The AI data platform built for Gen AI teams
Encord gives GenAI and LLM teams the infrastructure to run human eval pipelines, preference ranking, RLHF workflows, and ground-truth creation at scale, with full auditability.
Yutori builds autonomous web agents that complete complex tasks in real browsers. To push accuracy beyond foundation models, they needed a human-in-the-loop pipeline for training data and large-scale agent evaluation, with error categories that evolved weekly as model behavior changed.
Read the full case studyCustom error categories, iteratively refined weekly
of weekly trajectory evaluations conducted per week at scale
Accuracy uplift over leading foundation models
Why leading Gen AI teams are switching to Encord
Most annotation tools were built for bounding boxes. Encord was built for the full GenAI data lifecycle, from curation to RLHF to deployment monitoring.
One platform for the full eval lifecycle
Data curation, annotation, RLHF workflows, quality control, and analytics all live in one platform. No tool switching, no data loss between stages, no maintenance overhead for your engineers.
One platform for the full eval lifecycle
Data curation, annotation, RLHF workflows, quality control, and analytics all live in one platform. No tool switching, no data loss between stages, no maintenance overhead for your engineers.
Custom model evaluation UI
Encord's no-code UI editor lets you build custom evaluation interfaces, like side-by-side comparisons, ranking tasks, rubric scoring forms, and more, without writing a single line of code.
Scale to 500 annotators without rebuilding your pipeline
Internal reviewers and Encord's managed GenAI annotation teams all operate inside the same platform. Consensus and quality sampling are tracked in real time.
Agentic workflows with human-in-the-loop routing
Deploy any foundation model into your annotation pipeline, including as an LLM judge that scores or ranks model outputs. Route low-confidence predictions, model-graded disputes, or annotator disagreements automatically to human review.
Agentic workflows with human-in-the-loop routing
Deploy any foundation model into your annotation pipeline, including as an LLM judge that scores or ranks model outputs. Route low-confidence predictions, model-graded disputes, or annotator disagreements automatically to human review.
Bring your subject matter experts into the loop
Route specific tasks directly to domain experts so specialist judgment is captured at scale, with the same quality controls and audit trails as the rest of your pipeline.
Bring your subject matter experts into the loop
Route specific tasks directly to domain experts so specialist judgment is captured at scale, with the same quality controls and audit trails as the rest of your pipeline.
Enterprise-grade deployment without compromising
Full VPC deployment keeps all data inside your cloud environment. SOC 2 Type II, role-based access control, annotator data isolation, and full audit trails on every label action, meeting the security bar for the most regulated GenAI teams.
Enterprise-grade deployment without compromising
Full VPC deployment keeps all data inside your cloud environment. SOC 2 Type II, role-based access control, annotator data isolation, and full audit trails on every label action, meeting the security bar for the most regulated GenAI teams.
A platform for every modality your model touches
If you're building video generation models, voice AI, VLMs, or any multimodal GenAI system, you end up managing separate tools per modality. Encord supports text, audio, image, and video natively with purpose-built editors for each.
A platform for every modality your model touches
If you're building video generation models, voice AI, VLMs, or any multimodal GenAI system, you end up managing separate tools per modality. Encord supports text, audio, image, and video natively with purpose-built editors for each.
Additional resources for Generative AI teams
Frequently asked questions
Most teams are running their first live project within 48-72 hours of onboarding. Encord provides a dedicated onboarding engineer who helps configure your ontologies, connect your cloud storage (S3, GCS, or Azure), and set up your first workflow. For VPC deployments, setup typically takes 1-2 weeks depending on your infosec review process. A proof of concept can be scoped and started in parallel with legal and procurement review so no time is lost.
Encord's team handles migration end-to-end. Your existing labels, ontologies, and project structures can be exported from the other tool and re-imported into Encord. No annotation work is lost. Most teams run a parallel POC for 2-4 weeks before fully cutting over, which also lets annotators ramp up on the new interface before go-live. Encord's SDK makes it straightforward to replicate any existing pipeline logic programmatically.
You have full flexibility. Encord supports three workforce models: your internal reviewers, Encord's managed annotation teams (including GenAI-specialist annotators), and third-party crowdworkers like Prolific. You can mix and match per project: use internal staff for sensitive model outputs and Encord's team for high-volume preference labeling, for example.
Encord offers full VPC deployment where your data never leaves your cloud environment. Annotators access tasks through the Encord interface but your raw data stays in your own S3 or GCS bucket, never touching Encord's infrastructure. Encord is SOC 2 Type II certified, supports role-based access control down to the individual task level, and provides a complete audit trail of every annotator action. HIPAA and GDPR compliance documentation is available on request.
Yes. Encord supports multi-turn conversation annotation natively. Annotators can label individual turns, rate full conversations, flag specific responses, or compare outputs side-by-side within a single task. This covers instruction fine-tuning datasets, RLHF preference pairs, and safety labeling workflows. Text, HTML, and structured document formats are all supported in the same annotation editor.
Yes, multimodality is core to Encord. Text, audio, image, and video are all supported natively within the same platform, with purpose-built annotation editors for each. This matters for GenAI teams running VLM evals, audio quality assessments, and text preference labeling within the same sprint. You don't need to move data between tools or maintain separate pipelines per modality.
Encord is built to plug into your existing stack rather than replace it. Cloud storage integrations (S3, GCS, Azure Blob) are native. The Encord Python SDK lets you programmatically ingest data, trigger annotation jobs, and export labeled data in any format (JSONL, CSV, Parquet) directly into your retraining or reward model pipeline. Webhooks and the API support event-driven workflows so your MLOps tooling can trigger Encord jobs automatically when new model outputs are ready for review.