Model comparisons
The same practical task tested across different AI models so the differences in output quality, control, speed and review effort are easier to judge.
Example areas
Research, analysis, writing, planning and structured data work
AI field notes and knowledge
Practical AI learning, tested against real work.
We document what we learn as we compare AI models, apply them to business workflows and support teams through implementation. The aim is useful evidence, not tool hype.
What this library will cover
From model capability to useful team practice.
Each track answers a different founder question: which model fits the task, where can it improve real work, and what must be designed around it before a team can rely on it?
Real-work use cases
Clear examples of where AI can support sales, delivery, reporting, management and internal operations without removing necessary human judgement.
Example areas
Meeting follow-up, process documentation, quality checks and team reporting
Implementation lessons
What it takes to move from an interesting test to a workflow a team can use safely, consistently and with a visible business outcome.
Example areas
Data boundaries, workflow design, adoption, review points and measurement
Published knowledge
Start with the system behind the work.
The first article establishes the operating lens we use when evaluating tools and workflows. New field notes will be added when there is a real test, a useful result and enough context for someone else to apply the learning.
The field-note standard
Enough context to make your own decision.
Every practical AI note will make the conditions of the test visible. That matters because a result without its task, inputs and review criteria is difficult to trust or reproduce.
- 01
The real task
What work needed to be completed, who normally owns it and what a useful result looks like.
- 02
The test setup
The model, inputs, constraints, workflow steps and quality criteria used for the comparison.
- 03
The result
What improved, what failed, how much review was needed and where judgement remained human.
- 04
The application
How a founder or team could adapt the learning, including risks and implementation conditions.
Turn the learning into action
Read the evidence. Use the tools. Ask for help when the system gets harder.
The resource library contains free templates you can implement yourself. If the problem crosses roles, workflows and decisions, contact us for hands-on diagnosis and implementation support.