Standing facts
- 08 engines · one evidence model
- 02 arenas · francophone Europe / Africa
- Entry from 2 minutes · free, no account needed
- Every engagement ends in a verdict , never a slide deck
- Fees anchored to value at stake · never consultant days
- Every instrument published blank · 33 rows in the public register
Adoption is not value. Prove it before you scale.
Which workflows should be augmented, automated or left unchanged?
Activity is mistaken for value.
- 01
AI adoption metrics do not prove enterprise value.
- 02
Speed gains can conceal quality failures, rework, risk or weak adoption.
- 03
Executives need controlled experiments that separate activity from verified value.
STREDGELAB response: prove workflow value before you scale it.
From baseline to a scale-or-stop verdict.
- 1Baseline
- 2Pre-register experiment
- 3Assign cohorts
- 4Observe outcomes
- 5Scale or stopNEXT
Only proven workflows pass the verdict gate.
What this engine has passed.
- Internal gates passed
- 37/37
- Assurance rung
- L3 candidate
A gate is one internal technical check the engine must pass before its output is treated as signed: a reconciliation, a boundary condition, or a determinism-and-replay test. The count is gates passed of gates defined. Independent certification is pending across all engines.
An assistant that saves time, tested before it is scaled.
A support function wants to roll an AI assistant out to 180 agents. The studio pre-registers the outcome measure, runs a treated and a control cohort, and prices the difference.
| Baseline handling time | 14.0 min |
|---|---|
| Control cohort, after | 13.8 min |
| Treated cohort, after | 11.2 min |
| Effect attributable to the assistant13.8 − 11.2; the 0.2 min drift is not claimed | 2.6 min |
| Contacts per year | 240,000 |
| Hours released | 10,400 |
| Loaded hourly cost | €38.00 |
| Gross value of time released | €395,200 |
| Licence, integration and supervision | −€260,000 |
| Net, before redeployment | €135,200 |
The workflow passes, on one condition stated in the output: released hours only become value if they are redeployed. Unredeployed, the €395,200 is capacity, not cash, and the verdict reverts to no.
Deterministic engines. Governed outputs.
- Experiment validity engine
- Quality-adjusted productivity engine
- Risk-adjusted value engine
- Treatment heterogeneity engine
- Verdict engine
The evidence resolves to an outcome.
L3 candidate · 37/37 internal gates passed · independent certification pending
AI may explain, summarise, translate and draft from signed engine outputs. It cannot calculate authoritative values, select decision states, approve actions or alter audit records.
One decision. One verdict.
Bring us the decision. We build the evidence, and end on a verdict you can defend.
- ✓Scale proven workflows
- ✓Redesign weak interventions
- ✓Constrain by segment or risk class
- ✓Stop value-destroying automation
The questions buyers actually ask.
Why a control cohort?
Because handling time moves for reasons that have nothing to do with the assistant. Without a control you cannot separate the tool from seasonality, staffing or a process change, and the saving you report will not survive scrutiny.
What does pre-registration change?
The outcome measure, the cohorts and the decision threshold are fixed before the data arrives. That is what stops a disappointing result being re-cut until it looks like a success.
What may the AI itself decide?
Nothing that is authoritative. It may explain, summarise, translate and draft from signed engine outputs. It cannot calculate authoritative values, select a decision state, approve an action or alter an audit record.
Evidence for your next high-stakes decision, starting with nine gates.
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