Conversational AI Companies · Speech Analytics
Speech Analytics Data for Conversational AI Companies
Voice-bot and chat-plus-voice platforms deploying into Indian markets, where the gap between demo accuracy and live accuracy is a code-mixing problem. Mining Indian-language call and meeting audio for intent, compliance and quality signals.
- Buyer
- Conversational AI Companies
- Use case
- Speech Analytics
- Metric
- Intent accuracy
Where the two meet
Bots trained on clean single-language data fail on real switching mid-utterance That is a speech analytics problem, and it is solved by data shaped like this:
- Domain-realistic conversation audio
- Intent, outcome and compliance labels
- Speaker-separated channels
Your evaluation criteria
- Does the data include overlap, interruptions and backchannels?
- Are utterances collected over the same channel conditions as production?
- Is intent labelling done against your live taxonomy?
Metrics
- Intent accuracy
- Compliance-event recall
- Summary factuality
Pitfalls
- Labels defined without listening to real calls first
- Ignoring dialect coverage in the target market
Contract points
- Scenario confidentiality
- Right to reuse across bot versions
- Delivery in a format that drops into an existing pipeline
Frequently asked
What does a first engagement look like?
Usually a scoped pilot: one language, an evaluation set plus a first training batch, delivered in three to five weeks, followed by the full programme.
Can you match our existing vendor's schema?
Yes. Working to your schema avoids a conversion pass and keeps deliveries comparable across vendors.
How is provenance documented?
Per-item contributor records and consent mapped to IDs in the manifest.
Send your requirement
Language, volume, metric, deadline.