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Data annotation & labeling services from Nairobi, KenyaRaw data in.
Ground truth out.

InitLabel turns your unlabeled images, video, LiDAR, text and audio into clean, reviewed AI training data. A trained human team works to your guidelines, every batch passes two review layers, and one project lead knows your data.

Request a quoteTry a pilot batch
  • No commitment to start
  • NDA-signed annotators
  • You own every label
street_market_0087.jpgLabeled view
Street scene shown raw and after data annotationA city street with a motorcycle rider, a pedestrian, a traffic light and a tree. In the labeled half, each object has a bounding box and class name, and the road is segmented.motorcycleriderpedestriantraffic_lighttreeroad · maskRAWLABELED
Drag the slider to see a delivered frame: boxes, classes and a road mask, each reviewed twice.
1 business dayto hear back with a plan, timeline and price
2+ review layerson every batch before it reaches you
Pilot phasetry a pilot, so you judge real output first
UTC+3Nairobi hours that overlap Europe and the Gulf

A model believes everything you show it.

One wrong label is a typo. The same mistake repeated across fifty thousand images is a lesson, and your model learns it perfectly. That is why most AI projects that stall don’t have a modeling problem. They have a training data problem.

We fix it at the source. Our annotators learn your edge cases before they touch production data, and every label is checked by a second person before it ships. You spend your time improving the model, not auditing the dataset.

Test us on your hardest dataHow our QA works
horse ✗
Before reviewWrong class, loose box. Multiply by every similar image in the set.
zebra ✓
After QA sign-offCorrect class, tight box, and a guideline note so it never repeats.

If your model can learn from it, we can label it.

Pick a data type to see formats, tools, use cases and rates. Not sure what your project needs? Send a sample and we’ll recommend the approach.

All data annotation services

Beyond labeling, the same managed teams run

From first call to first delivery in days, not months.

Every project follows the same path, so you always know what happens next and who is accountable.

  1. Scope the work

    We review your data, guidelines and goals, then send a quote with volumes, timelines and quality targets.

  2. Run a pilot

    Data labeled to your spec and scored against your gold set, so you see real output first.

  3. Train and certify the team

    Annotators study your edge cases and pass a gold-set test before production access.

  4. Label and review

    Work moves through labeling, peer review and QA audit, with progress you can check any time.

  5. Deliver and improve

    Batches arrive in COCO, YOLO, JSON or your schema. Feedback goes straight into the guidelines.

Read the full six-step process

Your evening hand-off is our morning start.

Nairobi runs on UTC+3 all year, which puts our working day right alongside yours in Europe and the Gulf, so questions about edge cases get answered while you are still at your desk.

Working from the US East Coast? Upload at the end of your day and a full Nairobi shift is finished before your 9 a.m. stand-up. Extended shifts can be scoped into your quote.

More on working hours and coverage

NairobiEAT · 8–5
InitLabel
DubaiUTC+4 · 9–6
9 h
BerlinUTC+1 · 9–5
6 h
LondonUTC · 9–5
5 h
New YorkUTC−5 · 9–5
Hand-off
Grey bars show a typical working day in each city; the dashed outline marks InitLabel’s core hours. The right column shows hours of live overlap. Standard time shown; daylight saving shifts these by an hour.

Teams putting AI into the real world.

All industries we serve

Trained people, not an anonymous crowd.

Every label you receive is placed by a trained, managed professional in Kenya. People who stay on your project learn your data better each week, and that consistency shows up in your metrics.

Kenya’s English-fluent, tech-savvy talent pool gives you that quality at a fraction of onshore cost, and every engagement creates skilled digital work close to home.

Why KenyaMeet InitLabel
Illustration of InitLabel annotators working at screens showing labeled imagesvision · review 2/2keypoints · QANER · Swahiliteam online

Good questions to ask first

What is data annotation?

Data annotation is the work of adding labels to raw data, such as drawing boxes around objects in images, tagging entities in text or transcribing audio, so a machine learning model can learn what to look for. The accuracy of those labels sets the ceiling on how well your model can perform.

Why outsource data labeling instead of doing it in-house?

Labeling is slow, repetitive and hard to staff for peaks. A managed data annotation partner gives you a trained team that scales with your volumes, built-in quality review, and engineers who stay focused on the model instead of the labeling queue.

Why work with a data annotation company in Kenya?

Kenya offers a large, English-fluent, university-educated workforce, strong digital infrastructure and a UTC+3 time zone that overlaps Europe and the Middle East, at a lower cost than onshore teams. Read more on why Kenya.

How much does data annotation cost?

It depends on the task. Contact us for cost estimation.

What do you need from us to send a quote?

A short description of your project, a small sample of your data, your labeling guidelines if you have them, and rough volumes and deadlines. That is enough for a project lead to reply within one business day. Send your brief.

Can we test you before signing a contract?

Yes. You can start with a short-term pilot, scored against your gold set, with results in about five business days. See how a pilot runs.

Your next model deserves better data.

Send us a sample and a few lines about what you’re building. We’ll label a pilot batch to your spec, so you can measure the difference before you spend a a dollar.

Request a quote