Generative AI in the product
Generative features users touch: versioned prompts, UX, and cost limits.
Generative AI in product
We design and build generative AI applications — assistants, agents, RAG, and generation features — with evaluation, security, and production rollout.
This page covers generative AI application development in product. If you already have a fragile demo, the right path may be our AI Stabilization Sprint.
Generative features users touch: versioned prompts, UX, and cost limits.
Workflows combining rules, APIs, and generative AI where human judgment does not scale.
Context retrieval from documents or internal systems with citations and access control.
Offline/online evaluation, logging, rate limits, and risk review.
We define value, available data, tolerable failures, and success criteria.
We build a measurable slice — not a slideware demo.
Observability, security, and deployment with rollback and monitoring.
AI Stabilization fixes fragile demos. This landing covers building generative AI applications where they are not yet — or expanding them with product rigor.
No — our scope is generative AI application development (LLMs, agents, RAG, and product integration). We do not train or operate classic ML models.
Yes — we integrate generative model APIs based on cost, latency, and data requirements.
You likely want the AI Stabilization Sprint: audit, refactor, and stable deploy on a fixed timeline.
Yes, often combined with deterministic automation. See also our automation landing.
Tell us the use case. We propose implementation, stabilization, or a scoped MVP.
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Tell us what you need to build or stabilize and where things stand today.