Case studies

The work, with the numbers.

A few deployments and what they moved. The contractor build is an illustrative example with modeled figures. The lender cases are modeled on real Philippine market data, not signed announcements, and we say so on each.

Read the cases

Four deployments.

Each card names the business, the shape of what we built, and the numbers it moved. Open the contractor story for the full build, day by day.

Case 01, Contractor01
3-person HVAC crew, Phoenix
Website, AI phone agent, local SEO, and review automation.
No website, no reviews, 40% of calls missed. In seven days: a contractor site, an AI phone agent that answers 24/7, missed-call text-back, review automation, and a CRM.
Growth tier, illustrative example
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+$14,200monthly revenue added, illustrative 90-day model
8 → 47
Google reviews in 90 days
65%
of missed calls recovered
22×
return on the 90-day cost
Case 02, Collections AI02
Top-5 PH commercial lender
Recovery rate moved from 20% to 30% on defaulted accounts.
Nova Finance sat on top of the existing LMS at the missed-payment branch: early-warning scoring, risk-based routing to AI voice and SMS agents, a compliance guard on every contact, and a settlement optimizer.
~₱29.9B outstanding, modeled over 12 months
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+₱141Madditional recovery per year, modeled on the defaulted pool
4×
lift in right-party-contact
~40
Tier-1 agents reduced
< 3 mo
payback period
Case 03, Microfinance03
Mid-tier microfinance cooperative
Field visits cut in half, recovery up from 25% to 38%.
SMS-first outreach in Tagalog and Cebuano, plus a prioritized daily route so officers only drive to the accounts where an in-person visit actually changes the outcome. BSP MFI Circular 1011 logged automatically.
~₱1.2B outstanding, modeled outcome
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−50%field visits avoided, with 1.8x more accounts per officer
+₱8M
additional recovery per year
1.8×
accounts per officer
~4 mo
payback period
Case 04, BNPL operator04
BNPL operator, ~10k active loans
Time to first contact dropped from 3 days to ~4 hours.
Pre-default early warning, with AI voice and SMS handling 70% of Tier-1 outreach autonomously. Human agents only touch the accounts the AI flags as human-required. A settlement optimizer maximizes recovery times acceptance probability.
~₱180M monthly issuance, modeled outcome
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+13 ptsrecovery-rate lift, from 22% to 35% on delinquencies
−38%
collections headcount needed
−95%
time to first contact
< 2 mo
payback period

Want this modeled on your actual numbers?

Send us your portfolio size and default rate, or your average ticket and missed-call rate. We model your additional recovery, agent efficiency, and payback, then send back a one-page memo. Twenty minutes on a call gets the same thing, live.

The rest of the platform

AI Operating SystemRun your whole company on one runtime Nova AISThe platform and its live systems AIS AssistantThe ontology-aware copilot inside AIS CompareNova vs the stitched-together stack

The work, with the numbers.

Website, AI phone agent, local SEO, and review automation.

Recovery rate moved from 20% to 30% on defaulted accounts.

Field visits cut in half, recovery up from 25% to 38%.

Time to first contact dropped from 3 days to ~4 hours.

Want this modeled on your actual numbers?