--- title: "Enterprise Services | YPAI" url: https://ypai.ai/enterprise-services/ description: "Enterprise AI services across implementation, data, and evaluation, delivered through one managed operating model with privacy-aware controls." source: "src/copy/routes (route /enterprise-services/)" --- # Enterprise Services > Enterprise AI services across implementation, data, and evaluation, delivered through one managed operating model with privacy-aware controls. Norwegian company. Global delivery. EEA residency by default. YPAI collects, licenses, annotates and evaluates multimodal data, and builds the AI systems that use it. Engage either service line independently, or connect both when the data and the system need to improve together. One partner can own the interfaces between the stages without forcing you to purchase every stage. Keep ownership, quality and delivery controls consistent across both lines. ## AI Data and Evaluation - Create, source and evaluate the data your AI depends on. - Collection · Licensing · Annotation · Evaluation - Individual work packages · Complete data operations ## AI Implementation - Turn a defined AI use case into a system your organisation can operate. - A defined use case · A failing prototype · A manual process · A broader implementation roadmap - End at handoff · Continue as a managed improvement cycle ## Built for multilingual, multimodal delivery across markets. That delivery spans video, image, speech, text and sensor projects through the same operating core. Selected organisations served through AI Data and Evaluation 5 stages, any entry, 1 accountable structure Continue as a managed improvement cycle the complete path, managed end to end Continue as a managed improvement cycle → 03 Evaluate - new multimodal data · required participants · specialist datasets · licensing options - ontologies and guidelines · label and review · resolve disagreements · model-ready datasets - data quality · model behaviour · multilingual performance · safety · acceptance criteria - RAG systems · agents · document workflows · automation · custom AI applications - production behaviour · investigate failures · regression evaluations ## Collect or source - Create new multimodal data, recruit the required participants, source specialist datasets or identify licensing options. ## Annotate and curate - Design ontologies and guidelines, label and review the data, resolve disagreements, filter low-quality inputs and prepare model-ready datasets. ## Evaluate - Test data quality, model behaviour, multilingual performance, safety, robustness and real-world acceptance criteria. ## Implement - Design and integrate RAG systems, agents, document workflows, automation and custom AI applications. ## Monitor and improve - Measure production behaviour, investigate failures, run regression evaluations and improve the system against changing requirements. Build the data your AI needs, with evidence your team can inspect. 7 service families, 150 plus languages YPAI designs and operates data projects around the model, product, environment and population you need to represent. Turn an AI opportunity into a system your team can use and govern. YPAI designs, integrates and operates AI systems around your workflows, data boundaries and existing technology. One delivery, when the work spans both Some organisations need a dataset. Others need an AI system. Fewer handoffs, less context loss and one accountable owner when problems cross stages. Interface detail from YPAI annotation tooling. ## Build the data, then build the system - Collect and prepare the required data, evaluate the model, then implement the system around the validated approach. ## Improve an existing system with better data - Identify failure patterns, create targeted datasets, retrain or reconfigure the model, and verify whether performance improves. ## Keep evaluation connected to production - Turn real failure cases, human review and operational feedback into repeatable evaluation and improvement cycles. ## Video, Physical AI and robotics data - For multimodal models, VLA systems, robotics and video-based products. - On-camera speech, conversational video, lip-sync data, multi-camera capture, egocentric and ego-exo video, workplace demonstrations, hand-object interaction, human activity data, robot demonstrations and action-observation trajectories. - Remote, studio, mobile, field and equipment-assisted capture models can be configured around the project. ## Speech and audio data - For ASR, voice agents, TTS and multilingual language systems. - Scripted and spontaneous speech, multi-speaker conversation, emotional speech, dialects, accents, code-switching, low-resource languages, noisy and far-field environments, turn-taking, interruptions, wake words and consented voice applications. - Projects can combine collection, transcription, linguistic annotation and model evaluation. ## Image, 3D and sensor data - For computer vision, perception and specialist visual models. - Image collection, multi-view data, scene and object coverage, document imagery, industrial and geospatial data, LiDAR, depth, IMU, sensor fusion and rare-event datasets. - Coverage can be designed around device, environment, lighting, pose, geography, population and edge-case requirements. ## Dataset licensing and sourcing - For projects where existing rights-cleared data is faster or more valuable than new collection. - Ready-made and bespoke dataset sourcing, exclusive and non-exclusive licensing, model-training and evaluation rights, provenance, dataset documentation, versioning, sample validation and buyer-request sourcing. - Public and NDA-gated inventory models are both available. ## Synthetic data - For rare cases, coverage gaps, augmentation and simulation-heavy workflows. - Synthetic text, speech, image, video and multimodal data, simulation-generated robotics data, rare-event generation, privacy-oriented synthetic datasets and synthetic-to-real validation. - Synthetic data is evaluated for utility, fidelity, coverage and downstream model performance, not treated as a substitute for real data. ## Annotation and data production For converting raw inputs into consistent, model-ready data. Ontology and taxonomy design, guideline engineering, image, video, text, speech, LiDAR and sensor annotation, model-assisted labelling, human verification, specialist review, adjudication and continuous production. Quality can be measured through gold sets, reviewer calibration, agreement analysis, acceptance sampling and project-specific gates. ## Model and agent evaluation For understanding how an AI system behaves before and after release. Human and expert evaluation, multilingual testing, preference data, rubric and benchmark design, retrieval and RAG evaluation, red teaming, robustness testing, agent task evaluation, failure analysis and regression testing. Evaluation is designed around the decisions the results must support, not generic benchmark scores. ## Discovery and architecture For deciding what should be built, how it should work and what success means. Use-case selection, data-readiness assessment, model and vendor selection, architecture design, risk and dependency mapping, delivery planning and acceptance criteria. The output is a buildable system plan, not a generic AI strategy presentation. ## RAG and knowledge systems - For making organisational knowledge usable through AI. - Enterprise search, grounded assistants, retrieval pipelines, document ingestion, access-aware knowledge systems, citation workflows and evaluation of answer quality. - Systems are designed around your documents, permissions, update cycles and operational use cases. ## Agents and agent governance - For multi-step work that requires tools, decisions and human oversight. - Task-specific agents, tool use, browser and computer workflows, multi-agent systems, human-in-the-loop controls, permission boundaries, failure recovery and trace-level evaluation. - Controls, human oversight and evaluation are built into execution, so the system can be reviewed and improved in production. ## Document AI and workflow automation - For reducing manual work across document-heavy and repeatable operations. - Extraction, classification, validation, routing, review queues, process orchestration and integration with existing business systems. - Automation is designed around the complete workflow, including exceptions, approvals and handoff to people. ## Private and enterprise deployment For organisations with defined security, residency and infrastructure requirements. Customer-environment deployment, private architectures, EEA residency by default, zero-egress configurations where appropriate, small and specialised models, enterprise integrations and staged release controls. The architecture is selected around the actual operating constraint, not a predetermined cloud or model vendor. ## Evaluation, observability and managed improvement For keeping the system reliable after the first release. Pre-release acceptance testing, production monitoring, regression evaluation, failure analysis, model and prompt comparison, staged promotion, rollback and iterative optimisation. The engagement can end at handoff, or continue with the same gates in production. Every YPAI engagement follows the same controlled delivery structure across data, evaluation and implementation. Control coverage drawing: the six controls positioned over the five delivery stages, from the entry gate to versioned handoff, inside the data protection boundary. ## Named project ownership - For knowing who is accountable for progress, decisions and delivery. - Each engagement has defined ownership, decision paths, milestones and escalation points. ## Scope and acceptance gates - For agreeing what is being delivered before production work expands. - Requirements, quality thresholds, output formats, review methods and acceptance criteria are defined against the specific engagement. ## Human quality controls - For applying judgement where automated checks are not sufficient. - Human and specialist review can be placed at the stages where errors carry operational, linguistic, technical or domain consequences. ## Rights and provenance - For tracing where data came from and how it may be used. - Consent records, model releases, rights chains, asset-level provenance, version history and dataset documentation are configured where relevant to the project. ## Data protection and residency - For matching the delivery architecture to the project's actual requirements. - DPA coverage, access controls, transfer considerations, subprocessors and processing locations are scoped per engagement. ## Versioned delivery and handoff - For receiving outputs that can be reviewed, accepted and operated. - Deliveries can include manifests, checksums, data cards, quality reports, change records, technical documentation and acceptance evidence. YPAI owns the agreed delivery process. Your organisation retains its statutory obligations, internal approvals and final business decisions. Your requirements are translated into operating constraints and acceptance gates throughout delivery. - final acceptance of the delivered work Every sector breaks AI in a different place. The difficult part is rarely the average case. It is the condition each sector actually operates in. ## AI companies and model developers - Proving that a change was actually an improvement. - Training data, preference data, multimodal evaluation, red teaming, agent trajectories and production feedback loops. ## Automotive and mobility - Accents, road noise, and the rare event. - In-cabin speech, perception data, video and sensor collection, edge-case coverage, annotation and model evaluation. ## Financial services - Explaining a decision long after it was made. - Document AI, knowledge systems, review workflows, traceable decisions and controlled automation. ## Healthcare and life sciences - Plausible and correct look identical without a specialist. - Specialist data, domain review, privacy-sensitive workflows and human-supervised AI systems. ## Industrial and energy - Field conditions, and the systems already running the site. - Visual, sensor and operational data, field workflows, private deployment and integration with existing systems. ## Public sector - Every step reviewable by someone outside the project. - Controlled data operations, knowledge systems, workflow automation, auditability and project-specific governance. Start with a pilot built around the real requirement. Every YPAI service can begin with a pilot tailored to your specification. Scope and commercial terms are agreed before it starts. It validates the delivery method before the full engagement is mobilised. Scope the system, the data, or both. YPAI collects, licenses, annotates and evaluates multimodal data, and builds the AI systems that use it. Engage us for AI Data and Evaluation, AI Implementation, or one connected delivery across both. A project lead replies inside one EU business day. ## message ## constraints ## acceptance_criteria ## name ## email ## company