AI development for products that need to understand, not just store
Most AI development projects fail in the gap between a good demo and a feature people rely on. We close that gap with evaluation sets built from your real data, prompts under version control, and cost ceilings agreed before a single token is spent in production.
What we build
AI features that map to a task someone currently does by hand — and that stay accurate as your data and prompts change.
- Conversational assistants grounded in your own documents (retrieval-augmented generation)
- Semantic search that understands a question instead of matching keywords
- Document understanding: invoices, forms, contracts and scans into structured records
- Classification, scoring and routing models for leads, tickets and applications
- Voice and image input — transcription, vision extraction, photo quality checks
- Multilingual layers for Gujarati, Hindi and English audiences
How an AI development project runs
We start with an evaluation set, not a prompt. A few hundred real examples with known correct answers let us benchmark models honestly, choose the cheapest one that clears the bar, and prove regressions have not crept in every time a prompt changes.
- Collect and label an evaluation set from your existing data
- Benchmark two or three models on cost, latency and accuracy
- Design retrieval, tool calling and fallbacks around the winner
- Ship behind a feature flag to a small group, then widen
- Hand over the prompt library, evals and runbook in your accounts
Cost, privacy and control
Business-tier API access where inputs are not used for training, the minimum data sent for each call, identifiers masked before anything leaves your systems, and a monthly spend ceiling enforced in code. For sensitive workloads we scope region-locked or on-premise options before the build starts.
Deliverables at handover.
- Working AI feature in your product, behind a flag until you approve it
- Evaluation set and scoring scripts in your repository
- Prompt library with version history
- Cost dashboard and hard monthly ceiling
- Written runbook and a recorded walkthrough
Every project also includes what is listed on the features page and follows the six-stage process. Pricing follows one of three engagement models.
AI solutions that pair with ai development.
Related work.
AI development: what people ask.
Which AI models do you use?
Whichever clears your accuracy bar at the lowest cost and latency — typically Anthropic, OpenAI or Google models, chosen per project after benchmarking on your own examples. We never default to one vendor.
How long does an AI feature take to build?
A scoped pilot answering one question — will extraction work on our documents, will the assistant answer accurately — usually takes two weeks. A production feature with retrieval, evaluation and monitoring typically takes four to eight weeks.
Will our data be used to train models?
No. We use business-tier API access where inputs are excluded from training, and we strip or mask identifiers before any data leaves your systems.
Where this service has done the most.
Often combined with.
See ai development on a real example.
A 30-minute demo on your own use case: what we would build, how long it would take with AI in the loop, and what it would cost. No deck, no obligation.