Case study
Eighty-five thousand prompt recordings across 20 languages for an assistant launch.
A top-10 technology company needed 85,000 prompt recordings across 20 languages, balanced enough to train an assistant that works for real speakers, not a narrow sample.
85,000 prompt recordings - 20 (incl, regional variants) - Multilingual AI assistant training
110,000+
verified language specialists
Language specialist network
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
projects delivered since 2015
Measured outcomes
AI assistant prompt data
Project overview
What landed, and what made it hard.
A top-10 technology company needed 85,000 prompt recordings across 20 languages, including regional variants like Parisian and Canadian French and European and Brazilian Portuguese, to train a multilingual assistant.
Delivery snapshot
AI assistant prompt data
- Client
- A top-10 technology company
- Service
- Multilingual prompt data collection
- Languages
- 20 (incl, regional variants)
- Volume
- 85,000 prompt recordings
Why this mattered
Outcome before process.
Assistant training data is only as good as its coverage: a thin or skewed sample in one language means the assistant fails for those speakers in production.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
Prompt data collection across 20 languages fails when regional variants are collapsed into one, when speaker diversity is thin, or when recording quality is inconsistent across languages.
The challenge
The problem to solve
Prompt data collection across 20 languages fails when regional variants are collapsed into one, when speaker diversity is thin, or when recording quality is inconsistent across languages.
The company needed balanced, specification-compliant recordings across all 20 languages on one standard.
Operating response
What MoniSa changed
MoniSa sourced speakers across the 20 languages and their regional variants and ran QA on every recording for specification compliance and audio quality.
- Regional coverageRegional variants were sourced separately rather than collapsed into a single language label.
- Speaker diversitySpeakers were sourced for diversity so the assistant generalized beyond a narrow sample.
- Per-recording QAEvery recording was checked for prompt accuracy, audio quality, and format compliance.
Results
Measured outcomes from this engagement.
The company received 85,000 prompt recordings across 20 languages and their regional variants, the multilingual data behind an assistant launch.
| Volume | 85,000 prompt recordings |
|---|---|
| Languages | 20 (incl, regional variants) |
| End use | Multilingual AI assistant training |
Selection logic
What protected the result.
Assistant data needs real regional coverage and speaker diversity, not a thin sample stretched across 20 language labels.
Why the fit was real
Why the fit was real
Assistant data needs real regional coverage and speaker diversity, not a thin sample stretched across 20 language labels.
What decided the result
What decided the result
Balanced coverage across every language mattered more than raw recording count.
What buyers can reuse
What buyers can reuse
- Assistant training data fails in production wherever coverage is thin, so regional variants cannot be collapsed.
- Speaker diversity and per-recording QA are what make multilingual voice data generalize.
- The evidence keeps the client details confidential and attributes the metrics only to this engagement.
Continue from this proof
Useful comparisons for the same problem.
Use these links to compare the case with the matching service, buyer guide, and language coverage.
Mapped context
Service and buyer context
Languages named
Examples referenced in the engagement.
- Regional French and Portuguese variants
- Indic languages
- East and Southeast Asian languages
More proof
Related proof
Compare this case with adjacent MoniSa proof before deciding whether the operating pattern fits your brief.
case evidence
Nearest proof pattern.
These related cases keep the next click close to the same kind of work.
AI data servicesNatural Hindi-English code-switching speech data, client details confidential.
Bilingual live-speech data
The challenge. A voice AI program needed 100 hours of natural Hindi-English bilingual conversation with code-switching.
What we did. MoniSa sourced genuinely bilingual speakers and captured unedited conversation with per-recording QA.
The result. The program received 100 hours of bilingual speech from 20 speakers at strong acceptance.
AI data servicesVoice data with a strong first-pass acceptance rate, client details confidential.
Voice data recording
Problem. A speech program needed 150 hours of spec-compliant voice recordings across three languages.
Action. MoniSa ran per-recording QA on every sample for script, audio, and format before submission.
Result. The program received 150 hours across Polish, Dutch, and Australian English with a strong first-pass acceptance rate.
AI data servicesLong-form transcription held to reviewed quality over length and dialect, client details confidential.
Long-form transcription
Problem. A model program needed 500+ hours of long-form transcription across four locales for AI training.
Action. MoniSa used dialect-matched transcribers and full-file QA to hold accuracy over long files.
Result. The program received 500+ hours across four locales with reviewed quality.
Buyer questions
Ask the questions weak vendors avoid.
Short answers for buyers checking fit, coverage, quality method, and next-step readiness.
What was delivered on this engagement?
Volume: 85,000 prompt recordings. Languages: 20 (incl, regional variants). End use: Multilingual AI assistant training
What control kept the work stable?
Balanced coverage across every language mattered more than raw recording count.
Where should similar work go next?
Use AI data services for the delivery model, the case studies hub for buyer-side evaluation, and the contact page for a scoped brief.
Similar brief
Send the constraint behind the metric.
A useful follow-up to a case study names the language mix, review model, deadline, and what proof your buyer team needs before approval.
Production-ready brief
01Closest matching challenge from this case02Language pair, dialect, and script coverage03Volume, cadence, or hours to deliver04Reviewer model and acceptance criteria05Security or platform constraints06Proof needed for stakeholder approval