We are building AI agents for trades businesses (HVAC, electrical, plumbing).

We tested 13 providers on 100 real customer calls with:

- Background noise (vans, job sites, crying babies)

- UK regional accents (England North/South, Scotland, Ireland)

- Critical info: postcodes, addresses, phone numbers

- Variable turn length (1-5 words vs 16+)

Results: 2.5x performance gap

    Best: Deepgram Flux (15.86% WER)
    Worst: OpenAI Whisper (39.78% WER)

Interesting findings:

(1) Postcode recognition was hardest across ALL providers (50%+ WER).

(2) Regional variance was massive. Ireland accents destroyed most models (20-30% higher WER than Southern England).

(3) Short confirmations ("yeah", "ok") actually had worse WER than long explanations. Counter-intuitive but likely due to less context for the language model.

Full breakdown with graphs: https://x.com/pstrav/status/2018416957003866564

Context: We're Elyos AI (YC S23), handling 100k+ calls/month for trades businesses across the world.

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