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$3.3M in new monthly premium, zero new headcount.

How a large insurance franchise closed a 27-minute response gap and added $3.3M in monthly premium using Gail's AI voice agents.

Gail Team3分で読めます
$3.3M in new monthly premium, zero new headcount.

Client

Large insurance franchise (US)

INDUSTRY

Insurance

CHANNEL

Outbound voice + SMS

DEPLOYED

April 2026

The 27-minute gap

The franchise had a good problem. Inbound leads came in by the thousands each month — qualified, ready, and on average worth around $3,000 in annual premium. The trouble was not generating demand. It was reaching it in time.

Average response time from lead submission to first call sat at 27 minutes. Connect rates landed below 10%. By the time a rep dialed, the lead had either gone cold, bound somewhere else, or stopped picking up. Reps could see the pipeline they were losing. They were stuck on a callback queue that grew faster than they could work it.

Hiring more people was the obvious lever, and the wrong one. Even adding a small team would not close the speed gap, because the bottleneck was not headcount; it was the time between submission and first ring.

Call every lead, the moment it comes in.

Gail deployed AI-powered voice agents that called every inbound lead the second it was submitted. No queue, no rotation, no waiting. The agent verified the prospect, qualified them in real time, and warm-transferred to an available rep — who picked up a hot conversation instead of dialing a stale callback.

For leads that did not answer the first ring, Gail sent an automated SMS with the agent's name and a callback option. The reps' job stopped being "chase the list" and became "close the conversation in front of you."

<60

sec

Response time, down from a 27-minute average. Reps started picking up live conversations instead of cold callbacks.

The results, in one sentence.

Same product, same reps, same close rate. The franchise sold 1,100 more policies per month, which translated into $3.3M in new monthly premium. The 37% quote-to-close rate did not move. What moved was the number of conversations that ever got to "quote" in the first place.

2,849

Monthly policies sold before Gail.

3,959

Monthly policies sold after Gail. A 39% lift in quotes.

$0

Increase in marketing spend. Pure leverage on existing lead flow.

Why it worked.

Most "AI for sales" pitches promise a different funnel. This one did the opposite: it kept everything about the funnel intact, and fixed the one thing the team could not fix on its own — latency. The reps still wrote the quote. The reps still closed. Gail just made sure they were talking to leads who were still shopping.

The lesson generalizes. When you have qualified demand and a trained team, the highest-leverage problem is rarely "more leads" or "better scripts." It is the seconds between lead submission and first ring. Close that, and the rest of the machine works the way it was already supposed to.

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Case study deck

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Walk through every number, the deployment timeline, and the rep workflow in a 16-slide deck.

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Gailについて

2024年にMichael Vega-SanzとMatthew Vega-Sanzによって設立されたGailは、金融サービスセクター専用に設計された専門的なAIソリューションを提供しています。本社はフロリダ州マイアミにあり、サンフランシスコにもオフィスを構えています。

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