Your Personal AI (YPAI) is a company that builds production-grade AI systems and provides multimodal data, annotation, and evaluation services, with its core proposition being to deliver AI systems and the data behind them under the same accountable delivery model. According to its official website, YPAI builds production AI systems around defined workflows and provides multimodal data for training, evaluating, and improving these systems; clients can use either service line independently or connect the two when performance depends on both the system and the data.
I. Two Service Lines
- AI Implementation: Turning defined workflows into runnable AI systems, including assistants, agents, RAG, document intelligence, workflow automation, voice, and multimodal systems, designed, integrated, and deployed around specific operational needs. Its process is described as: define the workflow → build and integrate → deploy and evaluate → improve.
- AI Data & Evaluation: Building, acquiring, and evaluating the data that models depend on, covering custom collection, dataset acquisition and licensing, annotation, validation, and human evaluation, with data types including voice, video, images, text, 3D, and sensor data. Its process is described as: acquire or collect → prepare and annotate → validate and evaluate → deliver.
II. Data Platform and Data Assurance YPAI operates its own collection, annotation, and assurance infrastructure. Project requirements remain linked to contributor qualifications, purpose-specific consent, collection, validation, human review, version management, and delivery. Client teams can inspect how a release was produced, why it passed, and what changed. Data assurance records are organized around three types of controls:
- Rights: Use only data that complies with the agreed purpose and rights basis; contributors are identity-verified, consent is recorded by contributor and purpose, and provenance can be traced back to collection records.
- Quality: Each release is judged against standards agreed upon before production; this includes technical validation, documented sampling, human review, and exception records.
- Change: Affected records and actions taken can be traced; requests are recorded, affected records are identified, releases are updated, and deletions or remediations are recorded. The stages within the platform include: project specifications, contributor qualifications, consent and rights, collection, technical validation, human review, and versioned delivery.
III. Improvement Loop The official website describes an evidence-driven improvement loop: put into service the first usable version (deploy a usable system or evaluate an existing system against a defined baseline, producing a working baseline); measure failures (identifyUnder tasks, users, languages, conditions, and operational constraints, where does performance break down, producing a failure set and evaluation record); change the right layer (improve the model, retrieval, integration, workflow, controls, or underlying data based on evidence, producing system, control, or data changes); validate release (test changes against agreed baselines and decide the next step, producing a validated release and the next decision). The official website states that a team can be responsible for the entire cycle from the first scope to the next validated release.
IV. Controlled Delivery Controlled Delivery is the operational layer that runs across both service lines, defining how scope, rights, quality, security, change, and acceptance are handled from the first decision to handover, specifically including:
- Scope and acceptance: objectives, constraints, outputs, acceptance criteria, and decision gates.
- Rights and data flows: sources, permitted uses, access, retention, deletion, and residency requirements.
- Quality and review: sampling, validation, review roles, escalation, and pass or fail criteria.
- Security and isolation: environments, permissions, sub-processors, and handover defined for the project.
- Versions and change: baselines, revisions, known limitations, and change control.
- Handover and accountability: delivery records, acceptance decisions, and agreed next steps.
V. Handover Content
- AI data and evaluation delivery: a versioned release, not a pile of unexplained files. This includes assets and metadata in the agreed format, rights/consent or lawful basis records (where applicable), collection/annotation or evaluation specifications, quality/review and acceptance records, and a manifest of the delivered version, known limitations, and handover notes.
- AI implementation delivery: a runnable release and a clear acceptance record. This includes the agreed release in the agreed environment, integration and deployment documentation, evaluation baselines/test results and known limitations, access/monitoring and change control as agreed, and acceptance records and the next version decision.
VI. First Validation The official website states that when a pilot is the appropriate validation step, YPAI will define the pilot scope based on the customer's requirements, quality thresholds, and acceptance criteria; commercial and delivery terms are agreed before work begins.
VII. Company Information and Experience The official website shows that YPAI is a Norwegian company, defaults to EEA residency, and provides Article 28 DPA terms. Regarding the selected experience, the official website mentions delivery experience across automotive, voice AI, and multilingual data.
VIII. Applicable Scenarios YPAI is aimed at teams where data quality, operational fit, language coverage, auditability, security, or residency requirements materially affect outcomes. The official website suggests that customers come with a workflow, dataset, or performance gap and explain what is being built, where the current approach falls short, and what the first delivery must prove.The official website also provides contact entry points such as the pilot working method, data handling method, partnership discussions, and LinkedIn, and notes that analytics and Google Ads cookies are set only if the user accepts, and the choice can be changed at any time in Cookie Settings.
Information about fees, pricing, specific platform compatibility, the number of supported languages, deployment environment details, and similar matters is not provided in the above materials and is not yet available.
