AI products and agents
AI agent development, end to end: agents and agentic workflows, voice AI, retrieval-augmented generation (RAG) over your data, and the evals, integrations and monitoring that make it dependable. Built in your cloud, handed over to your team.
- Typical duration
- First production release in 6–10 weeks
- Who it’s for
- Teams making AI part of the product
- You leave with
- A production AI product your team can run, extend and trust.
What you receive
- A deployed AI product, agent or workflow inside your systems
- An evaluation set and results, so accuracy is measured rather than assumed
- Integrations, guardrails, logging and monitoring around the model
- Code, infrastructure and documentation in your accounts, with a recorded handover
How it runs
- Weeks 1–2Frame and design
Use case, data, systems, where AI helps and where it must not, and the metric we will be measured on.
- Weeks 3–8Ship
A review of working software on a real environment every week.
- LaunchOwn
Deployed in your accounts with evals, monitoring, documentation and support after launch.
Is it the right fit?
Best when
You have a validated use case, a prototype worth taking to production, or a workflow ready for automation with real consequences.
Questions from the first call
Related servicesDiscovery sprintAI agent audit
What kinds of AI agents do you build?
Agents and agentic workflows with tool use and human-in-the-loop review, voice and conversational systems, and retrieval-augmented generation (RAG) over documents, systems and databases.
How long does it take to build an AI agent?
A first production release typically takes 6–10 weeks: two weeks to frame and design, then a review of working software every week until launch.
Which models do you use?
Whichever scores best on your evaluation set within your cost and data constraints: hosted models via API, or open-weight models in your cloud when data cannot leave it. You own the choice.
Does our data train anyone’s model?
No. We use API tiers with no-training terms or self-hosted models, and we document the data flow.
What do you build on?
Typically Python or TypeScript services, LangGraph for agent orchestration, PostgreSQL with pgvector or a managed vector store, and your cloud, most often AWS.
Can our engineers take it over?
That is the goal. The code is conventional and documented, and the handover is written for them.
Start with a conversation
We reply with questions or a time for a call, then send a written scope and quote.