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 - reviewed quality
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
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
- reviewed quality
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
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 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 guidelinesObject detection, sentiment, and NER each had their own annotation rules and acceptance examples.
- Task-specific reviewReviewers tracked consistency within each task type, not a blended average.
- Window disciplineWork 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 reviewed quality, each task held to its own standard within six weeks.
| Volume | 967 hours |
|---|---|
| Task types | Object detection, sentiment, NER |
| Quality | reviewed quality |
| Duration | ~6 weeks |
Selection logic
What protected the result.
Multi-type annotation needs per-task guidelines and review, not one blended standard stretched across three jobs.
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.
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Mapped context
Service and buyer context
Languages named
Examples referenced in the engagement.
- Object detection labeling
- Sentiment analysis
- Named-entity recognition
More proof
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Buyer questions
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What was delivered on this engagement?
Volume: 967 hours. Task types: Object detection, sentiment, NER. Quality: reviewed quality
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?
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Production-ready brief
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