Custom AI Implementation
AI that works inside your business or product: knowledge systems over your documents, agents that do defined jobs, AI features in your applications, and the integrations and infrastructure to run them. When an important capability is uncertain, we prototype it first.
When you need this
- Your team spends hours finding, reading or checking information.
- You want an AI feature inside your product or internal tools.
- A repetitive process could be handled by an agent, with a person approving the key steps.
- Your data has to stay in a particular cloud, region or network.
What this includes
- Custom AI applicationsSoftware built around an AI capability your business needs.
- AI knowledge systemsSearch and answers across your documents, with sources for every answer.
- AI agent engineeringAgents that do defined jobs with approved tools and human approval.
- Private AI infrastructureRun models and data where your requirements say they must run.
How we do it
1. Define
The task, the users, the data involved and how success will be measured on real examples.
2. Prove, when needed
If the core capability is uncertain, a prototype on your own examples, such as a Prove Sprint, before the build.
3. Engineer
Integrations, permissions, evaluation, logging and deployment into your environment.
4. Operate
Monitoring, quality and cost reviews, and improvements, or a handoff to your team.
What you get
- An AI system connected to your data and tools
- Permissions, logging and human approval where needed
- An evaluation set and its results
- Deployment and operating documentation
A manufacturer connects its service manuals and past repair tickets to an assistant inside its existing support tool. Technicians get answers with the source page linked, and the assistant passes the question to a person when it cannot find support for an answer.
Start with a project conversation
Scoped to your project
Tell us what you want to build. We reply to set up a short conversation. A clear project can be scoped directly; if the solution is not yet clear, solution design comes first.
Questions
Do we need a Prove Sprint first?
Only when an important capability is uncertain. If the approach is well understood, we go from solution design straight to the build.
Which AI models do you use?
Whichever fits the task, cost and data requirements: managed APIs from the major providers, or privately hosted open models. The choice is made per project and tested on your examples.
Often combined with
- AI prototyping and Prove SprintTest the one uncertain AI capability that decides whether an idea is worth building.
- AI evaluation and Reliability AuditMeasure how well an AI system already in use works, and fix what is failing.
- Managed Hosting & Ongoing EngineeringKeep your software running and improving after launch.