When to use it
When a model reads well in English but its outputs in rare, low-resource, and culturally distinct languages need native judgment on accuracy, safety, and tone before release.
Prompt Evaluation service
Prompt Evaluation Services for work where a model reads well in English but its outputs in rare, low-resource, and culturally distinct languages need native judgment on accuracy, safety, and tone before release.
LLM prompt creation, model output evaluation, and RLHF-style human feedback with native-speaker judgment in the languages your model actually serves: rating, ranking, and reviewing responses against a written rubric.
confidential evaluation records show a written rubric, independent raters, and senior adjudication on disputed scores across multiple languages, including pairs few vendors can staff with qualified native raters.
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
Scope dossier
Prompt Evaluation service fit confidential evaluation records show a written rubric, independent raters, and senior adjudication on disputed scores across multiple languages, including pairs few vendors can staff with qualified native raters.- Typical inputs
- Model outputs or prompt-response pairs, a scoring rubric, rating scale, safety policy, target languages, gold examples
- Controls
- Rubric calibration, independent raters, IAA on a pilot, disagreement adjudication, senior escalation
- Best fit
- Prompt evaluation, LLM evaluation, RLHF data, response rating and ranking, multilingual safety and toxicity review
Service signal
Pick the service by the result at risk.
Buyers can see the result, review depth, and file-shape fit before they compare vendors line by line.
Strongest fit
Prompt evaluation, LLM evaluation, RLHF data, response rating and ranking, multilingual safety and toxicity review
How the work runs
Rubric calibration round, then scored batches with rater agreement tracked across the run
Who this is for
Each stakeholder sees their risk.
Buyers need to see when the service fits, what can go wrong, and how review reduces rework.
VP Data Ops
Needs language coverage, throughput, and quality controls for multilingual data.
LSP vendor manager
Needs rare-language capacity without exposing the end client.
Media localization lead
Needs subtitle, dubbing, metadata, and QA workflows to meet a release date.
Specification
Lock the details that decide quality.
Use this table to compare inputs, review model, fit, and output before a buying committee asks.
| Typical inputs | Model outputs or prompt-response pairs, a scoring rubric, rating scale, safety policy, target languages, gold examples |
|---|---|
| Review path | Rubric calibration, independent raters, IAA on a pilot, disagreement adjudication, senior escalation |
| Strongest fit | Prompt evaluation, LLM evaluation, RLHF data, response rating and ranking, multilingual safety and toxicity review |
| How the work runs | Rubric calibration round, then scored batches with rater agreement tracked across the run |
Quality method
Quality starts before the first batch moves.
MoniSa uses a three-layer system: pre-production gates, in-production controls, and post-delivery review.
Screen
Profile review, nativity verification, domain questionnaire, screening call, sample task.
Calibrate
Every assigned team works against the same calibration items before production volume starts.
Pilot
The first batch is reviewed deeply so instruction drift is caught before scale.
Review
Sampling, senior review, agreement checks, and same-day feedback loops run during production.
Escalate
Critical errors trigger pause, recalibration, replacement, or operations-lead escalation.
Learn
Client feedback feeds back into resource profiles, glossary rules, and the next batch.
case evidence
Proof that matches prompt evaluation services, not generic language work.
The records below stay close to this delivery model so the proof feels operational, not decorative.
AI data servicesMixed-script Document AI dataset moved through validation.
Document AI OCR annotation
The challenge. A Document AI buyer needed readable, consistently labeled files across scripts and document types.
What we did. MoniSa grouped files by script, validated structural labels, and escalated disagreements.
The result. The buyer received an annotated dataset prepared for Document AI model training.
AI output reviewSafety annotation stabilized across multilingual batches.
Multilingual content safety
Problem. A content-safety team needed consistent risk labeling across languages and cultures.
Action. MoniSa tightened examples, retrained reviewers, and tracked recurring error patterns.
Result. The buyer received a steadier multilingual safety-review workflow with fewer correction cycles.
AI data servicesRolling audio production held together as rare-language scope expanded.
Multilingual audio intelligence
Problem. A speech AI buyer needed continuous multilingual audio throughput while adding hard languages.
Action. MoniSa moved new languages through sourcing, pilot work, training, and review before scale.
Result. The buyer kept a rolling audio-data program moving across a wider language footprint.
AI data servicesPhased audio collection kept training ingestion moving.
Compressed audio collection
Problem. An AI data buyer needed multilingual audio fast without waiting for a single final handoff.
Action. MoniSa split contributors by language, controlled scripts, and delivered phased batches.
Result. The buyer could begin using early datasets while collection continued in parallel.
Continue the evaluation plan
Choose the evaluation type, or connect model review to the data behind it.
Most visitors arrive here holding a specific kind of evaluation. The two routes below scope by evaluation type; the rest carry the work back into the data, annotation, and buyer decisions that make the review reliable.
LLM evaluation services
Score model output against a written rubric, with raters calibrated before production and agreement measured per language.
AI red teaming
Test the model adversarially in each language, with native speakers writing the attacks and rating what comes back.
AI data services overview
Scope the multilingual data and human-review program around the model.
Annotation for multilingual models
Set the labels and rater instructions the evaluation relies on.
AI training data readiness
Prepare the collection and dataset path that feeds model work.
AI/ML buyer lane
Follow the operating path for multilingual model teams.
Buyer questions
Ask the questions weak vendors avoid.
Short answers for buyers checking fit, coverage, quality method, and next-step readiness.
What is prompt evaluation?
Prompt evaluation is human review of how a language model responds: rating answers for accuracy, helpfulness, safety, and tone, ranking competing responses, and flagging failures against a written rubric. MoniSa runs this with native speakers when the outputs are multilingual, since quality judgments differ by language and culture.
What is the difference between prompt evaluation and RLHF data?
Prompt evaluation scores or ranks model outputs against a rubric. RLHF data is the human preference signal, which response is better and why, collected in a structured form a training pipeline can use. MoniSa produces both: rubric-based scoring and preference-style comparisons, with the same calibration discipline.
How does MoniSa keep LLM evaluation consistent between raters?
Each evaluation starts with a calibration round on shared examples. Raters score independently, inter-annotator agreement (IAA) is measured on a pilot, disagreements are adjudicated by a senior reviewer, and the rubric is tightened where raters diverge before the full run proceeds.
Can MoniSa evaluate model outputs in multiple languages?
Yes. Evaluation, prompt creation, and safety review run across 300+ languages and 4,500+ dialects with native-speaker raters. Multilingual safety and toxicity review is scoped to the policy, the languages, and the rater availability for each pair before work begins.
Next step
Send the details that decide the quote.
A useful brief names the language, content, deadline, review depth, and proof the buying team needs.
Production-ready brief
01Language pair, dialect, and script02Content or data type03Volume and deadline04QA and reviewer requirement05Security and access requirement06Proof needed for buyer 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.