Latency and cost budget
- Monitoring and handover documentation