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Case Study

Case Study: Allstate Insurance, 35,000 Lead Qualification Calls Per Day Across 1,200 Agent Offices

How EHVA conducts 19-question insurance lead qualification at scale, matches callers to the right Allstate agent in real time, and delivers transfer-ready leads with 91% of callers never suspecting they spoke with AI.

Last updated: April 17, 2026

Allstate’s retail agent network is one of the largest in the United States, with thousands of independent agent offices each carrying their own book of business, their own geographic territories, and their own licensing requirements. Connecting an inbound lead to the right agent is not a simple routing decision. It requires matching the caller’s zip code, coverage needs, risk profile, and eligibility against the active agent roster in real time, before the conversation ends.

The leads in this deployment come from an outbound cold calling campaign. A prospect receives a call, misses it, and calls back the number. When they do, EHVA answers. From that moment, EHVA conducts a complete 19-question insurance intake, matches the caller to an available Allstate agent office during the conversation, verifies critical data points against live APIs in real time, and either transfers the caller directly to their matched agent or books an appointment before the call ends.

At 35,000 calls per day across 1,200 agent offices, four live API integrations, and 19 qualification questions including some of the most data-sensitive fields in insurance sales, this is among the most technically demanding voice AI deployments ever documented.

35,000

Qualification calls handled per day

1,200

Allstate agent offices served

19

Qualification questions per call

4

Live APIs called during every conversation

91%

Of callers unaware they spoke with an AI

The Challenge

Insurance lead qualification is one of the most demanding applications for voice AI. The data collected must be accurate, not approximately accurate. A misheard date of birth, an unverified address, or an incorrectly transcribed email address does not produce a lower-quality lead. It produces a dead one. An agent who receives a lead with incorrect contact or eligibility data cannot write the policy. The call’s entire value is lost.

The 19 questions in this intake include fields that are difficult for any phone conversation to collect cleanly: full street address with apartment number, email address spelled out character by character, last name with unusual spelling, date of birth, and driving history including DUI disclosure. These are not questions that a caller answers quickly or without friction. They require patience, clarity, and the ability to handle corrections mid-flow without losing the caller’s confidence in the process.

The agent matching requirement added a second layer of complexity. Allstate’s agent network is geographically distributed and each office has its own territory, licensing coverage, and availability. A lead generated in one zip code might be served by multiple agents. The right match depends on the caller’s location, the coverage type they need, and which agent offices are actively accepting new transfers at the time of the call. This matching cannot happen after the call. It has to happen during it, so the transfer goes to the right place the first time.

At 35,000 calls per day, the system also had to operate without degradation at volume. Every call had to receive the same quality of conversation, the same API response handling, and the same transfer or booking outcome regardless of concurrent call load.

The Qualification Flow

EHVA conducts the full 19-question intake as a natural conversation, not a scripted interview. Questions are asked in a sequence that builds rapport and manages caller tolerance, with the most sensitive or demanding questions introduced after the caller is already engaged and trust has been established. Simultaneously, EHVA is running API calls in the background to match and verify data as it is collected, so by the time the last question is answered, the agent match is already identified and confirmed.

How the 19-question qualification and agent match works

1. Opening qualification and initial agent match

Zip code, current insurance carrier, coverage type needed, and number of vehicles.

API 1, zip code triggers a real-time agent match query. Available Allstate offices serving this geography are identified and ranked.

2. Risk and eligibility questions

Years licensed, accidents in past 3 years, moving violations, DUI or DWI history, home ownership status, and bundling interest.

API 2, risk profile built from responses. Agent match refined against office acceptance criteria for risk tier.

3. High-accuracy personal data collection

Full street address, apartment or unit number, email address, last name, date of birth, first name confirmation, and phone number confirmation.

API 3, address verified against a real-time validation API as collected. Corrections handled conversationally.

API 4, completed lead record pushed to Allstate’s lead management system with all 19 fields, agent match ID, and call recording reference.

4. Transfer or appointment

Caller preference: transfer now or book appointment.

If transfer, the call connects live to the matched agent office with a full lead summary spoken to the agent before the caller joins. If appointment, time preference is collected and booking confirmed within the same call.

The highlighted questions are the ones that require the most careful handling. Address collection, email spelling, last name, and date of birth are fields where a single error invalidates the lead for underwriting. EHVA is configured to confirm each of these fields back to the caller before moving on, and to handle corrections naturally if the caller indicates something was captured incorrectly. The address validation API fires immediately on collection, so if an address does not match a known record, EHVA asks the caller to clarify before moving to the next question rather than logging an invalid address and discovering the problem after the call.

Four live APIs, every call. Every qualification call fires four API integrations in real time, during the conversation, without the caller ever noticing a pause. Each API call happens in the background while EHVA continues the conversation, returns a response in under a second, and informs what EHVA does next, whether that is refining the agent match, correcting an address, adjusting the question sequence based on risk profile, or confirming the lead record is complete before offering the transfer.

Results

  • 35,000 qualification calls per day, full 19-question intake conducted on every call at consistent quality regardless of concurrent volume or time of day
  • 1,200 agent offices served, each call matched to the right office in real time based on geography, coverage type, and risk tier acceptance criteria
  • 91% AI-unawareness rate in caller assessments across the deployment, callers reported no awareness that they had completed a 19-question intake with an AI rather than a human agent
  • 100% address verification coverage, every address collected is validated against a live API before the call ends. No lead enters the system with an unverified address field
  • 4 live API integrations per call, agent matching, risk tier refinement, address validation, and lead record submission all fire during the live conversation without audible delay
  • 19 questions per intake, including the most data-sensitive fields in insurance sales: address, email, date of birth, last name, and driving history with DUI disclosure

What Made This Deployment Technically Possible

The 19-question intake at this scale is not a prompt engineering problem. It is a systems problem. The questions themselves are not difficult to ask. The difficulty is in handling the full range of caller responses to each one, managing corrections without losing caller confidence, firing API calls without creating perceptible pauses, and making routing decisions mid-conversation based on API responses that may return conditions the original call flow did not anticipate.

The address verification step is the clearest example of this complexity. A caller gives an address. EHVA fires the validation API. If the address validates, the conversation continues. If it does not, EHVA has to handle the correction naturally: confirm what was heard, ask the caller to clarify or re-spell, re-validate, and continue. This correction loop has to feel conversational rather than mechanical, because a caller who senses that the system is struggling loses confidence in the process and abandons the call.

The same applies to email address collection. Spelling out an email address over the phone is inherently awkward. EHVA collects it character by character, reads it back for confirmation, and handles corrections. The caller’s experience of this exchange determines whether they trust the process enough to continue to the DUI question, the date of birth, and ultimately the transfer. Getting the mechanics right is what made the 91% unawareness rate achievable across a 19-question call.

“We have run high-volume lead qualification programs for years using human agents. The data accuracy EHVA delivers is better. The call consistency is better. And it runs at a scale we could never staff to.”

Performance marketing operator, Allstate lead generation program

“19 questions. Four APIs. 35,000 calls a day. 91% of callers thought they were talking to a person.”

The 91% AI-unawareness rate is the number that stops people in this deployment. A 19-question insurance intake is not a short or casual interaction. It takes several minutes and asks for sensitive personal and financial information. The fact that nine out of ten callers completed it without recognizing they were speaking with an AI is not a measure of how good EHVA is at seeming human. It is a measure of how good EHVA is at conducting a call. When the questions flow naturally, the corrections are handled gracefully, and the transfer lands at the right agent’s office the first time, the question of whether the voice was human simply does not arise.

“The agents are receiving leads that are already fully qualified and verified. They are not spending the first five minutes of every call re-gathering information that should have been collected before the transfer.”

Allstate agency network operations, deployment review

Conclusion

The Allstate deployment represents the outer edge of what insurance lead qualification via voice AI can do. Thirty-five thousand calls per day, 19 questions, four live API integrations, real-time agent matching across 1,200 offices, and data accuracy requirements that allow no margin for error on the most sensitive fields. The fact that this runs at consistent quality, around the clock, with 91% of callers completing the process without identifying EHVA as AI, makes this one of the most significant performance marketing voice AI deployments in the insurance industry.

For insurance lead generation operations evaluating whether voice AI can handle the complexity and data demands of a real qualification program, this deployment provides a direct and detailed answer.

Allstate is a trademark of Allstate Insurance Company. EHVA’s deployment referenced in this case study serves individual Allstate agent offices and lead generation programs and does not represent an endorsement by or formal affiliation with Allstate Insurance Company. Quotes are drawn from deployment feedback and have been lightly edited for clarity.

See also

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