--- title: "AI Data, Evaluation and Implementation | YPAI" url: https://ypai.ai/ description: "YPAI builds production AI systems and delivers multimodal data, annotation and evaluation under one accountable delivery model." source: "src/copy/routes (route /)" --- # We build the AI. And the data behind it. > YPAI builds production AI systems and delivers multimodal data, annotation and evaluation under one accountable delivery model. YPAI builds production AI systems around defined workflows and delivers the multimodal data used to train, evaluate and improve them. Engage either service line independently, or connect both when performance depends on the system and its data. Scope a data or evaluation project A rainy intersection at night seen from a fixed camera, the camera's own mast in frame. Machine-reading overlays mark two cyclists in the marked crossing, a passing vehicle, and the crossing corridor itself. A docked production panel reads the same second: detections, an evaluation gate, a human review return, and a completed workflow feeding the next release. One second of the world becomes a production decision. Input data moves through processing, evaluation, human review and workflow action. This is one example of how YPAI can connect data production and AI implementation without splitting accountability between suppliers. Each service line has its own scope, deliverables and acceptance criteria. Engage either independently, or both when the system and the data need to improve together. Turn a defined workflow into a working AI system. Assistants, agents, RAG, document intelligence, workflow automation, voice and multimodal systems designed, integrated and deployed around a specific operational requirement. A deployed agent system processing one request: intake, retrieval against a corpus, agent steps, action and measurement, with a span trace below Build, source and evaluate the data your models depend on. Custom collection, dataset sourcing and licensing, annotation, validation and human evaluation across speech, video, image, text, 3D and sensor data. LiDAR point cloud with a 3D annotation cuboid drawing itself around a vehicle When the problem crosses the boundary, the delivery should not. Deployment can expose a missing language, cohort, edge case, retrieval source, control or evaluation need. YPAI can change the system, produce the missing data or do both under one accountable scope. ## The data and its evidence stay connected. YPAI operates its own collection, annotation and assurance infrastructure. Project requirements stay linked to contributor qualification, purpose-specific consent, collection, validation, human review, versioning and delivery. Your team can inspect how a release was produced, why it passed and what changed. Use only data covered by the agreed purpose and rights basis. identity-verified contributors · consent documented per contributor and purpose · provenance linked to the collected record Judge each release against criteria agreed before production. technical validation · documented sampling · human review · exceptions recorded Trace the affected records and the action taken. request logged · affected records identified · release updated · deletion or remediation recorded inside the platform: project specification · contributor qualification · consent and rights · collection · technical validation · human review · versioned delivery Contributor identity verified · collection source recorded Technical and statistical checks · pass Human decision recorded · reviewer r-041 The evidence defined for the engagement remains connected to the delivered release. Affected records identified · excluded from the next release · deletion or remediation recorded under the project terms ## Production reveals what must change next. A performance gap may sit in the model, retrieval layer, integration, workflow, controls or data. YPAI measures the failure, changes the right layer and validates the next release. Put the first version into use Deploy a working system or evaluate an existing one against a defined baseline. Identify where performance breaks across tasks, users, languages, conditions and operational constraints. Improve the model, retrieval, integration, workflow, controls or underlying data according to the evidence. Test the change against the agreed baseline and decide what happens next. One team can own the loop from the first scope to the next validated release. ## AI data, evaluation and implementation under one accountable delivery model. One second of the world drawn as a continuous timeline through six representations: a pedestrian tracked with pose estimation, a vehicle as a 3D point cloud, its sound as a waveform with a wake-word span, two model responses compared token by token, an agent trace retrieving from a document corpus, and a production system handling live traffic with human review, ending in a completed action ## Work your team can inspect, test and accept. Controlled Delivery is the operating layer across both service lines. It defines how scope, rights, quality, security, change and acceptance are handled from the first decision to handover. Objectives, constraints, outputs, acceptance criteria and decision gates. Sources, permitted uses, access, retention, deletion and residency requirements. Sampling, validation, reviewer roles, escalation and pass or fail criteria. Environments, permissions, subprocessors and handoffs defined for the engagement. Baselines, revisions, known limitations and change control. Delivery record, acceptance decision and the agreed next step. Know the deliverable before production starts. The scope defines the output, the evidence used to assess it and the decision required at handover. A versioned release, not an unexplained folder of files. Assets and metadata in the agreed formats Rights, consent or lawful-basis records where applicable Delivery version, known limitations and handover notes A working release with a clear acceptance record. The agreed release in the agreed environment Evaluation baseline, test results and known limitations Access, monitoring and change controls as agreed Prove the first delivery before committing to scale. Where a pilot is the right validation step, YPAI scopes it against your requirements, quality thresholds and acceptance criteria. Commercial and delivery terms are agreed before work begins. ## Built on international data operations. Built for AI work that has to perform under real constraints. YPAI supports teams where data quality, operational fit, language coverage, reviewability, security or residency materially affects the result. Training and evaluation data, model assessment, multilingual performance and release evidence. In-cabin, voice, perception and multimodal data for systems operating under real-world conditions. Task demonstrations, human motion, 3D, sensor data and evaluation for embodied systems. Sensitive data, project-specific review requirements and traceable handling. Document intelligence, workflow automation, human review and change control. Operational AI with defined security, residency and continuity requirements. ## Bring the workflow, the dataset or the performance gap. Tell us what you are building, where the current approach falls short and what the first delivery must prove. What are you building or improving? Describe the workflow, system, model or dataset. What must the delivery account for? Modalities, languages, markets, integrations, security, residency, timeline or delivery requirements. What must the first delivery prove? Tell us what your team must be able to test, accept or decide. YPAI reviews each brief against scope, fit and delivery requirements. for how enquiry data is processed.