Governed AI-ready assurance data

AI-native infrastructure for trusted AI assurance data at scale.

We are developing AI-native infrastructure for organisations that coordinate expert validation, quality assurance and AI evaluation workflows — helping teams structure review, provenance, metadata and governed access around the evaluation data AI systems need.

Explore capabilities

Problem vs Solution

Expert validation needs structure before it can become trusted AI assurance data.

Where teams struggle

Expert review often happens across spreadsheets, task tools, messages and disconnected quality documents.

Validation decisions can be hard to compare, audit or reuse across AI evaluation and quality-assurance workflows.

Reviewer guidance, scoring rules and escalation paths are not always captured in a structured way.

Provenance, metadata and access controls are often added after the work, rather than built into the workflow.

What the infrastructure provides

A structured infrastructure layer for high-volume expert-validation and quality-assurance workflows.

Configurable intake, routing, review, escalation and adjudication flows for teams coordinating many reviewers, tasks and quality decisions

Evidence capture around rubrics, reviewer decisions, metadata and provenance.

Governed access patterns for organisations coordinating internal experts, approved reviewers or specialist workforce partners across multiple workflows.

Why it matters now

Trusted AI needs more than automation. It needs evidence.

Proof behind model behaviour

AI teams need clearer proof of how model outputs were evaluated, what expert reviewers decided, why those decisions were made and what quality outcomes were recorded.

Assurance is becoming operational

Governance and assurance work is becoming more repeatable, workflow-driven and audit-sensitive.

Expert judgement needs structure

Expert judgement is valuable only when it is captured in a consistent, traceable and reusable way

Quality control needs consistency

Quality-assurance workflows need shared rubrics, escalation paths, review records and rework logic.

Expert workforces need infrastructure at scale

Organisations using internal experts, approved reviewers or specialist partners need better infrastructure to coordinate quality across large, distributed or multi-workflow review environments.

Responsible deployment starts early

Controlled access, provenance, metadata and review records need to be built into the workflow from the start

Why it matters now

Trusted AI needs more than automation. It needs evidence.

Proof behind model behaviour

AI teams need clearer proof of how model outputs were evaluated, what expert reviewers decided, why those decisions were made and what quality outcomes were recorded.

Assurance is becoming operational

Governance and assurance work is becoming more repeatable, workflow-driven and audit-sensitive.

Expert judgement needs structure

Expert judgement is valuable only when it is captured in a consistent, traceable and reusable way

Quality control needs consistency

Quality-assurance workflows need shared rubrics, escalation paths, review records and rework logic.

Expert workforces need infrastructure at scale

Organisations using internal experts, approved reviewers or specialist partners need better infrastructure to coordinate quality across large, distributed or multi-workflow review environments.

Responsible deployment starts early

Controlled access, provenance, metadata and review records need to be built into the workflow from the start

Core capabilities

Infrastructure for the assurance-data layer behind trusted AI.

The company is developing AI-native infrastructure for organisations that coordinate expert validation, quality assurance and AI evaluation workflows at scale. It helps those organisations structure reviewer input, quality controls, metadata, provenance, access permissions and assurance-ready outputs across internal experts, approved reviewers, specialist workforce partners, multiple review queues and governed evaluation workflows.

AI-ready review workflows

Help teams structure evaluation and quality-assurance material with the context, metadata and documentation needed for later review and assurance use.

AI-ready review workflows

Help teams structure evaluation and quality-assurance material with the context, metadata and documentation needed for later review and assurance use.

Expert-validation routing

Support organisations that use internal experts, approved reviewers or specialist partners by routing work according to skill, risk, confidence and review need.

Quality-assurance rubrics

Capture consistent scoring, review criteria, escalation logic and adjudication records across expert-led evaluation workflows.

Provenance and metadata

Record source context, rights basis, review history, decision rationale and version state around validation activity.

Governed workflow access

Support permissions, role-based access, audit logs, restrictions and release controls for operational review environments.

Expert intelligence

Use skills evidence, availability signals and quality history to support better expert allocation, oversight and workflow planning.

A simple process for turning expert validation into governed AI-ready evidence

How it works

Map the assurance need

Book a free discovery call

Define the model, workflow, risk area, task type, review requirement or quality-control process that needs structured expert input.

Configure the material

Set intake fields, reviewer guidance, scoring rubrics, metadata requirements, access permissions and escalation paths

Coordinate validation

Package decisions, quality outcomes, rationale, provenance, audit records and workflow metadata into AI-ready evidence that can support evaluation, assurance and quality oversight.

Capture AI-ready evidence

Package decisions, quality outcomes, rationale, provenance, audit records and workflow metadata into AI-ready evidence that can support evaluation, assurance and quality oversight.

The workflow is designed to make evaluation and expert review clearer, more traceable and easier to govern across teams, reviewers and recurring AI quality workflows.

A simple process for turning expert validation into governed AI-ready evidence.

How it works

The workflow is designed to make evaluation and expert review clearer, more traceable and easier to govern across teams, reviewers and recurring AI quality workflows.

Map the assurance need

Define the model, workflow, risk area, task type, review requirement or quality-control process that needs structured expert input.

Map the assurance need

Define the model, workflow, risk area, task type, review requirement or quality-control process that needs structured expert input.

Configure the material

Set intake fields, reviewer guidance, scoring rubrics, metadata requirements, access permissions and escalation paths

Coordinate validation

Package decisions, quality outcomes, rationale, provenance, audit records and workflow metadata into AI-ready evidence that can support evaluation, assurance and quality oversight.

Coordinate validation

Package decisions, quality outcomes, rationale, provenance, audit records and workflow metadata into AI-ready evidence that can support evaluation, assurance and quality oversight.

Capture AI-ready evidence

Package decisions, quality outcomes, rationale, provenance, audit records and workflow metadata into AI-ready evidence that can support evaluation, assurance and quality oversight.

Capture AI-ready evidence

Package decisions, quality outcomes, rationale, provenance, audit records and workflow metadata into AI-ready evidence that can support evaluation, assurance and quality oversight.

Use cases

Designed for recurring AI evaluation and assurance workflows.

The infrastructure is designed for organisations that need to structure expert judgement, validation evidence, provenance and governance controls across AI development, evaluation and deployment workflows at scale.

AI output review operations

Coordinate structured review of model outputs, edge cases, hallucination risks and factuality concerns.

Expert validation and quality assurance

Support controlled intake, reviewer assignment, rubric-based quality checks and rework loops for expert validation teams.

Expert adjudication workflows

Route ambiguous, high-risk or specialist cases to appropriate reviewers and capture final decision rationale.

Model evaluation operations

Help teams organise evaluation tasks, review records, quality records and operational feedback loops.

Compliance review support

Structure validation evidence, escalation history and governance notes for teams preparing internal assurance records.

Expert quality governance

Use skills evidence, review history and quality outcomes to improve expert allocation, oversight and continuous improvement.

Who it is for

Built for organisations coordinating complex AI review and assurance activity.

The infrastructure is relevant where organisations already use, manage or partner with expert reviewers and need stronger workflow control, quality assurance and review-record capture across multiple teams, queues, projects or evaluation workflows.

AI companies and model teams

Teams that need structured expert review, evaluation workflows and quality evidence around model behaviour.

AI companies and model teams

Teams that need structured expert review, evaluation workflows and quality evidence around model behaviour.

AI assurance and governance teams

Teams that need review records, rationale, quality evidence and access controls around AI evaluation activity.

Expert validation providers

Organisations coordinating validation, quality assurance and expert review work for AI development workflows.

Enterprise AI adopters

Companies deploying AI and needing controlled review processes, escalation paths and traceable assurance evidence.

Specialist expert-workforce partners

Providers that may supply expert reviewers while using infrastructure to coordinate quality, access and assurance-ready outputs.

Product and operations leaders

Leaders replacing fragmented spreadsheets and manual coordination with a more governed infrastructure layer.

Governance and trust

Workflow governance built into daily AI review operations.

The governance layer focuses on how expert-led review work is controlled, evidenced and overseen. It supports day-to-day AI evaluation and quality-assurance workflows through controlled intake, role-based permissions, audit-ready records, metadata, provenance and operational release controls.

Controlled intake

Teams can define what material enters a workflow, what context must be attached and which review route applies.

Audit-ready activity records

Review actions, rubric versions, escalation decisions, rework outcomes and quality checks can be recorded for later oversight.

Expert adjudication workflows

Route ambiguous, high-risk or specialist cases to appropriate reviewers and capture final decision rationale.

Role-based permissions

Experts, reviewers, managers and client-side users should only see the records and workflows they are authorised to access.

Operational release controls

Outputs can be separated by project, client, workflow, permission level and intended use before being shared or reused internally

Find your Solution

Need better infrastructure for expert-led AI review at scale?

Contact us to discuss AI evaluation, expert validation, quality-assurance workflows, governed review operations or infrastructure needs across larger, more complex or recurring AI review environments.