Case study
Twenty thousand prompts across 50 languages in an accelerated evaluation sprint.
A model team needed 20,000 prompts evaluated across 50 languages under a compressed decision window, where the fine-tuning decision could not wait on a slow evaluation bench.
50 - ~20,000 prompts - project-scoped quality review
50
Languages
~20,000 prompts
Volume
Measured outcomes
LLM fine-tuning evaluation
Project overview
What landed, and what made it hard.
A model team needed 20,000 prompts evaluated across 50 languages under a compressed decision window, with five evaluators per language working in parallel.
Delivery snapshot
LLM fine-tuning evaluation
- Client
- An AI model team
- Service
- Multilingual model evaluation
- Languages
- 50 languages
- Volume
- ~20,000 prompts
- Quality
- project-scoped quality review
Why this mattered
Outcome before process.
Evaluation at this speed is a sourcing and calibration problem: 50 languages cannot ramp sequentially, and compressed decision windows leave no room to re-train evaluators mid-sprint.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
A compressed 50-language evaluation fails if calibration is uneven across languages, if any language track lags, or if quality is traded for speed under the deadline.
The challenge
The problem to solve
The team needed all 50 languages evaluated to one standard inside the sprint, not a fast average that hid weak language tracks.
Operating response
What MoniSa changed
MoniSa sourced five calibrated evaluators per language and ran all 50 tracks in parallel against a shared rating framework, with quality checks through the sprint.
- Parallel sourcing Five evaluators per language ran simultaneously so no track waited on another.
- Pre-calibration Evaluators were calibrated against the rating framework before the sprint started, not during it.
- In-sprint checks Quality was monitored through the sprint so speed did not quietly trade against accuracy.
Results
Measured outcomes from this engagement.
The team received ~20,000 prompt evaluations across 50 languages during the accelerated sprint at project-scoped quality review, with every language held to the same standard.
| Languages | 50 |
|---|---|
| Volume | ~20,000 prompts |
| Quality | project-scoped quality review |
| Timeline | Compressed sprint |
| Team | 5 evaluators per language |
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
A compressed 50-language sprint needs parallel pre-calibrated sourcing, not a bench that ramps languages one at a time.
What decided the result
What decided the result
Holding all 50 languages to one standard inside the sprint mattered more than a fast average.
What buyers can reuse
What buyers can reuse
- An accelerated multilingual evaluation is a sourcing and calibration problem solved before the sprint, not during it.
- Speed is only useful if every language track holds the standard, not if a fast average hides weak ones.
- 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.
- 50-language coverage
- Parallel evaluation tracks
- Calibrated rating framework
More proof
Related proof
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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?
Languages: 50. Volume: ~20,000 prompts. Quality: project-scoped quality review
What control kept the work stable?
Holding all 50 languages to one standard inside the sprint mattered more than a fast average.
Where should similar work go next?
Use AI and ML buyer lane 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.