AI forward deployed engineering that takes your prototype to production
Your AI prototype answers well in the demo and stalls when it meets your real systems. I work inside your team to take it to production, and you end up with running software, tests and evaluations your team owns.
A demo stops working when it meets real systems
A demo agent does well on the examples it was built with. Then it needs access to real systems, it has to fail safely, someone has to decide which model handles which request, and nobody can say whether a change made it better or worse.
I close that gap in your repositories, alongside your engineers, against your data. That means settling what the agent may do, which steps are ordinary code and which are model calls, and how it reaches your internal systems through MCP servers and tool integrations.
Failure gets designed in, including when every provider goes quiet
Routing sends each request to a model suited to it, and what happens when a provider fails gets decided before it happens. Agents run in sandboxes with permissions bounded to the task, and the workflow states which actions need a person and records who approved them.
My own factory shows what that design looks like. In one recorded incident with every provider silent, it backed off, made 3 retries over more than a day, then blocked the ticket with a handoff for a person to pick up.
A fixed set of real cases shows whether a change helped
Evaluation comes before tuning: a fixed set of cases drawn from your real work, checks that run on every change to a prompt, a model or a tool, and a record of what changed. Without that, a model upgrade or a prompt edit is a guess.
I run my own engineering this way
My software factory has merged 667 tickets across five projects, mostly its own development, with automated review and validation on the way through. The same habits go into what I build with your team.
Your team owns the result and can run it without me
A workflow review comes first: I look at the prototype, how your team ships and what my software factory can take on. You leave with a plan either way.
If you go ahead, the factory does the volume, I review what it produces, and you set the priorities without constant meetings.
You finish with production software, tests and evaluation suites that your team can operate and change without me. Start with a workflow review of the prototype you want in production.
Capabilities
Agents and workflows
- Agent development
- Agentic workflow architecture
- Human-in-the-loop workflows
- Workflow automation
Integration
- Model orchestration and routing
- MCP and tool integrations
- Custom agent skills
- Enterprise integrations
- Internal tools
Production
- Secure execution environments
- AI infrastructure and deployment
- Evaluation and validation systems
Stack
- Go
- Python
- Kubernetes
- Terraform
- AI agents and orchestration
- MCP
