AI DATA, EVALUATION AND IMPLEMENTATION
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.
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.
TWO SERVICE LINES
Each service line has its own scope, deliverables and acceptance criteria. Combine them only when the requirement crosses the boundary.
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.
Define the workflow → Build and integrate → Deploy and evaluate → Improve
Explore AI ImplementationOne connected engagement
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.
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.
Source or collect → Prepare and annotate → Validate and evaluate → Deliver
Explore AI Data & EvaluationThe data platform
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.
inside the platform: project specification · contributor qualification · consent and rights · collection · technical validation · human review · versioned delivery
data assurance record
The evidence defined for the engagement remains connected to the delivered release.
THE IMPROVEMENT LOOP
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.
Deploy a working system or evaluate an existing one against a defined baseline.
outputWorking baseline
Identify where performance breaks across tasks, users, languages, conditions and operational constraints.
outputFailure set and evaluation record
Improve the model, retrieval, integration, workflow, controls or underlying data according to the evidence.
outputSystem, control or data change
Test the change against the agreed baseline and decide what happens next.
outputValidated release and next decision
One team can own the loop from the first scope to the next validated release.
Controlled Delivery
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.
WHAT ARRIVES AT HANDOVER
The scope defines the output, the evidence used to assess it and the decision required at handover.
AI Data & Evaluation delivery
A versioned release, not an unexplained folder of files.
dataset releaseversioned
assets/Assets and metadata in the agreed formatsrights/Rights, consent or lawful-basis records where applicable spec/Collection, annotation or evaluation specification qa/Quality, review and acceptance recordsMANIFESTDelivery version, known limitations and handover notes AI Implementation delivery
A working release with a clear acceptance record.
system releasedeployed
deploy/The agreed release in the agreed environment docs/Integration and deployment documentationeval/Evaluation baseline, test results and known limitations access/Access, monitoring and change controls as agreed HANDOVERAcceptance record and next-release decision FIRST VALIDATION
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.
Norwegian company EEA-based processing available where required Article 28 DPA terms available
SELECTED EXPERIENCE
Selected delivery experience across automotive, voice AI and multilingual data.
Automotive
Voice AI
Multilingual data
OPERATING CONTEXTS
YPAI supports teams where data quality, operational fit, language coverage, reviewability, security or residency materially affects the result.
Start with the requirement
Tell us what you are building, where the current approach falls short and what the first delivery must prove. YPAI will map the requirement to the right service line, delivery structure and first decision.
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