AI certification with practical proof, evidence a manager can actually show.
What participants post publicly
No polished testimonial deck, just real posts published right after the hackathons.
The posts are loaded from LinkedIn, which may process data and set cookies of its own. They only load once you request them here.
The evidence gap this closes
High search demand for "ai certification" hides a harder question. Most buyers do not want a certificate for its own sake, they want credible evidence that people can use AI responsibly, and something they can put in front of an auditor, a board or a regulator without overstating it. A certificate-only path leaves four gaps.
No demonstrated capability. A badge records that someone sat through a course, not that they can now do a task with AI. Evidence is the part that is missing.
Nothing to show under scrutiny. When a manager is asked what the training achieved, a completion list is thin. A built artifact and documented outputs are not.
Overclaiming risk. A course that promises "AI Act compliance" invites trouble, because Article 4 asks a company to assess measures by role and risk, not to buy a stamp.
Skill and proof are separate. The certificate lives in one file and the actual capability, if any, lives somewhere else, so the record does not describe reality.
An evidence-first record closes the gap by making the proof and the capability the same thing. The person is credentialed on work they demonstrably did.
Goals of the certification path
The aim is a record that describes reality and holds up when questioned, not a stamp that sits in a folder.
A competence record for each participant, tied to a prototype they built and outputs that exist.
A documented AI literacy measure that can support EU AI Act Article 4 as one building block, framed honestly.
Evidence a manager, HR partner or compliance lead can show without overstating what it means.
A capability that is real, because the record is based on work the person did rather than a passive test.
What the programme entails
This is a guided, one-week path where the certification is a by-product of doing real work, not the reason for it. Discovery, tool setup, challenge design, facilitation, a prototype sprint and a documented handoff. The evidence is generated as the team builds, so the record and the capability are one and the same.
A typical sprint has four interlocking parts:
- Challenge scoping. Each group takes a real, narrow use case with a named user and a desired output, so the evidence produced describes a genuine work task, not an exercise.
- Tool workshop. Before building, each group learns exactly the stack its challenge needs, and participation is recorded as part of the literacy measure.
- Build phase. Teams build with a coach beside them. The work, the decisions and the outputs are documented, which is what turns activity into evidence.
- Pitch, handoff and record. Each team demonstrates its result and hands over a documented artifact, and each participant receives a competence record tied to what they actually built.
Deliverables
Pre-scoping with a per-team challenge design.
A curated tool stack per challenge, including access.
A facilitated build sprint with a coach present throughout.
At least one working prototype per team, with documented outputs that serve as evidence.
A per-participant competence record tied to the work they did.
A documented AI literacy measure for the cohort, framed for the EU AI Act Article 4 discussion.
A handoff document per prototype with owner, access, open risks, acceptance criterion and next step.
Who it fits, and who it does not
Honest fit saves both sides time. An evidence-first certification path is the stronger lever when these points hold. When they do not, we say so.
| Good fit when | Not a fit when |
|---|---|
| you need a record that survives scrutiny from HR, compliance or a board | a simple attendance badge already satisfies your requirement |
| you want the credential tied to demonstrated work, not a passive test | you specifically need an accredited third-party exam we do not issue |
| data and tool access can be approved in principle | real data cannot be touched for legal reasons under any circumstances |
| you accept a qualified Article 4 claim, honestly framed | you want a guarantee of legal compliance, which no training can give |
Certificate-only path vs evidence-first record
| Criterion | Certificate-only path | AI Hackathons evidence-first record |
|---|---|---|
| What it proves | attendance | a demonstrated capability on real work |
| What you can show | a completion list | a built artifact, documented outputs and a literacy measure |
| Relationship to skill | often separate | the record and the capability are the same |
| Article 4 framing | risk of overclaiming | a qualified measure, honestly framed |
| Durability under audit | thin | backed by evidence that exists |
| Time to first result | weeks to months | ready to start in one week, evidence depends on scope |
The table compares ways of working, not vendors, and deliberately carries no invented percentages. What the record can support depends on your own roles and risk assessment. A hundred people who can demonstrate real work outweigh a thousand who only hold a certificate, which is why we certify on demonstrated work, not on attendance.
How the value can add up (a model, not a client number)
An honest view of the payback uses your own figures, not a borrowed case number. Beyond the built artifact, an evidence-first record has a second value, the audit and reporting time it saves.
Weekly or annual time your team spends assembling evidence of AI competence for an audit or internal review.
The share that a ready, documented competence record removes.
Internal hourly rate times the number of people involved in that assembly.
Formula: hours saved on evidence assembly × hourly rate × the number of reviews per year × people − the one-off cost of the sprint. Put your own numbers in. As an illustrative model only: if preparing AI competence evidence for a review costs a compliance lead twenty hours, and a ready record removes fifteen of them across two reviews a year, that is thirty hours you can weigh the investment against honestly, before counting the value of the prototype itself. We run this in the discovery call with your real numbers, not ours.
Interactive calculator: /tools/ai-enablement-readiness/.
Does this match your requirement? Decide with a person, or read first.
The one-week path, as an animated timeline
| # | Step | Owner | Output |
|---|---|---|---|
| 1 | Intro call | AI Hackathons + sponsor | agreed goal, rough scope, date |
| 2 | Tools and challenges call | AI Hackathons + team leads | challenges scoped, tool stack chosen, evidence plan agreed |
| 3 | Finalise info and data | Client (IT, privacy) | approved data access, documented boundaries |
| 4 | Hackathon prep | AI Hackathons | prepared environments, access, run of show |
| 5 | Tool workshop | AI Hackathons + teams | participation recorded, each team can operate its stack |
| 6 | Hackathon sprint | Teams (coached) | working prototypes, documented outputs as evidence |
| 7 | Pitches, handoff and record | Teams + owner | competence records, handoff documents, literacy measure |
Social proof
Teams from Adobe, YOYABA, Onventis and NavVis have worked with us.
See the case studies
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