“I very much respect the knowledge that you have. You have a lot of things, just in our few communications, that no one else has mentioned. No one else has even made me think about them.”
AI Development
AI development that survives contact with real users
Custom AI models, LLM integration, RAG pipelines, and agentic workflows: engineered for production and evaluated against your users' actual success criteria.
Why choose us for AI development?
AI features are easy to demo and hard to keep working. We build the evaluation, guardrails and plumbing that keep a model useful once real users and real data arrive.
LLM integration + orchestration
We have wired OpenAI, Anthropic and open-source models into products already in use, so the prompting architecture, streaming and error handling arrive as one piece of work rather than three surprises after launch.
Fine-tuning + custom models
Fine-tuned models on your domain data are evaluated against your success criteria rather than generic benchmarks that do not reflect your users, so the number in the report is the number your users will feel.
RAG pipelines
Chunking strategies, embedding models and reranking are tuned to your actual document corpus, because retrieval quality is decided by the documents you have rather than the ones in a demo.
Agentic workflows
Multi-step agent architectures with tool use, memory and human-in-the-loop checkpoints, shipped to production rather than left in research, with the architecture chosen against the risk profile of the actions the agent takes.
AI feature integration
The AI model is the easy part. Evaluation harnesses, guardrails, streaming, observability and the UX layer around the model are where most of the effort sits, and we scope for them from the start rather than discovering them at launch.
Eval + safety frameworks
Automated evaluation suites, guardrail layers and regression testing mean a new model version cannot quietly regress your AI features; the harness tells you before your users do.
What we build in AI development
Features where a model does the work, with the retrieval, evaluation and human sign-off around it that keep them useful in production.
RAG assistants
Assistants that answer from your own documents, tuned to the corpus you have.
- Chunking, embeddings and reranking tuned to your documents
- Citations back to the source passage in every answer
- Permissions carried through so users only see their documents
Agents and copilots
Multi-step agents that call tools and hold memory across a task.
- Tool calls with typed inputs and scoped credentials
- Human approval step where an action carries risk
- Every step traced, so a bad run can be replayed
Document extraction
Invoices, contracts, forms and reports turned into structured fields.
- Confidence score on every extracted field
- Review queue for the documents the model is unsure of
- Scanned, photographed and native PDFs handled the same way
Classification and routing
Tickets, emails, leads and content sorted and sent to the right queue.
- Evaluated against your labelled examples before going live
- Low-confidence items fall back to a human
- Labels and routing rules you can change without a retrain
Recommendation
Products, content or next actions ranked for each user from their behaviour.
- Signals from behaviour, catalogue and context combined
- Cold-start handling for new users and new items
- Measured against the baseline it replaces
AI features inside existing products
A model added to the product you already run, without a rewrite.
- Abstraction layer between your code and the model provider
- Evals that run on every prompt or model change
- Cost and latency monitoring per feature
Products we shipped in AI development
Products from our AI development work. Each result is the one its own case study reports, in its own words.
1,000+ skincare formulas resolved from a 60-second phone scan in 55+ San Pablo pharmacies, before the customer leaves the store.
2250
A.I · Custom Web App

150ms from a user’s last word to the companion’s reply, inside CBT logic and encrypted end to end; the working demo that carried the seed raise.
AI Mental Health Companion
A.I · Custom Web App

1.8K comments a month in each user's own voice; rate-limited scheduler, Stripe from day one.
LinkedIn AI Comment Generation
A.I · SaaS

Contracts drafted in 3 minutes instead of 2 days; jurisdiction-tagged clauses, handed straight to DocuSign.
AI Contract Generation Platform
SaaS · A.I
How we build AI development
Four stages, with the evaluation harness built before the production features it will judge.
Week 1 to 2: Discovery + feasibility
We map your use case against model capabilities, estimate accuracy ceilings, and build a quick end-to-end prototype on your actual data.
Week 3 to 4: Evaluation framework
Before building production features, we build the eval harness: the tests that tell you whether the AI is actually working for your users.
Week 5 to 10: Build + iterate
Model selection, fine-tuning, prompt architecture, and the integration layer. Weekly demos on real data, not synthetic benchmarks.
Week 11 to 12: Hardening + launch
Guardrails, fallback behaviour, observability dashboards, cost monitoring, and a runbook handoff so your team can operate and extend it.
- See our full process
What clients say after the build
Real client reviews, every one checkable at source.
Frequently asked questions
Straight answers about how we approach AI development: no buzzwords, no hand-waving.
OpenAI (GPT-4o, o-series), Anthropic (Claude), and open-source models via Together, Fireworks, or self-hosted. We pick the model that fits your latency, cost, accuracy, and compliance constraints, not the one trending on Twitter.

