Stop being the approval queue.
When every exception needs you, governed evaluation keeps decisions moving without losing control.
Pain snapshot
- Approvals stall when one person is the default escalation path.
- Revenue and customer responses slow while teams wait for founder decisions.
- Different managers make different calls on similar exceptions.
- Urgent deals and support cases pile up at the same bottleneck.
- Growth gets capped by founder bandwidth instead of team capacity.
Why typical AI approaches fail here
Promise: Find the policy fast.
Where it breaks
- Finding policy text does not enforce approval criteria.
- Interpretation shifts across managers and business pressure.
- High-stakes exceptions still return to the founder.
Example: A pricing exception cites the right policy but still gets approved inconsistently by different leads.
Promise: Answer exception requests instantly.
Where it breaks
- Risk posture varies across sessions and prompts.
- Tone and strictness drift when cases become ambiguous.
- No stable basis for repeatable founder-level judgment.
Example: One renewal discount is rejected in the morning and approved later with only wording changes.
Promise: Route approvals faster.
Where it breaks
- Routing speed does not decide edge-case outcomes.
- Complex requests still escalate without better judgment.
- Limited reasoning trace slows review and policy updates.
Example: The system forwards every non-standard request quickly, but the founder still clears the final decision.
Faster answers ≠ aligned decisions.
What changes with governed evaluation (IAYS)
Evaluation boundaries are defined before the model answers, so teams apply the same standards every time.
Only defined unknowns escalate, reducing noise while preserving oversight on genuine risk cases.
Decisions are linked to explicit rule sets, making reviews faster and policy updates easier to manage.
IAYS transforms probabilistic output into structured evaluation.
Pilot approach
One workflow, one agent, four implementation phases.
Target outcomes (illustrative)
Results vary based on workflow maturity.
Baseline: 24h Pilot: 2h
Baseline: 45 Pilot: 12
Baseline: 62% Pilot: 95%
- Phase 1
Select workflow + capture edge cases
Define one workflow to improve and map the edge cases that currently create delays.
- Phase 2
Structure decision criteria
Turn policy and approval logic into clear governed criteria the agent can evaluate.
- Phase 3
Shadow-mode testing
Ship an agent in shadow mode and compare outcomes against current team decisions.
- Phase 4
Go-live with monitoring
Go-live with override controls, escalation visibility, and ongoing monitoring.