967 hours of annotation across three task types
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 per engagement rules
110,000+
native linguists and AI data contributors
Founder-reported combined network · 4 Oct 2026
300+
languages across active service lines
4,500+
dialects and regional variants
1,000+
organizations served since 2015
Measured outcomes
Multi-type annotation
Multi-type annotation
- Client
- An AI company
- Service
- Text and image annotation
- Volume
- 967 hours
- Task types
- Object detection, sentiment, NER
- Quality
- Reviewed per engagement rules
An AI company needed 967 hours of annotation spanning object detection, sentiment analysis, and named-entity recognition, delivered within a six-week window.
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
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.
How AI data services was delivered
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.
What was delivered on this engagement?
The company received 967 hours of annotation across object detection, sentiment, and named-entity recognition under project-scoped review, each task held to its own standard within six weeks.
| Volume | 967 hours |
|---|---|
| Task types | Object detection, sentiment, NER |
| Quality | Reviewed per engagement rules |
| Duration | ~6 weeks |
What supported the result
Why this team fit the assignment
Multi-type annotation needs per-task guidelines and review, not one blended standard stretched across three jobs.
What decided the result
Holding each task type to its own standard mattered more than a single headline accuracy number.
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.
Work connected to Multi-type annotation
Related services, buying guidance and language coverage
AI data services and buyer guidance
Languages listed
- Object detection labeling
- Sentiment analysis
- Named-entity recognition
More proof
Compare this case with adjacent MoniSa proof before deciding whether the operating pattern fits your brief.
What changed in the work
Not the result — the decision taken mid-programme that the result depended on.
Three task types, three rulebooks
Object detection, sentiment and NER each had their own rules, examples and reviewers. Consistency was tracked per task, because a single accuracy number across three tasks hides which one is drifting.
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Multi-type annotation questions
What was delivered on this engagement?
Volume: 967 hours. Task types: Object detection, sentiment, NER. Quality: Reviewed per engagement rules
Which control supported the Multi-type annotation result?
Holding each task type to its own standard mattered more than a single headline accuracy number.
Scope work like Multi-type annotation
For a comparable project, include the language mix, review requirements, deadline and evidence your team needs.
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