AI data, evaluation and implementation under one accountable delivery model.

Scope an engagement

Norwegian company. Global delivery. EEA residency by default.

YPAI collects, licenses, annotates and evaluates multimodal data, and builds production AI systems for organisations that need controlled delivery, documented rights and human-verified quality.

Data and systems should not become separate failure points.

One partner can own the interfaces between the stages without forcing you to purchase every stage.

AI Data and Evaluation

Create, source and evaluate the data your AI depends on.

Begins with

Collection · Licensing · Annotation · Evaluation

Engaged as

Individual work packages · Complete data operations

Explore AI Data and Evaluation →

AI Implementation

Turn a defined AI use case into a system your organisation can operate.

Begins with

A defined use case · A failing prototype · A manual process · A broader implementation roadmap

Engaged as

End at handoff · Continue as a managed improvement cycle

Explore AI Implementation →

Keep ownership, quality and delivery controls consistent across both lines.

How delivery is controlled →

Built for multilingual and international operations, with large-scale delivery across video, audio, image and multilingual data delivery.

See how pilots work →
150+
210,000+
languages covered
contributor network
50+
30 days
countries represented
end-of-contract erasure SLA

That delivery spans video, image, speech, text and sensor programmes through the same operating core.

Selected organisations served through AI data and evaluation

Start at any stage. Connect the stages that matter.

5 stages / any entry / 1 accountable structure

Source data

Collect or source

Annotate and curate

Evaluate

new multimodal data · required participants · specialist datasets · licensing options

Create new multimodal data, recruit the required participants, source specialist datasets or identify licensing options.

ontologies and guidelines · label and review · resolve disagreements · model-ready datasets

Design ontologies and guidelines, label and review the data, resolve disagreements, filter low-quality inputs and prepare model-ready datasets.

data quality · model behaviour · multilingual performance · safety · acceptance criteria

Test data quality, model behaviour, multilingual performance, safety, robustness and real-world acceptance criteria.

An operating AI system

End at handoff

Monitor and improve

Implement

production behaviour · investigate failures · regression evaluations

Measure production behaviour, investigate failures, run regression evaluations and improve the system against changing requirements.

RAG systems · agents · document workflows · automation · custom AI applications

Design and integrate RAG systems, agents, document workflows, automation and custom AI applications.

the complete path under one accountable delivery structure

Build the data and evidence your AI needs to perform.

7 service families · 150+ languages

YPAI designs and operates data projects around the model, product, environment and population you need to represent. The service can begin with collection, licensing, annotation or evaluation, without forcing the project into a fixed catalogue.

Collect or source

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 can be used depending on the dataset and rights structure.

  • 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 rather than treated as a substitute for real data by default.

Annotate and curate

  • 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.

Evaluate

  • 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.

One delivery, when the work spans both

Some organisations need a dataset. Others need an AI system. Many need both.

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.

Fewer handoffs, less context loss and one accountable owner when problems cross stages.

Turn an AI opportunity into a system your team can use and govern.

6 implementation capabilities

YPAI designs, integrates and operates AI systems around your workflows, data boundaries and existing technology. Engagements can begin with a defined use case, a failing prototype, a manual process or a broader implementation roadmap.

Implement

  • 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.

Private and enterprise deployment

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, operated 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.

For organisations with defined security, residency and infrastructure requirements.

Customer-environment deployment, private architectures, EEA-based processing, 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.

Monitor and improve

  • 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 as a managed improvement cycle.

Continue as a managed improvement cycle → 03 Evaluate

AI Data and Evaluation

Scope a data or evaluation engagement →

AI Implementation

Scope an AI implementation →

Controlled delivery

The same delivery discipline across data and systems.

Every YPAI engagement follows the same controlled delivery structure across data, evaluation and implementation.

Shared operating layer
01 02 03 04 05 ownership review gates provenance handoff residency
01 Collect or source 02 Annotate and curate 03 Evaluate 04 Implement 05 Monitor and improve
  1. 01

    Named project ownership

    ownership

    For knowing who is accountable for progress, decisions and delivery.

    Each engagement has defined ownership, decision paths, milestones and escalation points.

  2. 02

    Scope and acceptance gates

    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.

  3. 03

    Human quality controls

    review

    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.

  4. 04

    Rights and provenance

    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.

  5. 05

    Data protection and residency

    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.

  6. 06

    Versioned delivery and handoff

    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.

Clear ownership

We run the delivery. You retain organisational authority.

YPAI owns the agreed delivery process.

Your organisation retains its statutory obligations, internal approvals and final business decisions.

YPAI runs

  1. project planning and delivery leadership 01
  2. contributor, specialist and partner mobilisation 02
  3. data and AI workflow execution 03
  4. quality controls and remediation 04
  5. delivery documentation 05
  6. agreed reporting and acceptance process 06

Your organisation retains

  1. 01 business objectives and risk appetite
  2. 02 legal basis and statutory obligations
  3. 03 sector-specific requirements
  4. 04 internal security and compliance approval
  5. 05 deployment and production-release decisions
  6. 06 final acceptance of the delivered work

Your requirements are translated into operating constraints and acceptance gates throughout delivery.

Operating conditions

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.

Validate before scale

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.

The pilot is designed around

  • the actual modality
  • workflow
  • quality thresholds
  • output format
  • acceptance criteria

The pilot can validate

  • participant or data sourcing
  • capture and submission workflow
  • annotation and review design
  • technical quality
  • human quality controls
  • model or workflow performance
  • delivery format and documentation
  • acceptance criteria

It validates the delivery method before the full engagement is mobilised.

Every pilot is customized. The scope is agreed case by case.

Scope a pilot →

Bring the objective, the constraints and the acceptance criteria.

YPAI defines the service line, delivery architecture and first validation step.

Engage us for AI Data and Evaluation, AI Implementation, or one connected delivery across both.

Service line (optional)

A project lead replies within one business day.

Norwegian company · Global delivery · EEA residency by default · DPA available on request

Selected organisations served through AI data and evaluation