Data Labeling Outsourcing | AI Data Preparation

Last updated: November 11, 2026

AI models are only as good as the data they learn from. Inconsistent bounding boxes, ambiguous text labels or unchecked LLM preference data quietly cap model accuracy, and an engineering team that spends its days labelling is not building product. With data labelling and AI data preparation outsourcing, a 360 Soft team annotates image, video, text, audio and LLM data according to your guidelines, with multi-stage quality assurance, measurable accuracy and secure handling. Teams scale from one annotator to 200+ people, work remotely, on-site or hybrid, and typically start within 1–2 weeks. See all outsourcing services for the wider catalogue.

Data Labeling and AI Data Preparation Outsourcing

What is data labelling and who needs it?

Data labelling (annotation) is the process of adding structured information to raw data so that machine learning models can learn from it: drawing boxes around objects, tagging entities in text, transcribing speech, rating chatbot answers or classifying documents. AI data preparation also includes collecting, cleaning, de-duplicating, anonymising and splitting datasets, and building evaluation sets to test models. Typical clients are AI start-ups and product teams, computer-vision companies in retail, logistics, manufacturing, agriculture and mobility, companies fine-tuning LLMs or building retrieval-augmented assistants, healthcare and insurance teams digitising documents, and research groups that need consistent, well-documented datasets. Turkish, English and Arabic language data are a particular strength of an Istanbul-based team.

Typical problems teams face

Engineers and data scientists spend too much time labelling instead of modelling
Crowd platforms deliver fast but inconsistent labels, with little control over who sees the data
Guidelines are unclear, so labels drift between annotators and over time
No reliable measure of label quality or inter-annotator agreement
Sensitive data (faces, health, documents) cannot be sent to anonymous workers
Volumes fluctuate: big batches before a model release, then quiet periods

Labelling types and scope

Data type	Typical tasks
Image	Classification, bounding boxes, polygons, semantic and instance segmentation, keypoints
Video	Object tracking, event and action tagging, frame classification
Text	Classification, named entity recognition, sentiment, intent, relation extraction
Documents	OCR correction, key-value extraction, table annotation, document classification
Audio	Transcription, speaker diarisation, intent and emotion tagging
LLM data	Prompt and response writing, preference ranking (RLHF/DPO), red-teaming, evaluation sets, RAG relevance judgements
3D / LiDAR	Cuboids, point-cloud segmentation (scope agreed per project)

We work in your preferred tools, such as Label Studio, CVAT, Labelbox, SuperAnnotate, Encord or in-house platforms, and can set up an open-source tool in your environment if you do not have one.

How quality assurance works

Guidelines and gold set: written instructions with edge cases, plus a reference set labelled by experts.
Qualification: annotators pass a test against the gold set before working on production data.
Multi-stage review: label, review and, for critical classes, a second independent review.
Agreement metrics: inter-annotator agreement (e.g. Cohen’s kappa, IoU for boxes) tracked per batch.
Feedback loop: disputed cases go back into the guidelines; weekly calibration sessions keep labels consistent.

Regulation and data governance

Training data often contains personal data such as faces, voices, names or health information. Our team operates under Turkey’s KVKK; for international clients we sign a GDPR DPA with Standard Contractual Clauses (or the UK IDTA). We support data minimisation, anonymisation and redaction before labelling, and document dataset provenance and labelling methods, which helps with transparency and documentation obligations under the EU AI Act. Copyright and licensing of source data remain your responsibility; local rules in your country apply in addition.

Delivery and service models

Model	How it works	Best for
Dedicated team	Named annotators and reviewers trained on your domain	Continuous labelling, complex guidelines
Batch projects	Defined dataset, deadline and quality target	One-off datasets, evaluation sets
Flexible pool	Core team plus trained reserve for peaks	Fluctuating volumes before releases
On-site / secure room	Work in a controlled environment or your VDI	Highly sensitive data

Process steps and typical timelines

Scoping (2–5 days): data types, volumes, classes, tools, quality targets and security needs.
Pilot (about 1–2 weeks): guidelines drafted, a sample labelled, agreement measured, guidelines refined.
Team ramp-up (1–2 weeks): annotator selection, qualification tests and tool access.
Production: batches delivered on schedule with QA reports.
Iteration: guideline updates as your model and classes evolve, active-learning queues where useful.

Illustrative scenario

Illustrative scenario: a retail-tech company needs shelf images labelled with product bounding boxes. After a pilot on 500 images, guidelines are refined for occluded and partly visible products, a gold set is fixed, and a team of annotators and reviewers labels weekly batches in the client’s CVAT instance, with IoU and error-type reports per batch. This is an example of a possible set-up, not a reported client result.

SLA and KPI table

KPI	What it measures	How targets are set
Label accuracy	Share of labels matching gold or reviewer decision	Agreed per class after pilot
Inter-annotator agreement	Consistency between annotators	Kappa or IoU thresholds per task
Throughput	Items labelled per day or week	Based on pilot measurements
On-time delivery	Batches delivered by deadline	Per batch plan
Rework rate	Items returned after client review	Tracked and reduced over time
Security incidents	Data handling breaches	Zero tolerance, logged access

Pricing factors

Factor	Impact on cost
Task complexity	Segmentation and LLM ranking take longer than classification
Domain expertise	Medical, legal or technical data needs specialist annotators
Quality level	More review stages increase effort
Volume and continuity	Steady volumes allow more efficient teams
Languages	Turkish, English, Arabic or other languages
Security requirements	VDI, secure rooms or on-site work

Pricing can be per hour, per item or per dedicated team; we propose the model after the pilot, when effort per item is known. We do not publish fixed prices.

Data Labeling and AI Data Preparation Outsourcing — overview

In-house team, crowd platforms or outsourcing?

Criteria	In-house	Crowd platforms	Outsourcing (360 Soft)
Quality control	High but costly	Variable	Multi-stage QA, agreement metrics
Data security	Full control	Anonymous workers	Named, vetted staff, NDA, DPA
Scalability	Slow	Very high	1 to 200+ people, typically 1–2 weeks
Domain knowledge	High	Low	Builds up in a dedicated team

Risks and mitigations

Label drift: calibration sessions, gold-set checks in every batch.
Ambiguous guidelines: pilot phase and a living edge-case log.
Bias in labels: diverse review, documented decisions, bias checks on sensitive classes.
Data leakage: no downloads, VDI or your tool only, logged access.
Missed deadlines: capacity planning and a trained reserve pool.

Security, data protection, NDA and IP

Annotators work in your tool or a secured environment with named accounts and MFA; data is not stored on personal devices. Everyone signs confidentiality undertakings. Labels, guidelines, gold sets and derived datasets belong to you.

A note on the contract model

We work under a B2B service agreement with a statement of work, SLA annex and change procedure. Intellectual property in deliverables (rules, reports, code, designs, documentation) is assigned to you on payment, and a mutual NDA is signed before any data is shared. Where we process personal data on your behalf, we sign a GDPR-compliant data processing agreement (DPA) with Standard Contractual Clauses, or the UK IDTA/Addendum for UK clients, and we operate under Turkey’s KVKK. Team members are employed by 360 Soft under Turkish labour law; we remain responsible for their management and replacement. This is general information, not legal advice; please have your counsel review the final contract.

Vendor selection checklist

Does the vendor run a pilot and measure agreement before scaling?
Are annotators named, vetted and bound by NDA?
Can work be done inside your tool or VDI without data downloads?
Are quality metrics reported per batch?
Is a GDPR DPA with SCCs and KVKK compliance in place?
Can the team handle your languages and domain?
How are peaks and staff changes handled?

Frequently asked questions

Which data types can you label?

Image, video, text, documents, audio and LLM data, and 3D/LiDAR data scoped per project.

Do you work with LLM and RLHF data?

Yes. We write prompts and responses, rank model outputs, build evaluation sets and judge retrieval relevance according to your rubrics.

Which annotation tools do you use?

Your existing tool, such as Label Studio, CVAT, Labelbox or an in-house platform, or an open-source tool we set up in your environment.

How do you measure quality?

With gold-set checks, multi-stage reviews and agreement metrics such as Cohen’s kappa or IoU, reported per batch.

Is a pilot required?

We strongly recommend one. It clarifies guidelines, measures effort per item and sets realistic quality targets.

Can you handle sensitive or personal data?

Yes, with anonymisation where possible, work in secure environments, named staff, NDA and a GDPR DPA with SCCs.

Which languages do you support?

Turkish, English and Arabic are core strengths; other languages can be scoped on request.

Who owns the labelled data?

You do. All labels, guidelines and datasets are assigned to you.

Can you scale quickly for a release?

Yes. Teams range from one annotator to 200+ people, with a trained reserve for peaks.

How quickly can a team start?

Typically within 1–2 weeks, often with a pilot running in the first week.

Let us plan your labelling pilot

Tell us about your data labelling and AI data preparation needs and we will come back with a scoped proposal, a team plan and a realistic start date (typically 1–2 weeks). Call +90 850 335 10 87, message us on WhatsApp, email info@360-soft.com or use the contact page. 360 Soft Marketing Yazılım ve Danışmanlık Ltd. Şti., Nidakule Ataşehir Batı, Barbaros Mah. Begonya Sk. No:1 D:2, 34746 Ataşehir/İstanbul.

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Data Labeling and AI Data Preparation Outsourcing