You do not need a better model. You need a better collaboration architecture. 18 months of empirical research, 12 documented failure episodes, and a tested control stack that reduces AI error at the system level — not by prompting harder.
The failures organisations experience are not random. They follow a documented six-stage cascade — and every stage is addressable at the architecture level.
AI reconstructs rather than retrieves. Without a canonical ground truth architecture, each session diverges from the last. Outputs become inconsistent without any visible cause.
Multiple versions of the same document exist simultaneously. The AI draws from whichever is most salient — not whichever is correct. Errors compound silently.
More fluent AI output is more dangerous. Confident, well-formed responses mask errors more effectively than hesitant ones. Fluency is not a reliability signal.
Teams experiencing repeated AI failures reduce their ambition and their usage. The problem is diagnosed as a model limitation when it is a structural one. The ceiling is artificial.
Each engagement is designed to produce a structural change in how your team collaborates with AI — not a one-time fix.
A structured diagnostic of your current AI collaboration setup. Identify where your failure cascade begins, which controls are missing, and what changes will produce the highest reliability gain.
Full implementation of the Lean Collaboration Operating System across your team or organisation. Canonical document architecture, control stack, verification protocols, and training for the people who will run it.
Embedded strategic oversight for organisations running AI at scale. Regular sessions to review architecture, catch emerging failure patterns, and keep the collaboration system calibrated as your usage evolves.
Every engagement starts with understanding your specific failure pattern before recommending anything.
A 45-minute session to understand where your AI collaboration is breaking, what you've already tried, and what the failure pattern looks like. No charge, no commitment. If I can't help you, I'll tell you that directly.
Review of your current workflow structure, document architecture, and control mechanisms. Mapped against the six-stage failure cascade to identify where degradation is entering the system.
Either a written remediation plan you can execute independently, or hands-on implementation of the LC-OS protocol across your team — depending on what the situation calls for and what you want.
Measurement of the structural change. The 89% and 93% figures came from systematic tracking — the same approach applies to your deployment. You should be able to see the improvement, not just feel it.
This work is most valuable where AI is already deployed and the failure pattern is already visible.
Engineering and product teams running AI in production workflows where reliability failures are costing time, creating rework, or eroding team confidence in the tooling.
Teams deploying AI across a workforce where inconsistent outputs create compliance risk, quality variation, or require constant human correction that was never in the original design.
Document-heavy environments where AI-assisted work is producing divergent outputs, version control failures, or outputs that cannot be traced back to a reliable source of truth.
Senior leaders who need to make architecture decisions about AI deployment and want an evidence-based framework for evaluating options — before committing to a direction that is difficult to reverse.
The first step is a 45-minute diagnostic session. No charge. If your situation is one I can help with, I'll tell you what that looks like. If it isn't, I'll tell you that instead.
Response within 24 hours.