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woodfrog
Industries · Insurance

Faster claims. Less leakage. Better risk.

For insurers, TPAs and brokers. We connect policy, claims and partner data so claims settle sooner and every rupee paid out is justified.

Contact usAt least 3× return within 12 months.

Where a typical claim waits

Illustrative
IntimationDocumentsSurveyApprovalsSettled

Most of a claim's time is spent waiting for documents and sign-offs. We show each wait, claim by claim.

The numbers we move
  • Claims TATDays from intimation to settlement
  • Loss ratioClaims paid against premium
  • LeakagePaid that should not have been
  • Fraud hit rateFlags that turn out real
  • PersistencyPolicies still in force
  • Renewal ratePolicies renewed on time
  • Underwriting TATHours to quote
  • Cost per claimAll in, to settle one claim
Problems we fix

Five problems every insurance head knows.

Cost figures are illustrative, not woodfrog results, unless linked to a case.

Hands holding a pen over an insurance form on a clipboard, with a laptop beside it
What it costs
Dayslost chasing documents and approvals
Complaintsfrom customers who wait
01Claims TAT

Claims take too long to settle

Documents arrive by email and WhatsApp. Each claim waits in a queue nobody can see end to end.

What we do
AIDocuments read and checked as they arrive
ANALYTICSEvery claim's wait visible, stage by stage
AISimple claims routed for fast approval
DATAClaims system · Email · TPA portals
Several hands pointing at an insurance form on a clipboard on a wooden table
What it costs
Rupeespaid on claims that should be lower
Flagsso noisy the team ignores them
02Leakage · Fraud

Fraud and leakage in claims paid

Rules catch the obvious cases. Patterns across hospitals, garages and agents are hard to see one claim at a time.

What we do
AIUnusual claims scored, with the reason shown
ANALYTICSPatterns across providers, agents and regions
ANALYTICSLeakage tracked against the loss ratio
DATAClaims · Provider data · Policy admin
A pair of glasses and pens resting on printed policy forms beside a laptop
What it costs
Hoursto quote a simple risk
Pricingthat ignores your own claims history
03Underwriting

Underwriting slow and blunt

Proposal forms are keyed by hand. Your own claims data rarely feeds back into pricing.

What we do
AIProposal documents read into structured fields
ANALYTICSRisk views built from your own claims history
ANALYTICSQuote time by product and channel
DATAPolicy admin · Proposal forms · Rating engine
Printed policy forms and two pens on a desk next to a laptop keyboard
What it costs
Premiumlost when a policy quietly lapses
Laterenewal calls, after the due date
04Persistency

Lapses and missed renewals

Warning signs sit in payments, service calls and agent notes. Nobody reads them together in time.

What we do
ANALYTICSAt-risk policies, with an owner and a date
AISignals read across payments and service
ANALYTICSPersistency by agent, product and region
DATAPolicy admin · CRM · PaymentsCase: Churn signals read together, $480k kept →
Printed policy form with two black pens and a spiral notebook on a table
What it costs
Daysto build each regulatory return
Twonumbers for the same measure, in two teams
05Reporting

Regulatory and board reporting

Returns are built in spreadsheets from several systems. Figures differ between finance, actuarial and operations.

What we do
DATAOne definition for every reported measure
DATALineage from the return back to each policy
AIAnswers that trace to the source record
DATAPolicy admin · Claims · FinanceCase: One agreed number per measure →
Done before

Built for regulated, high-volume work.

All case studies →
Systems we connect

From the proposal to the payout.

Core
Policy admin, claims, rating engines
Partners
TPAs, hospitals, garages, agents
Customer
CRM, call centre, WhatsApp, email
Finance
SAP, Oracle, Excel

Read only to start. Nothing changes in your core systems until you approve it.

Policy · Claims · CRMYour book
Antvia DataOne governed store
TAT · Leakage · RenewalsLive views
Printed policy form with two black pens on a table
Who you work with

Engineers who build for regulated work.

Ananta PatilSamruddhi MoreMandar JoshiParth ManhasPranjul KalsiNarendra P

A small team from Pune, India. The people who scope your problem are the people who build it.

Meet the team →
Where we have built
Regulated AITrace to source, rule checks and a stop button on every agent.
RetentionChurn signals read across three systems, $480k kept.
Data foundationsOne owned definition per measure, with full lineage.
Our promise

At least 3x return, within 12 months of delivery.

Measured on your own numbers: claims TAT, leakage, renewals and cost per claim.

How we measure it →

Questions insurance heads ask.

Do you replace our policy admin system?

No. We read from the systems you already run, such as policy admin, claims and CRM, and build one governed store beside them. Nothing changes in your core systems until you approve it.

Can data stay in India?

Yes. The data store can run on your own servers or in your own cloud account in an Indian region, on open formats. We work inside your IT, security and compliance rules.

How do you handle customer data?

We start read only and only touch the fields a use case needs. Access follows your existing roles, and the work is done under your data protection and confidentiality rules. We will agree the details with your security team before we begin.

Will AI decide claims on its own?

No. AI reads documents, scores unusual claims and shows the reason, and your people make the decision. Every answer traces back to the source record, and each agent has a stop button.

How soon do we see results?

It depends on the problem and how ready your data is. The first audit sets a plan for one problem, such as claims waiting time, and the first live view usually follows within the first weeks of the build.

A person in a dark suit holding a pen over an insurance form on a clipboard
Tell us your claims TAT.We will show you where the days go.

Talk to us about your book

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