Add GPT-powered features to your product without building an AI team — scoped right, shipped fast, and useful on day one.
Used well, AI is a differentiator; used carelessly, it's over-engineering.
We scope AI features, not just implement them.
Practical AI integration that users actually use.
Our process
We clarify the buyer, the core workflow, and platform requirements before any design or code begins.
High-fidelity screens and flows for the core journey, optimised for the way people actually use the product.
Implementation with clean architecture, tested as we go and wired to a real backend and staging environment.
We deploy, set up analytics, review the first real usage, and plan the next cycle with you.
Technical expertise
We match the model to the use case — not everything needs the top tier, which keeps costs sane.
System prompts, few-shot examples, and output structuring that make features reliable.
pgvector storage for RAG pipelines, semantic search, and embedding-based features.
Token-by-token UI updates so AI responses feel instant.
Token tracking, caching strategies, and cost-per-user visibility from day one.
Practical answers
Almost always something that saves the user a step they already do manually. We help you find it in the scoping session.
Token budgeting, caching repeated queries, and choosing the right model tier — set up before launch.
Yes. We've implemented vector search with pgvector and semantic retrieval for document-based products.
Yes. Keys are server-side only — no client-side exposure.
Yes. Most AI features are a new API route plus a UI component — we don't need to rebuild anything.
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Tell us what you're building and we'll scope an AI feature that earns its place.
