01Goal
- Evidence
- Workflow objective
- Operation
- Episode boundary
- Control
- Project scope
- Result
- Trainable task
Events → workflow episodes
Expertise Engine
The Expertise Engine turns structured workflow events into versioned episodes containing goal, context, observation, action, decision, evidence, correction, result and outcome.
StatusProprietary data layer
Reviewed2026-08-20
Direct answer
The Expertise Engine turns structured workflow events into versioned episodes containing goal, context, observation, action, decision, evidence, correction, result and outcome.
Evidence ledger
What enters, what changes, what remains accountable.
02Evidence
- Evidence
- Documents and observations
- Operation
- Source resolution
- Control
- Permission check
- Result
- Grounded context
03Correction
- Evidence
- Expert acceptance or rejection
- Operation
- Reason normalization
- Control
- Role validation
- Result
- Preference signal
04Outcome
- Evidence
- Observed project result
- Operation
- Decision connection
- Control
- Close-out review
- Result
- Effectiveness signal
01Normalize
Give different tools one workflow language.
Browser, document, model and project events are mapped into a consistent representation with identities, timestamps and artifact lineage.
02Construct
Build the complete learning episode.
The engine groups events around a bounded goal and connects evidence, decisions, expert correction and observed result.
03Version
Treat workflow data as a governed product.
Datasets carry version, provenance, annotation, quality status, consent boundary and export format.
Process / controlled sequence
From input to accountable outcome.
- 01Ingest approved events
- 02Validate schema and identity
- 03Resolve artifacts and context
- 04Construct episode boundaries
- 05Annotate decisions and evidence
- 06Apply quality gates
- 07Publish a dataset version
Design boundary
What the system will not pretend to be.
Events are not automatically training data
Quality, consent and review gates precede dataset publication.
Corrections remain attributable
The system records role and rationale without hiding expert authority.
Versions remain reproducible
Training can resolve the exact episode set used by a model.