AI data operations — human evaluation
Human feedback, evaluation and annotation workflows for teams moving models into production — run by trained in-house reviewers under multi-layer QA.
98%+ accuracy
Each capability is run by trained in-house teams against your guidelines — not a marketplace of anonymous labellers bidding on tasks.
Structured human feedback pipelines for instruction tuning, response ranking and safety alignment.
High-precision labelling delivered with pixel-level accuracy, for autonomous systems, robotics and industrial inspection workflows.
End-to-end datasets for ASR and voice AI, delivered with timestamp-level accuracy and reviewer consensus.
Human evaluation programmes that establish where a model actually stands before it reaches users.
Across every workstream
Turkish-language work is performed by native speakers. Native-language teams apply the same operating standard to every language we deliver.
A structured engagement model. You see output quality on real data before you commit to volume.
Engagement flow
Data scope, annotation schemas and quality benchmarks are defined and signed off before any labelling begins.
A controlled batch validates requirements, guidelines and expected output. Your team reviews; we iterate until it lands.
Multi-layer review enforces accuracy at scale before any batch is accepted.
Once quality is validated, throughput scales to your roadmap without re-onboarding, process resets or quality drift.
Quality control 3 tiers
Native-speaker teams, project-specific guidelines
Every task reviewed — not a sample
Independent spot-checks across the set
Tracked across all tiers
Inter-annotator agreement is monitored, and edge cases are documented and escalated rather than absorbed into the batch.
Edge cases refine the guidelines set at stage 01
Batch state Accepted
The difference between experimental datasets and production-ready AI systems.
Native-language operations
Every dataset reflects real-world language use, context and intent.
What this rules out
Workforce model
Engagements start with a focused pilot and scale to production volume. Capacity is elastic because it comes from teams already trained against your guidelines, so scaling does not mean re-onboarding.
Pipeline design
Pipelines are model-agnostic and built around custom annotation schemas with validation checkpoints — designed for consistency, traceability and model readiness.
We operate data pipelines for teams shipping real-world AI across language, vision and speech.
98%+
accuracy
Achieved by multi-layer verification across complex datasets.
What teams rely on
A pilot-to-production delivery model trusted by technical and procurement stakeholders.
Where the work is deployed
Our work supports AI systems deployed in these areas.
Client names protected under NDA
Security
Tell us what you're building. We'll come back with a scope assessment, a proposed pilot, and the QA standard we'd hold it to.
Enquiries are handled by native Turkish teams under strict NDA.
