The question PolicyDesk answers every morning: what do I need to do today?
India has over 25 lakh licensed insurance agents and distributors — the people who actually sell and service policies for millions of families. Almost none of them have software built for how they actually work. PolicyDesk is.
- 01RECEIVEpolicy document · PDF · scan · WhatsApp photo
- 02READinsurer-aware extraction · the layout each insurer actually uses
- 03LINKrenewal chain · matched by the identifiers on the page
- 04MAPfamily structure · covered-members table, read once
- 05FLAGhonest gap · “this figure isn’t available yet”
- 06WRITEclient record updated · no manual entry
Watch what PolicyDesk actually does
Three traces from the same system, played in the order a distributor’s day produces them: the book is scanned for gaps, a WhatsApp enquiry is recognised, and a renewal is chained — each landing on today’s list with its reasoning attached.
- 01READentire book · every client · every category · every family
- 02MATCHclient holds life + motor · no health policy
- 03GATEchildren uncovered under the floater · a gap, not a mystery
- 04SURFACEa conversation waiting to happen · reasoning attached
- 01RECEIVEWhatsApp message · “can you also cover my son”
- 02CLASSIFYgenuine business enquiry · recognised in the conversation
- 03LINKmatched to a known client · their existing record
- 04WRITEadded to today’s list · reasoning alongside
- 01READpolicy renewed five times · one relationship, not five rows
- 02LINKidentifiers on the documents · chain continued
- 03GATEmotor cover lapsing in three weeks · due, for whom, why
- 04SURFACErenewal on today’s list · money at risk shown
Four tools, none of them built for this work
A family holding four policies across three insurers looks like four unrelated clients. A motor cover lapsing in three weeks and a health floater running out of family coverage sit in two separate places, and nothing connects them.
Insurer portals
A different login, layout and export format for every company represented.
Where client conversations, policy documents and renewal reminders actually happen, with no structure and no memory.
Excel
The closest thing most distributors have to a client database, built and maintained by hand.
Paper and memory
For everything the first three do not cover.
AI does the data entry, so distributors don’t have to
A distributor uploads a policy document — a clean PDF, a scanned copy, or a photo taken on WhatsApp — and the details are extracted automatically, without a single field typed by hand.
Document arrives
PDF, scan, or a WhatsApp photo.
Insurer-aware extraction
Reads the layout each insurer actually uses — Tata AIG’s motor policies do not look like Star Health’s mediclaim bonds, and neither looks like an LIC endowment certificate.
Renewal chain built
Matched to prior policies by the identifiers on the page — a policy renewed five times is one continuous relationship, not five unrelated rows.
Family structure mapped
Read once from the covered-members table printed on the policy — no manual linking.
Client record updated
No manual entry.
Noticing what a client does not have
The hardest part of running an insurance book is not tracking what a client has — it is noticing what they don’t. PolicyDesk reads across a distributor’s entire book and surfaces exactly these moments.
A client with life and motor cover but no health policy is a conversation waiting to happen, not a mystery.
Cover a family is already paying for, about to lapse without ever being used.
A due date is also the moment to ask what else the family needs covered.
PolicyDesk reads the conversation where the business actually happens, recognises a genuine enquiry, and connects it back to what the distributor already knows about that client — then it joins today’s list, with the reasoning shown alongside it.
Ambiguity that breaks naive automation
A lot of what PolicyDesk does sounds simple — read a PDF, remind someone of a date. In practice, Indian insurance data is full of exactly the kind of ambiguity that breaks automation built on hope.
Identifying “the same client” needs real judgement, not a simple lookup.
Own-damage and third-party cover can sit with different insurers and different renewal dates for the same vehicle — easy to model wrong, expensive to get wrong.
Mobile numbers and email addresses almost never appear on Indian policy documents, because they were never meant to be extracted this way.
A certificate of insurance and a full policy schedule need to be read completely differently, even within one category.
A missing detail on screen — “this figure isn’t available yet,” “this distributor hasn’t confirmed this date” — is always preferable to inventing an answer and letting a distributor act on something that is not real. In an industry built on trust and long-term relationships, a wrong number shown confidently is worse than an honest gap. That discipline shapes every part of the product: how data is extracted, how gaps are surfaced, and how AI-generated leads are shown to a distributor — as information to act on, with reasoning attached, never as a decision already made for them.
Who it is for
Multi-product insurance distributors and small agencies in India — people managing real, growing books across life, health, motor and general insurance, often for the same families over many years. A distributor whose book has outgrown Excel, and whose time is worth more than manual data entry.