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
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
brands served 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
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 coverage Regional variants were sourced separately rather than collapsed into a single language label.
- Speaker diversity Speakers were sourced for diversity so the assistant generalized beyond a narrow sample.
- Per-recording QA Every 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.
The selection came down to whether MoniSa could source and review the work at standard, and whether that would hold across the full run.
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.
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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 approvalCapability at a glance
The answers most briefs open by asking for.
Buyers rarely start with who we are. They start with a list of fields to fill. Here are ours, so the first email can be about the work instead.
- Languages and locales
- 300+ languages and 4,500+ dialects, quoted per locale rather than per language — because the dialect decides whether a dataset is usable, whether a market accepts a release, and which specialist the work goes to.
- Specialist network
- 110,000+ verified language specialists — linguists, annotators, and reviewers — plus voice talent and subtitlers, matched to the language, domain and task before assignment.
- Capacity and mobilisation
- Named availability confirmed per pair before scoping. Coverage is reported as staffed today or needing a recruitment window, in writing, before a launch date or release window is agreed.
- Sourcing constraints
- Specialists can be sourced against geographic, residency, locale and demographic requirements — including native-only, in-country, and speaker-diversity quotas where a data programme demands them.
- Deliverables and specs
- Work is delivered to the receiving specification: structured formats and schemas for data and annotation work, and timed-text, audio and platform conformance for media — subtitle reading speed, line limits, cue timing, channel and sample-rate requirements included.
- Comparable work
- 62 documented case studies stating the scope, the constraint that made it difficult, and the measured result — across AI data programmes, partner overflow, and media releases. 2,000+ AI projects delivered and 1,000+ brands served since 2015.
- Certifications
- ISO 9001:2015 quality management, ISO 27001:2022 information security, and ISO 17100 translation services — scoped to translation specifically, and stated that way rather than implied across every line.
- Commercial basis
- Quoted in the unit the work is measured in — per word, per audio hour, per approved hour, per finished minute, per batch, per item — with what the unit includes stated alongside it, whether the quote is for you or for a client you quote onward.
Need this against your own template? Convert your scope between units and check the deadline, then send the brief with your language list, content type, volume and deadline, and the acceptance criteria you will judge the output against — those four decide feasibility, and the reply addresses them directly.