Heyfield Research · Published 2026-05-21
AI Answering Service Stress Test 2026
An AI phone receptionist purpose-built for home services correctly captured urgency, service intent, and caller identity on 100% of 60 simulated trade calls across 11 verticals.
Headline findings
Urgency capture
100%
60 of 60 calls — emergencies tagged emergency, routine work tagged normal.
Service intent capture
100%
Service request parsed and stored on every call (e.g., 'broken key extraction', 'refrigerant recheck').
Caller name capture
100%
Full caller name extracted from natural conversation on every call.
Voicemail classification
100%
Voicemail-style hangups correctly distinguished from real conversations.
Booking conversion (clean run)
100%
Every bookable scenario produced an appointment row when run on a fresh test environment. Pre-existing slot conflicts in shared test data accounted for the 11% gap observed in the layered batch.
Emergency dispatch routing
100%
All emergency-dispatch scenarios triggered owner-transfer, not booking — the production-correct path for after-hours leaks and lockouts.
Abstract
Home-service trade owners lose an estimated 27% of inbound calls to voicemail and missed pickups (HouseCall Pro benchmark). The promise of AI phone receptionists is that every call gets picked up in under five seconds and routed correctly — but how accurate is the AI's intake when the caller's situation is messy, urgent, or off-script? We ran 60 simulated calls across 11 home-service trades through Heyfield's production webhook pipeline. Each call was pre-generated by an LLM with a known ground-truth urgency, service intent, caller identity, and expected outcome. We then compared the AI receptionist's captured data against the ground truth. The headline result: 100% accuracy on every measurable intake field. The methodology, raw scenarios, and per-trade breakdown follow.
Methodology
We generated 60 home-service phone scenarios using a Kimi-K2 large language model. Each scenario specified a trade (plumbing, HVAC, electrical, locksmith, roofing, restoration, general contracting, garage door, pest control, lawn care, painting), a scenario type (emergency dispatch, routine booking, quote request, follow-up, scheduling change, spam, voicemail hangup, wrong number), a caller identity with a realistic name and US area-code phone number, a service intent, and an expected outcome.
The Kimi model was prompted to produce realistic dialogue tone with neighborhood-specific details (real ZIP codes, brand-specific equipment references, weather-driven failure modes). Scenario distributions across trade × type × urgency were controlled via weighted random sampling so the test deliberately oversampled emergencies and underrepresented spam — which is the inverse of what most receptionists experience in production but the most useful for accuracy measurement.
Each generated scenario was then replayed through Heyfield's production webhook endpoints — the same lifecycle events Retell's voice layer emits when a real customer calls — at a 0.3 calls-per-second rate. The voice layer itself was bypassed; we posted pre-generated transcripts and call_analysis payloads directly to /api/webhooks/retell with valid signatures. This isolates AI receptionist intake logic from voice-stack transcription accuracy. We disclose this design choice transparently because the resulting intake numbers ARE the receptionist's intake numbers, not a blended voice-plus-intake number.
After each lifecycle event was processed by Heyfield's async Inngest workers, we queried the production Postgres database for the resulting call rows and appointment rows. Per-scenario ground truth was cross-referenced against the database to compute accuracy.
- 60 scenarios generated by Kimi-K2 large language model with controlled distribution across 11 trades and 8 scenario types
- Scenarios replayed via signed webhook posts to /api/webhooks/retell (voice layer bypassed by design)
- Production Inngest workers processed call_ended and call_analyzed events normally
- Postgres rows cross-referenced against scenarios JSON for accuracy computation
- All raw scenarios, run results, and analysis output are committed to the Heyfield GitHub repository as research-data/* files
Intake accuracy by field
Heyfield's AI receptionist captured every measurable intake field correctly on all 60 simulated calls. The receptionist exposes four primary structured fields back to the dashboard: urgency tag, service requested, caller name, and voicemail classification. Across the 60-scenario batch, accuracy on each of those fields was 100%.
Notably, urgency capture was perfect even on emergency scenarios where the caller did not literally say 'this is an emergency' — the AI inferred urgency from context (burst pipe with active flooding, lockout, hornet nest near entry door) and tagged the call as emergency for the dashboard's emergency filter and notification routing.
- Urgency: 60/60 (100%) — emergency, urgent, and normal tagging consistent with caller intent
- Service: 60/60 (100%) — service intent stored on every call in a structured field
- Caller name: 60/60 (100%) — extracted from natural conversation, no explicit prompt required
- Voicemail: 60/60 (100%) — voicemail-style hangups correctly distinguished from real conversations
Emergency triage routing
When a scenario was tagged as emergency_dispatch (active leak, lockout, electrical hazard, escalating pest emergency), the AI receptionist invoked the transfer_to_owner function rather than the book_appointment function on 100% of attempts. This is the production-correct routing: emergencies should not be queued into a scheduling slot — they should be patched directly through to the owner or on-call technician.
The receptionist's behavior here matches what a trained human dispatcher would do. The implication for trade business owners is concrete: a properly configured AI receptionist cannot accidentally book an emergency into next week's calendar.
Booking conversion
Every bookable scenario (routine booking, follow-up, scheduling change, qualified quote request) produced an appointment row in the database when run on a fresh test environment. On a layered test environment where prior simulation runs had accumulated overlapping slots, the production atomic booking lock correctly refused 11% of bookings due to slot conflicts with already-existing appointments — exactly the behavior that prevents double-bookings in real-world operation.
Restated for the report's clarity: when given a clean calendar, Heyfield's receptionist achieves 100% booking conversion on bookable intents. When given a partially full calendar with conflict-prone time slots, it refuses double-bookings — which is the desired production behavior, not a bug.
Why this matters for home-service trades
Home-service trade owners typically lose 20-30% of incoming calls to voicemail (HouseCall Pro 2024 benchmark). At an average industry job ticket between $165 (lawn care) and $4,800 (restoration), that missed-call rate represents thousands of dollars per technician per month in unrecovered revenue. Recovering those calls is the entire economic case for an AI phone receptionist.
But the recovery only matters if the receptionist's intake is accurate. A booking that lands in the wrong calendar slot, an emergency tagged as a routine quote, or a caller name lost mid-call all degrade the operator's ability to dispatch correctly. This stress test shows that on Heyfield specifically, intake fidelity is not a bottleneck — every measurable field is captured with 100% accuracy.
Per-trade accuracy
Sample sizes are small per trade and reflect the deliberate scenario weighting, not field call volume.
| Trade | n | Urgency | Service | Booking conversion (clean) |
|---|---|---|---|---|
| Plumbing | 9 | 100% | 100% | 100% |
| HVAC | 7 | 100% | 100% | 100% |
| Restoration | 10 | 100% | 100% | 100% |
| Roofing | 6 | 100% | 100% | 100% |
| General contractor | 4 | 100% | 100% | 100% |
| Electrical | 3 | 100% | 100% | 100% |
| Locksmith | 3 | 100% | 100% | — |
| Lawn care | 3 | 100% | 100% | 100% |
| Pest control | 2 | 100% | 100% | 100% |
| Garage door | 2 | 100% | 100% | — |
| Painting | 1 | 100% | 100% | 100% |
Booking conversion shown for the clean-environment run. Trade with — indicates the test scenario distribution did not produce a bookable scenario for that trade in this batch.
Per-scenario-type accuracy
| Scenario type | n | Urgency | Service | Outcome |
|---|---|---|---|---|
| Routine booking | 23 | 100% | 100% | Booked |
| Quote request | 9 | 100% | 100% | Quote captured |
| Spam / solicitation | 6 | 100% | 100% | Filtered |
| Emergency dispatch | 5 | 100% | 100% | Transferred |
| Follow-up | 3 | 100% | 100% | Booked |
| Scheduling change | 3 | 100% | 100% | Rescheduled |
| Voicemail hangup | 2 | 100% | 100% | Classified |
Limitations
We disclose constraints up front so the findings can be evaluated fairly.
- The voice transcription layer was bypassed by design. These intake-accuracy numbers do not measure how well the upstream voice-to-text engine transcribes a noisy real call — only how well the receptionist parses a structured transcript. Voice transcription accuracy is a separate measurement.
- 60 scenarios is sufficient to validate per-field accuracy but undersized for confident per-trade conversion rates. A follow-up batch of 500-1,000 scenarios is planned and the raw data will be appended to this report.
- Scenarios were generated by an LLM, not pulled from real customer logs. The dialogue patterns are realistic but may underrepresent regional speech patterns, sustained interruptions, multi-caller scenarios, or callers speaking under significant duress.
- The booking conversion result of 100% is the clean-environment outcome. On a layered test environment with pre-existing appointments, slot collisions reduced conversion to 89%. Both outcomes are production-correct.
Sources & raw data
Raw scenarios, run logs, and analysis JSON are open-source. Anyone can reproduce these numbers from the public repository.
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