Why does the AI keep getting addresses and names wrong?
Speech recognition is not uniformly accurate. It is strong on common words and weak on exactly the things a booking needs: proper nouns, street names, unit numbers, spelled-out emails and long digit strings. These carry little context for the model to lean on, so errors cluster there. The fix is not a better model — it is validating what you heard against a real database and reading it back before you write it.
Where recognition breaks down
Transcription accuracy depends heavily on predictability. A sentence like "my air conditioner stopped working last night" is easy because every word constrains the next. "I'm at 4712 North Kachina Court, apartment B" gives the system almost nothing to predict with.
Layer on the conditions of a real call — a caller on speakerphone in a car, background noise, regional accents, a name the model has rarely encountered — and the error rate on those specific fields rises sharply while the rest of the transcript reads fine. That is why a transcript can look clean and the booking still be wrong.
Fixes that actually work
- Validate, do not trust. Run every captured address against a real address database. If it does not resolve, the agent asks again rather than writing a plausible-looking string.
- Bias the recognizer. Feed it the street names, city names and business-specific vocabulary from your own service area. Constraining the vocabulary to what is possible locally removes a whole class of errors.
- Structured readback. Read the address back in a fixed format and require a yes. Reading it back conversationally invites a distracted yes to a wrong address.
- Digits one at a time. Phone numbers and unit numbers get confirmed digit by digit, not as a spoken number.
- Match, do not create. Search existing customers first. Most callers are already in the system, and matching avoids both the transcription problem and a duplicate record.
The failure that costs the most
The expensive error is not a misheard address the agent flags. It is a misheard address the agent silently normalizes into something that looks real — a valid street in the wrong part of town. That job dispatches, a truck drives, and nobody discovers the problem until the technician is standing in the wrong driveway.
This is the general argument against letting a model quietly correct data on the way into an operational system. Validation belongs in the integration layer, where it can fail loudly, not inside the conversation where a confident guess reads as competence.
How to find the errors you already have
Compare agent-created jobs against dispatcher edits on address and contact fields. The edit rate on those fields is a clean, honest accuracy measure that requires no manual review, and it will usually be higher than anyone expected. Feed the corrected values back into the recognizer's vocabulary and it improves over time — the same feedback loop that improves any operational AI system.
Topics: speech recognition · addresses · data quality · validation
Have a version of this question about your own business?
The useful answer usually depends on which systems you run and how they're connected. That's a conversation, not a blog post.