Frame the work.
A focused conversation about the current workflow, its inputs, exceptions, and the people accountable for the result.
AI engineering with context.
AI coding workflows with project context, tests and review gates.
Discuss AI-assisted engineeringWho this is for
Software teams that want to use coding agents consistently, without turning unreviewed generated code into a new operational risk.
What we do
We help engineering teams set up AI coding agents around the way their repositories actually work: explicit project context, repeatable checks, and human review before changes land. LostMind's automated quality layer is how we refer to the checking and context layer used in that work.
Useful outcomes
- Project instructions aligned with your architecture and conventions
- Repeatable checks for agent-produced changes
- A review model that keeps engineering accountability clear
What you leave with
- Repository context and agent instructions
- Test and validation workflow
- Adoption and review playbook
How an engagement runs
A bounded path to useful.
Prove the useful part.
We agree a bounded scope and test the riskiest assumption with representative material before expanding the system.
Build the operating path.
The workflow gains validation, review gates and clear ownership so it can be used and maintained beyond a demonstration.
Review and hand over.
We review behaviour against agreed scenarios, document the operating decisions, and leave your team with a clear next step.
Timing depends on access, the workflow, and the exceptions we have to handle. We will give a written estimate of scope, fee and timing before work starts. The figures in that estimate are planning numbers, not a guarantee, and they can change if the material or access changes.
Scope, fees, deliverables and timing are agreed in writing before work begins. An enquiry does not commit you to a project.
A good place to begin
Tell us the workflow.
Month-end, a stubborn PDF, or a phone that nobody can answer.
Discuss AI-assisted engineering