DebugInit
Move from AI experiments to governed operational value.
Select high-value use cases, establish model and data controls, integrate AI into workflows and measure business outcomes.
Start with evidence, economics and operational risk—not technology hype.
When this fits
Four signals. If none of them describes your situation, this is not the engagement you need — and we would rather say so now.
Experiments that never landed
Promising pilots that never reached a workflow anyone depends on.
Governance is blocking deployment
Nobody can approve it because nobody can describe what it would touch.
The knowledge is unreachable
The answers exist in documents and history that no search can reach.
No measure of value
AI is being funded on enthusiasm because nothing was baselined.
Method
Evidence first, then economics, then a decision. Reversing that order is how expensive mistakes get made.
- 01
Select use cases
Find decisions repeated often enough, with a measurable outcome and an owner.
- 02
Establish controls
Data boundaries, provider choice, retention and the human approval points — before building.
- 03
Ground it
Connect to your records and documents with permission-aware retrieval, so answers cite sources.
- 04
Integrate into the workflow
Put it inside the task, bounded by the same permissions as the person doing it.
- 05
Evaluate
Measure against the decision it replaces, and keep measuring after launch.
Deliverables
What you hold at the end, whether or not you continue with us.
- Use-case portfolio
- Ranked by value, feasibility and risk, with the ones we advise against and why.
- Governance model
- Policy enforcement, approvals, traceability, data boundaries, review and rollback.
- Grounded capability
- Running against your own data, with citations and permission awareness.
- Evaluation harness
- How each feature is judged, and the results from before launch and after.
- Provider strategy
- Which models, on what terms, and what it takes to change them.
If the work leads you to a different vendor, it was still worth doing. That is the standard we hold it to.
Technology approach
AI runs inside the workflow, on the same platform as the work: it inherits identity, permissions, audit and approval rather than sitting beside them with its own weaker version of each.
Retrieval is permission-aware and grounded, so an answer cites what it came from and never surfaces a record the reader could not otherwise open. Provider choice is configuration, so a change of vendor — or moving inference inside a boundary — is not a migration.
Outcome measures
Baselined before the work starts and re-measured after. These are the measures; results belong to your engagement and are published only with evidence.
Decision cycle time
How long the decision takes with assistance versus without.
Accuracy against baseline
Compared to the human process it supports, not to a benchmark.
Escalation rate
How often output needs correcting before it is usable.
Coverage
Share of the relevant cases the capability actually handles.
Provider cost
Inference spend per unit of work, visible rather than bundled.
Adoption
Whether people use it when nobody is watching.
What we will not do
Where our incentive and your interest diverge, written down before you have to work it out.
- Add a chatbot to a form
- If the task does not need it, a chat window is decoration with an operating cost.
- Let an agent act unsupervised
- Anything carrying money, risk or a commitment stops for a named human.
- Lock you to one provider
- The model gateway sits in front of replaceable providers. That is the point of it.
- Ship without evaluation
- A feature with no success measure cannot be improved, defended or honestly withdrawn.
If an assessment concludes the honest recommendation is to change nothing, that is a valid result and we will say it.
Start with an assessment
Fixed scope, defined deliverables, and findings you own regardless of what you decide to do next.