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AIRA Founding Pilot

From AI promise to a demonstrably working workflow.

Test governed AI on one concrete decision workflow, with human control, traceable sources and measurable results.

What you get

Start small. Prove it seriously.

The pilot is deliberately bounded. Within weeks, you get real evidence of value without starting a large transformation programme.

01

Intake and success criteria

We select one workflow, identify the users and define in advance what measurable success looks like.

02

Working pilot prototype

AIRA works with real or safely anonymised data inside the agreed workflow.

03

Governance and evidence

Sources, assumptions, uncertainty, human controls and decisions remain visible.

04

Evaluation and scale decision

You receive a demonstration, evaluation report and a concrete recommendation for implementation or follow-up.

Possible workflows

Start where better decisions create immediate value.

Procurement

Procurement and opportunities

Find relevant opportunities, assess fit and prepare an evidence-based go/no-go decision.

Risk

Claims, evidence and uncertainty

Analyse documents and arguments and show where evidence, counterclaims or human review are required.

Public sector

Policy and decision-making

Structure options, interests, rules and consequences with transparent human accountability.

Method

Four steps to demonstrable value.

  1. 1

    Explore

    Problem and feasibility

  2. 2

    Scope

    Data and success criteria

  3. 3

    Execute

    Build, test and review

  4. 4

    Decide

    Evaluate and scale

Limited number of founding pilots

Which workflow in your organisation should demonstrably improve?

In an initial call we determine whether the workflow is suitable, which outcomes are measurable and what pilot scope is realistic.

Plan a pilot call

AIRA Founding Pilot · Market validation

Prove customer value before you scale AI.

One workflow, one accountable owner and success criteria agreed in advance. A paid pilot tests the technology, the operational value, the governance fit and whether the customer wants to continue.

€2,000 from · excl. VAT · 4–6 weeks · Evidence before scale

Paid market validationWBSO R&D awardedHumanX Europe 2026Human-in-control

Two commercial routes

Same governed core. Different operating context.

The method

Four phases. Evidence before scale.

01

Fit & baseline

Define the problem, accountable owner, current effort, evidence needs, risks and credible success criteria.

02

Map & prototype

Map actors, rules, data and dependencies; build one bounded governed workflow.

03

Controlled field test

Run with real users and record sources, corrections, exceptions, outcomes and adoption signals.

04

Evidence & decision

Compare with the baseline and decide: stop, improve, continue, productise or scale.

Market validation

Every paid pilot must answer four commercial questions.

01

Problem

Is this problem important and costly enough that a real customer prioritises it?

02

Value

Does AIRA create measurable operational value versus the customer’s own baseline?

03

Adoption

Does the workflow fit governance, users and existing systems well enough to be used in practice?

04

Pull

Does the customer want to continue, expand, renew, refer or reuse the capability?

PRODUCT-MARKET FIT

A successful pilot is evidence. It is not product-market fit by itself.

AIRA currently describes product-market fit as in validation, not proven. PMF requires repeated behaviour across multiple independent customers: the same core problem, willingness to pay, measurable value, continuation or expansion and increasingly repeatable acquisition and delivery economics.

Clear starting point

From €2,000 excl. VAT

Final scope and price follow the fit check. Data sources, integrations, security, assurance and organisational complexity determine the exact proposal.

  • 4–6 week delivery
  • One workflow and accountable owner
  • Baseline and measurable success criteria
  • Working prototype and controlled field test
  • Market-validation evidence and customer feedback
  • Results, risks and scale recommendation
  • No mandatory subscription or follow-on

Claim discipline

Targets, measured results and explicit uncertainty.

There is no defensible universal AI-savings percentage across organisations. Suitable workflows can improve materially, but task, data, adoption and controls determine the result. AIRA therefore measures against the customer’s own baseline and separates targets from proven outcomes.

FAQ

Clarity before commitment.

Does one successful pilot prove product-market fit?+

No. It proves evidence for that customer and workflow. PMF requires repeatable demand and value across multiple independent customers.

Are productivity targets guaranteed?+

No. They are hypotheses for suitable workflows. The baseline sets the credible target and the final report shows the measured result.

Does AIRA replace employees or final decisions?+

No. AIRA supports analysis, preparation and bounded execution. An authorised professional remains accountable for consequential decisions.

What if the workflow is not suitable?+

Then we recommend no pilot. The first fit check is free and without obligation.

Which workflow should we validate?

Choose an audience route or let Pilot Architect structure the first system and validation hypothesis.

PRIVACY · GOVERNANCE · CONTROL

Trust is a system requirement.

AIRA treats data minimisation, purpose boundaries, human review, traceability and deployment-specific governance as design constraints—not as a legal badge added afterwards.

We avoid blanket claims that every deployment is automatically GDPR- or AI Act-compliant; applicable obligations depend on the organisation, data, role and use case.

Information security

ISO/IEC 27001 readiness

Security management, access, risk and operational controls are developed against an ISO/IEC 27001 readiness baseline.

AI management

ISO/IEC 42001 informed

AI governance, human oversight and accountable lifecycle management are designed with ISO/IEC 42001 as a relevant reference framework.

AI risk management

ISO/IEC 23894 informed

AI risk identification, assessment, treatment and monitoring are informed by the ISO/IEC 23894 risk-management framework.

Reference frameworks and readiness direction only; this does not claim certification.