--- title: "AI Data Collection, Annotation & Evaluation | YPAI" url: https://ypai.ai/data-solutions/ description: "Managed AI data collection, annotation, validation, human evaluation and dataset sourcing across speech, video, image, text, agents and robotics." source: "src/copy/routes (route /data-solutions/)" --- # The data and evaluation work behind AI systems that hold up in production. > Managed AI data collection, annotation, validation, human evaluation and dataset sourcing across speech, video, image, text, agents and robotics. Custom AI data projects built to defined specifications, with quality, rights and acceptance evidence attached to delivery. YPAI runs managed collection, annotation, validation and human evaluation across speech, video, image, text, agents and robotics. Every delivery carries its record. Scope a data or evaluation brief Buy one defined workstream or connect several under one accountable operating model. The work is scoped to your specification and delivered with the quality, rights and acceptance records your team needs. ## Four problems. Four kinds of work. Most AI data work starts as one of four problems. Start with yours. Each is a different kind of work, and one frame runs through all four. - runs through all four kinds of work - with the problem your team is facing now Illustrative specimens. No customer data, record or result is real. 48 kHz · 24-bit · far-field · consent recorded - we would need the delivery by early spring - the window can be held if the volumes stay fixed bare asphalt inside the marking, so the stripe cannot be sampled as painted worn marking, bare asphalt inside the stripe the frame both models were shown - Two cars parked along the kerb; the van is waiting at the crossing. - Three cars parked along the kerb, one van stopped on the right, wet asphalt reducing lane-marking contrast under a single street lamp. - the van is waiting at the crossing the grader counts against the frame response B meets the threshold · response A fails on source use ### Collection - The data you need does not exist. - Speech, video, image, text, sensor and interaction data, collected to your specification in studio, field or in-car, in 150+ languages. Existing data is licensed first; only the gap is collected. - speech, video, image, text, sensor, interaction - studio, field, in-car, remote, moderated, on site - existing data licensed first, only the gap collected - [Explore collection](https://ypai.ai/data-collection/) - [Speech data](https://ypai.ai/speech-data/) - [Voice recording](https://ypai.ai/audio/voice-recording/) - [Video data](https://ypai.ai/video-data/) - [Physical AI data](https://ypai.ai/physical-ai-data/) - [Healthcare collection](https://ypai.ai/solutions/healthcare/custom-data-collection/) - [Geospatial](https://ypai.ai/geospatial-data-solutions/) - [Ready-made datasets](https://ypai.ai/audio/datasets/) ### Annotation - Your data exists but lacks structure. - Labels, transcripts, events, segments and rankings made against your guideline, with gold tasks, agreement checks and adjudication designed for the task. Every decision stays traceable. - boxes, segments, transcripts, events, keypoints, rankings - guideline, gold tasks, agreement, adjudication, sampling - second labeller and adjudication before a label counts - every decision bound to the guideline it was made against - [Explore annotation](https://ypai.ai/data-solutions/annotation/) - [Video](https://ypai.ai/annotation/video-annotation-services/) - [Image](https://ypai.ai/annotation/image-annotation/) - [Text](https://ypai.ai/annotation/text-annotation-services/) - [Audio and speech](https://ypai.ai/annotation/audio-speech-annotation-services/) - [LiDAR and 3D](https://ypai.ai/annotation/lidar-3d-point-cloud-annotation-services/) - [Sensor fusion](https://ypai.ai/annotation/sensor-fusion-annotation-services/) - [Segmentation](https://ypai.ai/annotation/semantic-segmentation-annotation-services/) - [Medical imaging](https://ypai.ai/annotation/medical-imaging-annotation-services/) - [Named entities](https://ypai.ai/annotation/named-entity-recognition-ner-annotation-services/) - [Robotics vision](https://ypai.ai/annotation/robotics-industrial-vision-annotation-services/) ### Validation - You cannot trust the data you have. - Conformity, representativeness, duplication, contamination and rights gaps, tested before you build on the data. One documented decision: accept, remediate or replace, with every failed item named. - every failed item named, with its reason - [Explore validation](https://ypai.ai/data-solutions/validation/) - [Audio data QA](https://ypai.ai/compliance/audio-data-qa/) - [Speech specifications](https://ypai.ai/speech-data/technical-specifications/) - [Provenance audit](https://ypai.ai/compliance/provenance-audit/) - [Article 10 data governance](https://ypai.ai/speech-data/ai-act-risk-classification/) - [EEA data residency](https://ypai.ai/data-residency-eea/) ### Human and model evaluation - You cannot trust what the model does. - Model output graded against your rubric, population, languages and thresholds by credentialed reviewers: response grading, preference, red-teaming and regression before a release. - credentialed domain experts where the work requires them - [Explore evaluation](https://ypai.ai/data-solutions/evaluation/) - [Clinical model evaluation](https://ypai.ai/solutions/healthcare/clinical-model-evaluation/) - [Speech evaluation project](https://ypai.ai/speech-data/evaluation-program/) - [ASR benchmark](https://ypai.ai/research/asr-benchmark/) ## Eight capability areas. One accountable delivery model. Choose a specialist route where the requirement is already clear. Bring YPAI the full brief when the work crosses several areas. ### Model post-training and evaluation data - Preference pairs and ranking data, supervised fine-tuning demonstrations, rubric-based response grading, red-team and safety evaluation, benchmark tasks and graded agent trajectories, produced by credentialed domain reviewers under defined quality measures. ### Video and multimodal data - Visible-speaker capture, talking-head data, egocentric video, human motion, multi-camera work, synthetic-media datasets, audiovisual evaluation and sensor-paired collection. - [video and multimodal data](https://ypai.ai/video-data/) - [video annotation](https://ypai.ai/annotation/video-annotation-services/) ### Speech and audio - Read and conversational speech, professional voice, TTS data, wake words, acoustic environments, accents, dialects, transcription, diarisation and speech-model evaluation. - [speech data](https://ypai.ai/speech-data/) - audio data collection - [ready-made audio datasets](https://ypai.ai/audio/datasets/) ### Image and computer vision - Custom image collection, classification, bounding boxes, segmentation, keypoints, tracking, source review and specialist visual QA. - [image and computer-vision data](https://ypai.ai/ai-data-annotation/) ### Agent and interaction data - Human demonstrations, tool-use traces, multi-turn interactions, trajectory review, environment state, task outcomes and graded agent behaviour. ### Dataset licensing and sourcing - Existing speech, audio, image, video, text and multimodal datasets sourced through YPAI and approved data partners, subject to ownership, technical fit and permitted use, plus synthetic datasets generated to specification with human verification. ### Text, language and localisation - Text corpora, document data, multilingual annotation, translation, terminology, parallel corpora, MTPE and specialist language review. - [text and language data](https://ypai.ai/annotation/text-annotation-services/) ### Rights, provenance and controlled delivery - Project-specific controls covering source, lawful processing, consent where applicable, permitted use, retention, review access, lineage, delivery and closure. - [delivery controls](https://ypai.ai/data-solutions/ethical-framework/) ## License what fits. Collect only the gap. A new data requirement does not always justify collecting everything from zero. YPAI assesses existing YPAI-controlled and partner-sourced inventory against the project's technical, geographic, quality and rights requirements, starts with licensing and validation where suitable data exists, and collects only the missing languages, cohorts, environments, formats or use-case coverage. One scoped data solution rather than two disconnected procurement exercises. ## One requirement set across the work. A specification should not be translated differently by sourcing teams, contributors, annotators, reviewers and delivery teams. YPAI carries the same project requirements through the engagement, from the first source decision to the final accepted dataset. ### Scope before production. - The engagement defines outputs, formats, rights, acceptance criteria, review methods and decision gates before production begins. ### Controls follow the requirement. - Collection, annotation, validation and review are configured against the same project specification rather than separate vendor assumptions. ### Evidence follows the data. - Quality decisions, rights state, lineage and known exceptions remain connected to the relevant assets and dataset versions. ### Changes remain attributable. - Requirements, guidelines, review policies and delivery versions can be tracked when the engagement changes. YPAI Data Collection & Assurance Platform - Proprietary video and audio collection platforms - Native human-review console as the platform core - Residency, subprocessors and transfers defined per engagement Inspect YPAI's Data Collection & Assurance Platform ## Delivery you can inspect, not claims you have to accept. Every engagement defines what must be delivered, how quality will be assessed and what evidence the buyer needs before accepting the work. Depending on the project, the delivery package can include asset and dataset manifests, source and processing records, rights documentation, quality, sampling and adjudication records, dataset versions with known limitations, and subprocessor, transfer and closure records. Every delivery carries the records the engagement requires and an integrity check on what is delivered. End-of-contract erasure completes within 30 days. YPAI is a Norwegian company. EEA residency by default. Storage, processing, review locations, subprocessors and transfers are defined for the specific engagement. For high-risk AI systems under the EU AI Act, the same delivery records map to what Article 10 expects buyers to hold: dataset origin, collection method, representativeness and documented bias review. - [How this maps to AI Act risk classes](https://ypai.ai/speech-data/ai-act-risk-classification/) - [Review delivery controls](https://ypai.ai/data-solutions/ethical-framework/) ## One pilot against your requirement. Pilot against the actual requirement, then scale the accepted workflow. A pilot is scoped to your specification, quality thresholds and acceptance criteria, and reviewed against them before anything scales. Scope and commercial terms are agreed before it starts. Production is a separate decision, taken after the pilot review. the first night · your requirement - scope and commercial terms agreed before it starts - against the agreed criteria, in a pilot workspace - a separate decision, taken after the review Production: a separate decision, taken after the pilot review ## Every decision stays bound to the frame. The platform carries your requirement through the work and binds every decision to the asset it was taken on. The record ships with the delivery, so what you receive can be inspected, not just trusted. - capture, consent, label, review, inspection, acceptance - ships with every delivery, with an integrity check - a native human-review core, running on self-hosted European servers - subprocessors and transfers defined per engagement - consent recorded · 48 kHz · 24-bit · far-field - Proprietary video and audio collection platforms with verified per-contributor consent - 3 objects · 1 region · adjudicated against the guideline - sampled lot · coverage gap · 3 items remediated - response B accepted on the rubric - Credentialed domain reviewers where the work requires them - manifest · integrity check · erasure within 30 days - [Delivery controls](https://ypai.ai/data-solutions/ethical-framework/) The model is one component. YPAI builds the working system around it. The same team builds and runs the systems this data goes into. Where a system falls short in production, the failure comes back here as the next data requirement. - comes back here as the next data requirement - the same team builds the system and the data - the record travels with the decision - failed tasks, weak languages, production edge cases - the people, task, device or behaviour it fails on - what data or evaluation would close it - delivered against the agreed acceptance criteria - the release decision, with the record attached - [Explore AI implementation](https://ypai.ai/ai-implementation/) ## Use model failures to define the next data workstream. Bring YPAI the evidence of where the system is falling short: failed tasks, underperforming languages or populations, production edge cases. YPAI translates the failure into a targeted collection, annotation, validation or evaluation project built around the affected people, language, task, device, environment or behaviour. ## Scope a data or evaluation brief. Bring the use case and what you already have: modality, intended use, volume, languages or markets, format, devices or environments, deadline, existing data, acceptance criteria, and any processing, rights or security requirements. YPAI comes back with what has to be clarified before scope, price and terms can be agreed. Reply inside one EU business day with a feasibility read. Your Personal AI AS · Lysaker, Norway - [Discuss a pilot](https://ypai.ai/pilots/)