AI for insurance companies in India

Private document AI and placement workflows for insurance teams. Less manual re-entry, rules built in, and people reviewing exceptions.

Bring one problem

Less retyping. A clearer path through the work.

Policy documents
Private intake and field extraction.
Checked information
Rules applied. Exceptions reviewed.
Placement workflow
Requests and insurer quotes in one record.

Document AI is live; commercial placement is recently deployed, with results not yet measured.

Insurance problems the founding team has solved

Insurance document AI

Insurance marketplace, India

Live

A private document AI engine on the client's own servers. Six steps on every document: receive, identify the policy type and load its rulebook, mask names and IDs before any model reads it, extract 180+ fields, verify every field against the rules, route failed checks to a person. An open-source model replaced per-call public AI fees.

~98%: fields read correctly; hand-verified test set
80%: faster turnaround on quotes and policies; client-reported result
+10–20%: more quotes converted into issued policies; client-observed range
78%: lower AI cost per document processed; hand-verified test set

Delivered by our founding team at a previous firm. Clients anonymised under NDA.

Commercial insurance placement

Same insurance marketplace, commercial lines

Recently deployed

One deal record for every request. Insurers answer from a dashboard or by email, and both update it; AI reads each insurer email and turns missing documents into tasks. Pre-fill from last year's policy, one submit to every chosen insurer, quotes side by side, bind and push to the core system. Sealed bids; a doubtful email waits for a person.

Results to follow.

Delivered by our founding team at a previous firm. Clients anonymised under NDA.

What transfers to your insurer

Document intake and field extraction for policy, quote and endorsement documents.
Masking personal data before any model reads it.
Failed checks routed to your team for review.
Validate on your data: your document types, your field list and your core systems.

Data stays inside

Deployment inside your perimeter, PII masked before any model, internal VAPT before go-live, NDAs as standard. No certification is claimed.

Straight answers

Do you need our data to leave our environment?

No. We build and deploy inside your infrastructure.

Are commercial placement results measured?

The commercial placement system is recently deployed. Results are not yet measured; results will follow.

Did Ideolyst deliver these insurance builds?

The Ideolyst founding team did, at a previous firm, before Ideolyst existed. They are published with that attribution. iProcure is a product Ideolyst builds and runs.

Who owns the model and code?

For custom builds, the code, the weights we train for you and your data are handed over to your team. Packaged engines use volume-based pricing; confirm their deliverables and usage terms when scoping the engagement.

Start with one costly process

Tell us what your team does today, where it gets stuck and what a better outcome would look like.