№ 001 / DELIVERY INTELLIGENCE LUXEMBOURG · EU-27

Europe
the capital.
Execution
is the gap.

Izere is the execution-intelligence layer for EU institutions, DFIs and infrastructure programmes — explainable AI that detects delivery risk 2–3 months earlier, before delays harden into losses.

378B
Cohesion 2021–27
2–3mo
Earlier detection
11%
Worst-on-record absorption
EU-27
Target geography
Live demo · five real EU programmes

Click a programme. See the evidence chain. Run interventions.

The execution gap

Europe can commit capital at scale.
It cannot systematically deliver it.

EU-funded programmes depend on fragmented ecosystems of SMEs, subcontractors, utilities, and regional authorities. Delivery-relevant data is dispersed, qualitative, and not reusable across programmes.
35% of EU public-investment projects experience significant delays, cost overruns, or performance shortfalls. Source · ECA, OECD
€2.3T in EU public procurement annually — execution risk, not capital, is the primary bottleneck. Source · DG GROW
11% of the 2021–2027 Cohesion Policy envelope implemented by August 2025 — worst start on record. Source · DG REGIO
Case study · Rail Baltica

A €18B overrun.
14 months of warning signs.

Rail Baltica is the largest cross-border infrastructure project in EU history. Its cost trebled and timeline doubled. The signals were in the data the whole time — they just weren't being read.

Where Izere would have flagged it.

By May 2024, Latvia's procurement signals across 18 contract notices showed a 47% drop in qualified bidders versus the 2018 baseline. Subcontractor concentration crossed the 0.62 Herfindahl threshold three quarters earlier than ECA detection.

Coordination signals between the three Member States — meeting cadence, document throughput, milestone amendments — diverged sharply in Q3 2024. Izere's graph model would have moved Rail Baltica's risk score from 41 to 78 between June and September.

By the time the European Court of Auditors flagged the programme in January 2026, the cost had compounded for fourteen months. Earlier detection would have surfaced corrective action while the cost gap was still €2.4B — not €18B.

€23.8B
Latest cost estimate
€5.8B
Original baseline
+11.4mo
Izere lead time vs. ECA
2035
Latvia revised completion
Risk trajectory · Rail Baltica
Izere score Traditional monitoring
IZERE FLAG · MAY '24 ECA FLAG · JAN '26 2023 2024 2025 '26
Q1 2024Bidder concentration · Herfindahl index crosses 0.62 threshold across 18 Latvia contract notices.
May 2024Izere risk score moves from 41 → 78. Programme flagged HIGH for the first time.
Q3 2024Coordination divergence · 3-Member-State meeting cadence drops 60%; document throughput halves.
Jan 2026ECA flags programme · 14 months after Izere's first signal. Cost compounded to €23.8B.
NowLatvia signals 2035 completion. Estimated correction window cost: €18B avoidable.
How it works

From fragmented data
to explainable intelligence.

Izere operates as a decision-support layer alongside existing procurement, programme-management, and supervision systems — not replacing them.
STEP 01 / INGEST

Read what programmes already produce.

Milestone reports, change events, coordination signals, governance structures, and disbursement patterns are unified into a single execution data model. API-first; nothing proprietary required.

TED · PPDSESPDArachneAPI-first
STEP 02 / ANALYSE

Score, explain, and trace every output.

Graph neural networks for actor-relationship modelling. SHAP-based explanations on every score. Federated learning so insights compound without data leaving an institution's perimeter.

Graph NNSHAP / XAIFederatedAudit trail
STEP 03 / INTERVENE

Detect risk months before traditional monitoring.

Programme managers and managing authorities receive structured risk intelligence months earlier — enabling timely corrective action while the cost of correction is still small.

Risk scoreAlert feedAudit packPlaybooks
Methodology

Built for institutions that will be audited.

Every Izere output is independently contestable. Model classes, explainability framework, and audit pathways are documented and reviewable by your auditors before deployment.
M.01

Graph neural networks for actor-relationship modelling

Programmes are modelled as dynamic graphs: contracting authorities, suppliers, subcontractors, and dependencies. The model learns structural risk patterns — concentration, brittleness, governance distance — that linear models miss.

M.02

SHAP-based explanations on every score

Every risk score decomposes into the specific signals that drove it, with weights and provenance. No black-box outputs. No score is published without an evidence chain that an institutional auditor can read end-to-end.

M.03

Federated learning across deployments

Models train on insights without raw data ever leaving an institution's perimeter. Cross-programme intelligence compounds; data sovereignty is preserved. GDPR- and EU-AI-Act-compliant by construction.

M.04

Independently reviewable audit pathway

Every model version, training set, and feature is versioned. Institutional auditors can reproduce any score retrospectively. EU AI Act high-risk system documentation is generated continuously, not retrofitted.

If your auditors cannot read it, you should not deploy it.

Izere ships full methodology documentation with every deployment. Model cards, evaluation protocols, and sensitivity analyses are reviewable before procurement.

Book a 45-minute
institutional briefing.

We'll demonstrate the platform against real EU programme data and discuss fit with your specific portfolio. No slide deck — just the system.

USUAL RESPONSE · 1 BUSINESS DAY · LUXEMBOURG TIME · OR EMAIL alain@izereai.com