Case study
Nine hundred and sixty-seven hours of annotation across three task types in six weeks.
An AI company needed 967 hours of annotation across three different task types in six weeks, where each task carries its own labeling rules and failure modes.
967 hours - Object detection, sentiment, NER - project-scoped quality review
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
Multi-type annotation
Project overview
What landed, and what made it hard.
An AI company needed 967 hours of annotation spanning object detection, sentiment analysis, and named-entity recognition, delivered within a six-week window.
Delivery snapshot
Multi-type annotation
- Client
- An AI company
- Service
- Text and image annotation
- Volume
- 967 hours
- Task types
- Object detection, sentiment, NER
- Quality
- project-scoped quality review
Why this mattered
Outcome before process.
Multi-type annotation is three jobs in one: each task has its own guidelines, edge cases, and consistency traps, and mixing them without per-task control degrades the dataset.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
Annotation across three task types fails when one set of guidelines is stretched across all of them, or when consistency is not tracked per task.
The challenge
The problem to solve
The company needed object detection, sentiment, and NER each held to their own standard within one fast-moving engagement.
Operating response
What MoniSa changed
MoniSa ran each task type with its own guidelines and reviewers, tracking consistency per task across the six-week window.
- Per-task guidelines Object detection, sentiment, and NER each had their own annotation rules and acceptance examples.
- Task-specific review Reviewers tracked consistency within each task type, not a blended average.
- Window discipline Work moved on a schedule that held quality across the six-week deadline.
Results
Measured outcomes from this engagement.
The company received 967 hours of annotation across object detection, sentiment, and named-entity recognition at project-scoped quality review, each task held to its own standard within six weeks.
| Volume | 967 hours |
|---|---|
| Task types | Object detection, sentiment, NER |
| Quality | project-scoped quality review |
| Duration | ~6 weeks |
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
Multi-type annotation needs per-task guidelines and review, not one blended standard stretched across three jobs.
What decided the result
What decided the result
Holding each task type to its own standard mattered more than a single headline accuracy number.
What buyers can reuse
What buyers can reuse
- Multi-type annotation is three jobs: each task needs its own guidelines and consistency tracking.
- A blended quality average hides weak task types; per-task review is what keeps the dataset usable.
- 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.
- Object detection labeling
- Sentiment analysis
- Named-entity recognition
More proof
Related proof
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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: 967 hours. Task types: Object detection, sentiment, NER. Quality: project-scoped quality review
What control kept the work stable?
Holding each task type to its own standard mattered more than a single headline accuracy number.
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.