AI Delivery Readiness Assessment
A fixed-scope assessment of how AI can work in your software delivery, and what it takes to do it safely.
Fixed price, quoted to scope
Make AI useful. Then make it dependable.
A working demo is a starting point. Production needs an agreed accuracy bar, reliable integrations and a clear response when the model gets it wrong.
Know what “working” means before you build.
We start with the workflow, its exceptions and the people accountable for it. Some steps need model judgment. Others need deterministic software. The design makes that distinction explicit.
Evaluation cases come from your records and your subject-matter experts. Accuracy, operating cost and failure behavior become acceptance criteria. Deployment proceeds through evidence-based gates, with a named human path for cases the agent cannot resolve.
Your team receives the code, evaluation harness and runbooks. The system should remain maintainable after the engagement ends.
Inspect the public demonstrationsThree ways to move forward.
Start where the evidence supports it. We can scope an assessment and an evaluation baseline together before deployment.
AI Evaluation Baseline
Know whether your AI actually works, on your data, at your accuracy bar, before it touches production.
Fixed price, quoted to scope
Agent Deployment Sprint
One workflow, one production AI agent, built on the systems you already run. We agree integration requirements with your team during scoping.
Fixed price, quoted to scope
Agent security is part of the engineering.
Tool permissions, credential boundaries, approval paths and audit records matter as much as prompts. Our published internal assessment shows how ordinary weaknesses can combine, and how re-testing exposes incomplete fixes.
Read our internal assessmentWhat needs to
work better?
Tell us about the system, the people who use it, and what is getting in their way. We’ll help define a useful first step, with a clear scope and acceptance criteria.
Discuss your project