AI Data Infrastructure

Production-Grade Training Data for AI Systems That Ship

We operate human feedback pipelines and annotation workflows that move AI projects from prototype to production. Specialized in RLHF, Computer Vision, and native Turkish language data — with the QA rigor enterprise teams require.

Built for production AI systems.

What We Operate

Production-Grade Data Pipelines for AI Systems That Ship

LLM Fine-Tuning & RLHF

We operate structured human feedback pipelines for instruction tuning, response ranking, and safety alignment.

Each annotator is trained against project-specific guidelines, with outputs continuously evaluated through multi-layer QA checkpoints to ensure consistency and model-ready data.

Computer Vision Annotation

High-precision bounding boxes, polygons, keypoints, and semantic segmentation delivered with pixel-level accuracy.

Designed for autonomous systems, robotics, and industrial inspection workflows where annotation quality directly impacts system performance.

Audio & Speech Data

End-to-end transcription, speaker diarization, and audio event classification for ASR and voice AI systems.

Native Turkish datasets delivered with timestamp-level accuracy, reviewer consensus, and multi-tier quality validation.

Supporting AI teams across fintech, autonomous systems, and conversational AI.

Enterprise-scale annotation workflows
Multi-stage QA checkpoints
Native-language expert teams

How We Work

From Requirements to Production-Ready Data

A structured engagement model designed for teams deploying AI systems in production.

01

Requirement Alignment

We define data scope, annotation schemas, and quality benchmarks upfront.

Project guidelines are finalized before annotation begins to ensure clarity, consistency, and measurable outcomes.

02

Pilot Dataset Delivery

A controlled pilot dataset validates alignment across requirements, guidelines, and expected output quality.

Your team reviews the results. We iterate until standards are met.

03

QA Validation

Multi-layer quality assurance ensures accuracy and consistency at scale.

Inter-annotator agreement is continuously monitored, and edge cases are documented to refine guidelines and reduce variance.

04

Production Scaling

Once quality is validated, we scale throughput without compromising accuracy.

Capacity adjusts to your roadmap while maintaining stable performance and QA standards.

Security & Compliance

NDA-bound annotation teams
Secure data handling workflows
Access-controlled production environments

Why Anatolia Data

AI Infrastructure — Not Just Annotation

The difference between experimental datasets and production-ready AI systems.

Native Turkish Linguistic Operations

All Turkish-language data operations are performed by native speakers with regional and cultural fluency.

No offshore routing. No machine pre-labeling. Every dataset reflects real-world language usage, context, and intent.

Security-First Operations

All team members operate under strict NDAs within secure, access-controlled environments.

Data handling follows privacy-first workflows, with support for custom security and compliance requirements when needed.

Pilot-to-Production Scalability

Engagements begin with a focused pilot and scale seamlessly to production volumes.

Our workforce model supports elastic throughput without re-onboarding, process resets, or quality drift.

Multi-Layer Quality Assurance

Quality is enforced by default, not retroactively. Each task passes through tiered review — annotator, senior reviewer, and audit sampling.

Inter-annotator agreement is continuously tracked, and edge cases are documented and escalated.

Operational Workflows

Designed for consistency, traceability, and model readiness.

Model-agnostic pipelines

Custom annotation schemas

Validation checkpoints

Reviewer consensus

Continuous monitoring

Dataset consistency controls

Trusted at Scale

Supporting teams building production AI systems

We operate data pipelines for teams shipping real-world AI across language, vision, and speech.

Operational Signals

  • Rigorous multi-layer verification processes achieving 98%+ accuracy across complex datasets, ensuring noise-free training data

  • Native-language annotation teams trained for domain-specific guidelines

  • Multi-stage QA workflows with reviewer consensus and audit sampling

  • Pilot-to-production delivery model trusted by technical and procurement stakeholders

Industries We Support

Our work supports AI systems deployed in:

  • Conversational and generative AI

  • Autonomous and computer vision systems

  • Fintech and risk intelligence

  • Enterprise speech and voice interfaces

(Client names are protected under NDA.)

What Teams Rely On

  • Consistent output quality across large volumes

  • Clear escalation paths for edge cases

  • Stable throughput aligned with product roadmaps

  • Data that is ready for training — not cleanup

Built for production AI systems.

Project Scoping

Let's Build Your Data Pipeline

Ready to Scale Your AI?

Our solution architects are ready to design a custom data pipeline for your specific model requirements.

Headquarters

Central Anatolia, Türkiye

Response Time

Within 24 hours

Confidential

Operated by Native Turkish Teams under strict NDA.

We typically respond within 24 hours with a detailed scope assessment.