Independent technical audit
I inspect architecture, code, security, agent environments and actual practices. I separate observations from unknowns.
Deliverable
An evidence-based assessment and a prioritised remediation plan.
Find where work gets stuck, what is worth funding and which decisions your teams need to make first.
01 / The method
How the work runs
We start with a decision you need to make and a real request. I follow it with the people who handle it and cross-check their observations against the repositories, CI and available traces.
Select a level in the funnel to see an example.
Choose the decisions, systems and user journeys to examine. Agree on access and boundaries.
Example We follow a bug fix from report to release in one repository, using read-only access.
Compare repositories, documentation and operational data with how the team actually works.
Example We compare the workflow in the wiki with the tickets, pull requests and approvals the team actually uses.
Try the checklist on a sample. Agents help gather evidence. I verify findings against sources or repeatable tests and flag what remains uncertain.
Example We rerun a check on a sample change to see whether it catches the defect it is meant to block.
Connect each risk to its impact and decide which actions to take first.
Example roadmap item
Give leadership the summary and roadmap. Give the team the evidence and steps to reproduce the findings.
Example Leadership gets the roadmap. The team gets the repository, revision and steps needed to rerun the check.
What your team will be able to do on its own
Trace a finding to the right revision, rerun the documented check and verify whether the fix resolves the problem.
If the audit Brain is in scope, the team can use an assistant to query the findings and retrieve the evidence and steps to rerun the checks.
02 / Anonymised audit example
During a follow-up audit, AI instructions appeared to have disappeared after a migration. Restoring them could have become a priority.
Whether the instructions were used in practice remains to be checked.
I inspect architecture, code, security, agent environments and actual practices. I separate observations from unknowns.
Deliverable
An evidence-based assessment and a prioritised remediation plan.
I follow work from request to delivery, with the people doing it, to identify queues, rework and handover gaps.
Deliverable
A map of the bottlenecks and changes to test first.
I align business goals, use cases and constraints with leadership and teams.
Deliverable
A prioritised roadmap, decision criteria and clear ownership.
I compare subscriptions, API usage and infrastructure for your workloads. We agree how to count accepted work and which costs to include, such as retries and review time.
Deliverable
A cost model with its assumptions, investment options and a plan to track cost per accepted task.
I examine who can authorise a tool or data access, which actions require human approval and who verifies the result. We review the rules against examples of how teams actually work.
Deliverable
A map of responsibilities and approval points, with gaps to address first.
If the Brain is in scope, your team can look up the evidence behind a finding and the reasoning behind a recommendation when picking up the work.
The team gets the evidence and steps to reproduce the finding. The handover separates what we verified from what still needs checking.