Underwriters, agents and claims handlers are doing data-entry work. AI gives them their judgement back.
Quotation, KYC, document checks, claim FNOL, fraud screening, renewals — almost every node in an insurance ops chain has 30-60% busywork. Below are 15 places I take that work off the team, and the rupees that fall to the bottom line.
₹26.57 L / year saved
For a mid-sized broking house / regional insurer ops cell · math shown below
Calculations are for this size of business
Type
Mid-sized broking house / regional insurer ops cell
Annual turnover
₹3–10 Cr brokerage
Team size
20–60 underwriters, claims & support staff
Locations
1–4 offices
Bigger setup? Multiply the numbers by your scale (e.g. 3 clinics ≈ 3× savings). Smaller? Divide. The ratio of savings to cost stays the same.
Built & shipped by Imaduddeen Khan — same engineer behind the heavy-haul AI platform
If this sounds like your week
Your best underwriter spends half their day re-keying KYC and matching policy schedules. That is not what you hired them for.
Read it honestly. If even three of these hit, you are bleeding hours and money you will never get back.
Lead-to-quote takes hours because data is keyed across 5 portals.
Claim FNOL still happens by phone & email; first 24 hours wasted.
Document checks (Aadhaar, PAN, driving licence, RC, medical) done manually for every case.
Renewal calls are random — high-value clients lapse silently.
Fraud signals only spotted after payout; recoveries near impossible.
Agents and brokers ask same product questions repeatedly; sales support drowning.
498 hrs
Hours wasted today
team time / month
68 hrs
Hours after AI
430 hrs returned
₹2.21 L
Monthly cost saved
81% reduction
₹26.57 L
Annual savings
compounds every year
The 15 automations
Traditional way → AI way, with the math on the table
Every line below is a real workflow I have built or could ship inside 2–6 weeks. The per-task numbers describe a reference setup at the upper end (busy clinic, full QSR week, etc.) using a loaded labour rate of ₹550/hr. The headline savings of ₹26.57 L/year at the top of the page are these per-task savings scaled down to the mid-sized broking house / regional insurer ops cell described above. If your business is larger, multiply; if smaller, divide.
01 · Quotation
Multi-insurer quote generation
Traditional way
Sales puts client info into 4-6 insurer portals, downloads PDFs, builds comparison sheet.
• Time: 30 min × 60 quotes/day
• Volume: ≈ 1,500 / month
• Total: 750 hrs / month
AI way (what I build)
Agent reads lead, fills insurer portals via API/RPA, builds branded comparison PDF in your template.
You'll be hiring an engineer who already shipped this.
The same systems described above — agentic workflows, document extraction, voice agents, secure APIs, deployment — are running today inside a logistics company I built for. Not slides. Production.
Production-grade systems
13 modules, real users, real money flowing through them — see the heavy-haul case study.
Industry-aware design
Workflows are designed around how your domain actually moves, not generic ChatGPT wrappers.
Fast turnaround
First working slice in 7–14 days, full build in 2–6 weeks for most workflows.
Honest pricing
Fixed-scope quotes. You see the calculation, the build cost, and the payback month before signing.
AI extracts data from claim forms, hospital bills, and FIR reports, runs first-level rule checks, and prepares a packet for the human assessor — turning 90 minutes per claim into under 10.
Q.Can AI handle policy renewals end-to-end?
Yes — for standard products. The agent personalises renewal quotes on WhatsApp, answers FAQs, accepts payments, and only escalates to humans for changes in coverage or risk profile.
Next step is small
Send one WhatsApp. Get a free workflow audit.
I'll look at one painful workflow in your business and tell you, in writing, what it would take to automate it. No deck, no obligation.