Insurance conversations, handled inside your perimeter
Objection handling, compliance-safe phrasing, product and rate lookup, and structured call records — fine-tuned on your own calls, in the languages your agents actually speak, running in your own cloud.
What it does
Objection handling
Price, coverage gaps, competitor comparison, timing. A finite set phrased differently every time — exactly the shape a fine-tuned small model handles well. Trained on how your agents answer, including when the objection arrives in Hindi and the product name stays in English.
Compliance-safe phrasing
Disclosure requirements, prohibited comparative claims, suitability language. Enforced at generation time and logged, rather than caught by QA sampling after the call.
Product and rate lookup
Grounded in your actual rate tables and product documents — retrieved, not recalled — so the model cannot invent coverage that does not exist.
Call structuring
Schema-valid output into your CRM: what was discussed, what was disclosed, what was promised, what happens next. Written in the script your systems expect.
Insurance sales language is already governed. AI does not change that — it changes who has to explain it
Suitability requirements, unfair trade practice rules, disclosure mandates and recording consent law already shape what an agent may say on a call. None of that is new.
What is new is that putting an AI in the loop creates a question your current architecture cannot answer: show me why it said that, to this customer, on that date. A hosted API call is not a record. A pinned model version with logged inputs and reproducible outputs is.
That is not a reason to avoid AI on calls. It is a reason to run models you control.
What this replaces: not your agents. The lookup, the phrasing and the paperwork — so the agent spends their attention on the customer.
Who this fits
Good fit: carriers, MGAs and brokerages with existing call recordings, their own cloud infrastructure, meaningful call volume, and a compliance function that has already blocked at least one AI pilot.
Not a fit yet: under a few thousand calls a month, no recordings, or no infrastructure of your own. Below that the economics do not work, and we will say so in the feasibility review rather than after you have paid for a pilot.
Common questions
Can this run without sending call data to a third party?
Yes. That is the point of the architecture. The models run in your cloud account, your data centre, or fully air-gapped. Audio, transcripts and generated responses stay inside your network boundary.
How does it handle a Hindi–English insurance call?
The model is fine-tuned on code-mixed calls from your own archive. It classifies the objection regardless of which language it arrives in, and generates a response that keeps each language in its correct script — Devanagari for Hindi runs, Latin for English product terms.
What call volume do we need to justify this?
As a rough threshold, a few thousand calls a month. Below that a hosted API is usually cheaper even accounting for token inefficiency, and we will tell you so.
Do we keep the model?
Yes. You keep the weights, the evaluation harness and the infrastructure definitions. If the engagement ends, the system keeps running.