Platform
AI that works inside the process.
DebugInit embeds intelligence into documents, decisions, approvals, service work, analysis and execution—with business rules and human accountability.
Model-independent • observable • permission-aware • auditable
Capabilities
What the layer provides. Every module and customer system inherits it rather than rebuilding it.
Copilots
Assistance inside the task, bounded by the same permissions as the person using it.
Retrieval
Permission-aware search across your records and documents, returning citations rather than assertions.
Extraction
Structure pulled from the contracts, invoices and forms the process already runs on.
Classification
Routing and triage where the decision repeats often enough to be worth modelling.
Summarisation
Long threads, cases and documents reduced to what the next person needs to act.
Prediction
Demand, delay and risk signals, measured against the judgement they support.
Recommendations
Suggested next actions, presented as suggestions with the reasoning visible.
Workflow agents
Multi-step execution inside a governed workflow, stopping at declared approval points.
Model gateway
One interface in front of hosted, private and local models, chosen per task on cost, latency, geography and policy. A change of provider is a configuration decision rather than a migration project.
That is also the answer to vendor dependence: the gateway is the reason a model provider can be replaced without the workflows above it noticing.
Context and permissions
An agent sees only the data, tools and actions permitted for that user in that workflow. Permissions are not a filter applied to the answer — they are the boundary of what the agent can reach in the first place.
Human control
High-impact actions require approval, clear a confidence threshold, or pass a policy check. Which actions those are is your decision, recorded as configuration rather than as convention.
Failure handling
Fallback models, safe refusal, retries, queues, rollback and human escalation. A capability that cannot fail safely is not deployed, because the interesting question is never how it behaves when it works.
How it is measured
Defined before launch and re-measured after. These are the measures; results belong to a specific engagement.
- Accuracy
- Against the human process it supports, not against a public benchmark.
- Groundedness
- Whether the answer is supported by the source it cites.
- Task success
- Whether the work actually got finished.
- Cost
- Inference spend per unit of work, visible rather than bundled.
- Latency
- Whether it is fast enough to be used during the task.
- Escalation rate
- How often output needs a human to correct it.
What this page does not claim
No fully autonomous operation, and no guaranteed correctness. Every capability here assists a process that a person remains accountable for, and anything carrying money, risk or a commitment stops for a named human. A vendor promising otherwise is describing a demo.
Bring one workflow
The clearest way to evaluate this is against a process you already run, with its exceptions included.