Runbook play: General intake— Nova recommends Review & approve
judgment: hard gate
Swimming PoolsRoof ClassElectrical SystemsPlumbingFire Protection & Defensible Space
broker tier-2judgment: hard gate51 provided · 12 service-filled · 0 to ask
playbook:
🔒 Approval locked16 items still to resolve — 4 PlanDesign fields · 12 open items. Fill each with a source + reasoning (Nova learns from every fix) to unlock approval.
Property Address
412 Maple Ct
City
Denver
State
CO
Coverage A (Dwelling)
480000
Year Built
2002
Nova AI underwriting copilotProvisions — decide, and teach me as you go
Every provision is a decision I learn from. Approve, change the value, waive, or request it from the customer — with the source and your reasoning. I capture each one to my vector memory attributed to you (a full audit trail), and I use it to make a sharper per-provision recommendation next time.
5 clean · folded
Decision-tree outcomesthe DB rule engine’s per-point verdicts (clear / refer / decline)5 points · all clear
UW Decisions · 5 clear · 0 need attention · from decision_rules (DB)
All decision points clear — nothing risky to review. Show all to see them.
Item-level approvals & request-info12 open · 0 approved · 0 unselected — bulk approve or compose the applicant ask12 items · 1 to ask
Insured has a primary policy with Zen Insurance97%rule-safe
UW AI Agent
has_primary_policy_with_carrier = No
Source text
“prefill (internal)”
Replacement Cost (Dwelling)97%reasoned
UW AI Agent
replacement_cost = 472000
Source text
“prefill (assessor)”
Road Access to Home97%rule-safe
UW AI Agent
road_access = Narrow paved
Source text
“prefill (hazard)”
ISO Protection Class (PPC)97%rule-safe
UW AI Agent
protection_class = 4
Source text
“prefill (hazard)”
Min Distance to Neighboring Home (ft)92%vision-input
UW AI Agent
min_distance_to_neighbor_ft = 130
Source text
“prefill (aerial)”
Slope Angle (degrees)97%rule-safe
UW AI Agent
slope_angle_deg = 15
Source text
“prefill (elevation)”
Roof Fire Classification97%reasoned
UW AI Agent
roof_classification = Class A
Source text
“prefill (catmodel)”
P(F) - Probability of Failure (fire simulation)97%reasoned
UW AI Agent
p_f = 0.710
Source text
“prefill (catmodel)”
Siding Fire Classification97%reasoned
UW AI Agent
siding_classification = Combustible
Source text
“prefill (catmodel)”
Vegetation Clearance / Defensible Space92%vision-input
UW AI Agent
vegetation_clearance = 100+ ft
Source text
“prefill (aerial)”
Property currently listed for sale92%rule-safe
UW AI Agent
listed_for_sale = Yes
Source text
“prefill (mls)”
Fire Department Response Time97%rule-safe
UW AI Agent
fire_dept_response_time = 5
Source text
“prefill (hazard)”
Act on this lead
Nova pre-wrote a question for each missing item. For each ask, tell Nova why you need it — it learns what gets requested (and where some of it could be pulled instead of emailing the customer).
1 selected · a reason is required for each — Nova learns every ask
Auto-filled PlanDesign formthe full binding form Nova pre-populated — item-approved fields are already confirmed; edit only to correct (edits still teach Nova)9 sections · 4 missing
Auto-filled PlanDesignThe customer submitted this instance; you are editing it. Each section folds — open one, edit a field, and every change is captured with a source + your reasoning, attributed to you and versioned (audit).
Protection
Opening Protectionmissing
Road Access to Homesystem-filledverifyNarrow paved
Wildfire
Siding Fire Classificationsystem-filledverifyCombustible
Vegetation Clearance / Defensible Spacesystem-filledverify100+ ft
Risk tree — first iterationour initial risk-appetite design, kept to show the iteration — superseded by provision-level approvals aligned with the runbook (▦)9 categories · all clear
Risk hierarchy
Locationclear
Accountclear
Insuredclear
Primary Coveragesclear
Propertyclear
Constructionclear
Plumbingclear
Protectionclear
Wildfireclear