Resources
Resources
Research-backed guides, whitepapers, and definitions for frontier alignment, agents, multimodal AI, speech, physical AI, evaluation, data quality, and governance.
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Featured guides
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Research-backed guidance for designing, sourcing, validating, governing, and evaluating the data behind frontier models, agents, multimodal systems, speech AI, and physical intelligence.
AI data decisions are no longer limited to annotation volume or unit price. Technical buyers need to understand whether a dataset is fit for a model objective, whether its source and transformations are traceable, whether the review system measures the right defects, whether a private benchmark can support a release decision, and whether the entire workflow remains secure and governable as the product changes.
This resource library is organized around those decisions. The guides explain how a modern AI data program works. The whitepapers provide operating frameworks and scorecards for enterprise discovery, pilots, and production governance. The glossary defines technical terms in the context of real data operations rather than reducing them to one-line marketing definitions.
Featured Guides
Guide to Frontier Alignment Data
A practical introduction to supervised fine-tuning data, preference data, critiques, reward signals, process supervision, expert review, red teaming, and post-training evaluation. The guide explains why alignment data should be designed around observable behavior and defensible evidence rather than unsupported claims about a model’s private reasoning.
Guide to Agentic AI Data
How to create task environments, golden trajectories, tool-use traces, policy constraints, recovery examples, and trace-aware evaluations for agents that operate software, browsers, APIs, and enterprise workflows. It covers environment reproducibility, permissions, state transitions, side effects, and long-horizon reliability.
Guide to Multimodal Data Pipelines
A source-to-release workflow for image, video, document, screen, audio, and text data. The guide addresses cross-modal alignment, temporal annotation, grounding, OCR and layout, multimodal hallucination evaluation, metadata, privacy, and quality controls that operate across modalities.
Guide to Physical AI & Robotics Data
A technical guide to task design, teleoperation, robot demonstrations, RGB-D, LiDAR, force and torque, joint state, native actions, synchronization, coordinate frames, calibration, episode completeness, source mixing, and closed-loop evaluation for embodied AI.
Guide to Model Evaluation
How to turn an intended product behavior into a versioned evaluation program using scenarios, protected splits, deterministic checks, human review, model-based graders, slice analysis, adversarial tests, uncertainty reporting, and release decisions.
Guide to AI Data Quality
A lifecycle view of quality from source intake through curation, annotation, validation, release, model impact, and drift. It explains why a single acceptance-rate number cannot replace property-specific metrics, source distribution, defect severity, and downstream evaluation.
Guide to Human-in-the-Loop Evaluation
How to decide which judgments require people, who is qualified to make them, how to calibrate reviewers, when to adjudicate, how to validate model-assisted review, and how to protect both data quality and contributor welfare.
Decision Frameworks and Whitepapers
Enterprise AI Data Readiness Checklist
A scored 0–4 maturity model across use-case definition, source rights, schema, quality, workforce, security, evaluation, integration, and commercial governance. Use it to determine whether a program is ready for discovery, a bounded pilot, production scale, or continuous iteration.
Physical AI Data Quality Framework
An episode-level scorecard for task validity, sensor health, synchronization, calibration, completeness, action and outcome integrity, coverage, and lineage. It defines release classes for training, evaluation, recovery, representation learning, and quarantine.
Private Benchmark Design for AI Teams
A framework for private capability, reliability, safety, policy, and agentic evaluations. It covers benchmark contracts, protected partitions, evidence-backed items, grader validation, contamination response, trace-aware testing, statistics, refresh, and launch gates.
Data Governance for Foundation Model Builders
A source-to-release governance system for pre-training, post-training, evaluation, retrieval, synthetic, multimodal, customer, and production-interaction data. It connects provenance, rights, quality, privacy, security, vendors, deletion, and documentation.
Browse by Technical Problem
I need to improve or specialize model behavior
Start with the Frontier Alignment Data guide, then use the AI Data Quality guide to define release criteria and the Model Evaluation guide to establish a protected holdout. Teams working with specialist domains should also review the human-in-the-loop qualification and adjudication model.
I need an agent to complete real workflows safely
Start with Agentic AI Data. Pair it with Private Benchmark Design so success, policy adherence, permissions, trajectory quality, recovery, and side effects are measured independently. Security teams should map tests to current agentic threat models rather than relying on final-answer checks.
I need image, video, document, audio, or screen data
Use Multimodal Data Pipelines to define the atomic record, alignment, temporal structure, grounding, and review. For speech-specific collection, annotation, or evaluation, also review the Speech & Audio Data product page.
I need robotics demonstrations or sensor data
Use the Physical AI & Robotics Data guide for program design and the Physical AI Data Quality Framework for acceptance. The framework is especially useful during pilot scoping because it makes clock, calibration, native action, intervention, terminal state, and transfer evidence contractual rather than implicit.
I need evidence for a model or product release
Use the Model Evaluation guide for method selection and Private Benchmark Design for the operating model. Evaluation should reflect the complete system boundary—including retrieval, tools, prompts, policies, and environment—when those components affect behavior.
I need to pass enterprise security and governance review
Start with the Enterprise AI Data Readiness Checklist and Data Governance for Foundation Model Builders. These resources help separate rights, privacy, security, quality, safety, and release approvals so one team’s decision does not silently substitute for another’s.
Technical Glossary
The glossary explains common terms as operational artifacts, including:
- RLHF, SFT, and DPO;
- Agentic AI, Tool-Use Trajectory, and Golden Trajectory;
- Multimodal Data, VLM, and VLA;
- Sensor Fusion, LiDAR Annotation, MCAP, and ROS Bag;
- Data Curation, Inter-Annotator Agreement, Red Teaming, and Model Integrity.
Work With a Data Expert
A strong data program begins with the model behavior, workflow, risk, and release decision—not with a generic volume estimate. Share the system you are building, the data you already have, the gaps you can observe, the quality evidence you need, and any security or governance constraints. We can scope a representative pilot and define what must be proven before production scale.