SynapseFlow AI · evaluation resource
Record your current-process baseline, then enter observations from a 14-day parallel evaluation using measures published in the invoice and logistics guides.
Illustrative tracking, not a savings forecast.
This scorecard compares values you enter. It makes no guaranteed-savings or measured performance claim. A parallel evaluation does not approve, write to, or execute work in live systems.
Paired observations
Use a consistent case set and definition for each measure. The suggested units make the published criteria easier to compare; agree on them with your team before evaluating.
Did each case follow the matching rules and tolerance your team approved?
Suggested measure: Cases aligned with approved policy
Evidence to note: Expected disposition, observed disposition, and the reason for each difference.
Enter a percentage from 0 to 100.
Could a reviewer see the invoice, order, receipt, and relevant explanation together?
Suggested measure: Cases with the needed review records available
Evidence to note: Which source records were available and which additional lookups were needed.
Enter a percentage from 0 to 100.
Was the case routed to the right reviewer, with approval authority still clear?
Suggested measure: Cases routed to the right decision owner
Evidence to note: Assigned owner, escalation reason, reviewer correction, and final decision-maker.
Enter a percentage from 0 to 100.
What changed in the work required to understand and resolve the same cases?
Suggested measure: Average reviewer time per case
Evidence to note: Review time, handoffs, rework, and cases that remained unresolved.
Enter minutes per case.
Did the evaluation identify the intended exception types without losing the source context?
Suggested measure: Intended exception cases identified
Evidence to note: Case type, source event, timestamps, records consulted, and missing-data rate.
Enter a percentage from 0 to 100.
Did each case reach the right queue or decision owner with a reason that can be reviewed?
Suggested measure: Cases sent to the right queue or decision owner
Evidence to note: Severity, business impact, confidence, escalation reason, owner, and response window.
Enter a percentage from 0 to 100.
Could a reviewer see the proposed action, policy context, downstream effects, and open work together?
Suggested measure: Unresolved cases at the end of review
Evidence to note: Proposed disposition, handoffs, retries, approvals, unresolved state, and final human decision.
Enter a percentage from 0 to 100.
Were ambiguous, material, or out-of-policy cases paused for the accountable person?
Suggested measure: Cases requiring review held for a human decision
Evidence to note: Review trigger, decision-maker, correction, approval record, and cases withheld from action.
Enter a percentage from 0 to 100.
Did the review produce evidence about recurring causes and the cost of the current process?
Suggested measure: Distinct recurring causes documented
Evidence to note: Exception frequency, time to triage, time to resolve, rework, repeat causes, and unresolved cases over time.
Enter a whole number of distinct causes.
Values are illustrative observations entered by you. They stay in this browser session and are not submitted or saved. A difference is not a guaranteed saving or performance result.
Start with your workflow
Choose one recurring exception path, agree on the source records and measure definitions, and keep your existing review process in control.
SynapseFlow AI · evidence for review, with people in control.