Software development services
Generative AI Development
Generative AI development is where most of the value — and most of the risk — sits right now. We build generative AI features into real products: drafting, summarization, classification, and search that understands intent. The model is the easy part; grounding it in your data with retrieval, measuring quality with evals, and keeping cost and latency sane is the work that makes it dependable.
What you get
Grounded in your data
Retrieval-augmented generation (RAG) and vector search over your own content, so output is based on your reality — not the open web's guesses.
Measured, not guessed
Evaluation suites turn 'it feels better' into a number, so you ship improvements with evidence and catch regressions before users do.
Production-grade
Streaming, caching, fallback handling, and cost controls — the plumbing that separates a generative AI feature from a weekend demo.
What we deliver
- Generative AI feature or product
- RAG pipeline and vector search over your data
- Prompt and output evaluation harness
- Cost, latency, and quality monitoring
- Integration into your existing product
How we work
- 01
Discover
We pressure-test the idea, map the users, and define the smallest thing worth building. You leave with a plan, not a proposal.
- 02
Design
Flows, prototypes, and a design system that makes the product feel real before a line of production code ships.
- 03
Build
Weekly releases in your stack. You see working software every Friday and steer with real feedback, not guesses.
- 04
Scale
We harden, instrument, and document the system — then hand off cleanly, or stay embedded. It runs without us.
Frequently asked questions
What is retrieval-augmented generation (RAG)?
RAG grounds a language model's answers in your own documents and data, retrieved at query time, instead of relying on what the model memorised. It's the single most effective way to make generative AI accurate and trustworthy for your domain.
How do you keep generative AI from making things up?
You engineer it down: ground answers in retrieved data, constrain and validate outputs, and measure hallucination rates with an eval suite so regressions get caught. Zero is unrealistic; low and monitored is achievable.
Which models do you use for generative AI?
Mostly OpenAI and Anthropic (Claude) for language, with open models where privacy or cost requires it. We choose per project on accuracy, latency, cost, and data-residency needs.
Let’s build
Have something worth building?
Tell us what you’re working on. We’ll come back within one business day with real, specific thoughts — not a sales deck.