Call Centre AI Companies · LLM Evaluation
LLM Evaluation Data for Call Centre AI Companies
Agent-assist, QA-automation and voice-bot vendors serving Indian BPO and enterprise contact centres, working with narrowband telephony audio and heavy accent variation. Human evaluation of large language model output in Indian languages, including cultural and factual fit.
- Buyer
- Call Centre AI Companies
- Use case
- LLM Evaluation
- Metric
- Rubric scores with confidence intervals
Where the two meet
Production audio is 8 kHz telephony; models trained on studio audio degrade sharply That is a llm evaluation problem, and it is solved by data shaped like this:
- Native-speaker rater panels per language
- Rubric-based scoring with calibration
- Overlapping assignments for agreement
Your evaluation criteria
- Is narrowband simulated at capture, not by downsampling studio audio?
- Are agent and customer on separate channels?
- Are emotion and escalation variants available on demand?
Metrics
- Rubric scores with confidence intervals
- Inter-rater agreement
- Failure-mode distribution
Pitfalls
- Raters who are fluent but not native in the variety
- Rubrics written in English and applied to non-English output without localisation
Contract points
- Consented synthetic-scenario audio with no real customer PII
- Scenario library ownership
- Per-scenario volume guarantees
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.