Labour mobility and skills intelligence

Plan labour corridors before shortages become crises.

ForeSkill helps institutions organize labour mobility pathways — starting from forecasting occupational shortages, comparing skills across countries, and designing evidence-based corridors — with data that is traceable to its source.

The first European system to organize international labour mobility pathways, starting from the forecasting of occupational mismatch.

NATIONAL DASHBOARD- ITALY
First pilot
Aggregated indicators, Italy
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Co-developed by IZILab, Pathways International and Migration AI Studio — in partnership with People Gateway, calibrated in the Turin and Piedmont institutional context.

The problem

Mismatch is not one thing — it is four gaps.

Labour markets across Europe are tightening just as migration governance is being rewritten, against a backdrop of an ageing workforce and a shrinking working-age population. Persistent shortages in care, construction, ICT and manufacturing meet fragmented qualification-recognition systems that leave real skills unread. Most tools report the resulting imbalance as a single number — but the mismatch is really four structurally different gaps, each with its own cause and its own response. ForeSkill reads all four at once.

  1. 01

    Quantity gap

    quantitative mismatch

    Not enough people. There aren't enough profiles to meet demand.

    Read with predictive models on historical time series.

  2. 02

    Skill gap

    qualitative mismatch

    People exist, skills don't match. The profiles are there, but their competences don't fit the roles.

    Read with semantic analysis of job postings and CVs.

  3. 03

    Mobility gap

    mobility mismatch

    People exist, but in the wrong place. The talent is available in another territory, not connected to local demand.

    Read by mapping flows.

  4. 04

    Recognition gap

    recognition mismatch

    Skills exist, but institutions can't read them. Real competences stay invisible without certification.

    Resolved by aligning ESCO and EQF frameworks.

See it in action

Real data, not a mockup.

An operational system is one you can interrogate, not just admire. This map runs on the real 2025 Excelsior/Unioncamere and ISTAT datasets behind ForeSkill's analysis — the same data infrastructure used in every client engagement. What you see here is a general-purpose demonstration of what the platform can read, not a commissioned corridor: explore any province, category or sector freely. A specific origin-destination corridor, scoped to your priorities, is built through a paid engagement — get in touch to commission one.

Dimension
Showing category
Choropleth
A CASE STUDY

The Piedmont–Colombia corridor.

Where will Piedmont need skilled ICT and manufacturing workers between 2025 and 2030, and which Colombia–Italy corridors are most feasible in terms of qualification recognition?
Question

Priority provinces, target occupations, feasible corridors, recognition bottlenecks.

Data used

Excelsior, ISTAT, regional employment data, Colombian GEIH 2025, diaspora and social-security indicators.

Method

Demand forecasting by province and sector, ESCO/EQF recognition analysis, corridor feasibility by time horizon.

Output
  • Turin: 101,600 ICT positions — 53,600 of them expanding demand (60% of the regional total).
  • Cuneo: strong manufacturing, oriented to Industry 4.0.
  • Biella, Vercelli, Asti: expanding ICT demand at zero.
  • Corridors by horizon — Trade 2025–27 · Manufacturing 2027–30 · ICT 2030–35.
  • ~8,000 Colombian residents already in Piedmont.
  • Recognition bottleneck — training gaps concentrated on safety certifications and digital-construction competences; Colombian technical qualifications are the most efficient starting point for targeted upskilling.
Decision enabled

Design targeted training, focus bilateral-agreement negotiation, engage employers and diaspora organisations.

Asked of a generalist chatbot, the same question returns: "I don't have this data — would you like me to run a search?" — transparent about its limits, but no actionable output.

How it works

From institutional data to decisions.

ForeSkill turns institutional data into decisions. It queries a curated data lake — kept up to date and integrated with qualitative field research and foresight analysis — and returns answers grounded in real data.

More than 18 integrated source families, and growing

Starting from our country.

Partners use ForeSkill to pinpoint provinces and economic sectors at risk, distinguish replacement demand from expansion demand, compare required skills across origin and destination countries, and evaluate qualification-recognition barriers. On that basis they design targeted training paths, score corridor feasibility over time, and monitor each corridor as conditions evolve — turning intelligence into a sequence of concrete actions.

  • Labour & occupational demandExcelsior, GEIH (DANE)
  • Demographic & economicISTAT, Eurostat
  • Training & educationMIUR, MUR
  • Migration & social securityINPS, entry and residence records
  • System benchmarksBA, BIBB, IAB (Germany), UBOS (Uganda)
Forecast horizons — validated Italian time series 2020–2025 with projections to 2030; a 2–3 year horizon for adaptive skills matching; scenario planning to 2035 for corridor design.
Traceable by design

Every number is source-traceable. Every recommendation is auditable.

A built-in validation layer checks each figure against its source before it reaches you — outputs that don't pass the quality gate are rejected and regenerated. Results are decision-support insights grounded in data, not black-box predictions.

Every answer carries the tools and sources behind it.

Unlike a generalist AI assistant, ForeSkill:

  • Distinguishes expansion demand from replacement demand — not a single mismatch number.
  • Scores corridor feasibility by time horizon, not just skill matching.
  • Incorporates diaspora and social-security data, not only labour statistics.
  • Draws only from named, curated institutional sources — nothing scraped from the open web.
Method

The method, in seven steps.

Not a chatbot over a folder of PDFs — a structured analytical pipeline:

  1. 01

    Data ingestion and harmonisation across institutional sources.

  2. 02

    Occupational taxonomy mapping (ESCO and national frameworks referenced to EQF).

  3. 03

    Time-series forecasting of occupational demand.

  4. 04

    Semantic matching of skills to demand.

  5. 05

    Corridor feasibility scoring between origin and destination.

  6. 06

    Qualification and recognition analysis.

  7. 07

    A source-traceable, trilingual (IT / EN / ES) agent interface.

Under the hood, ForeSkill runs on IZILab's Chorema Intelligence Platform.

Where it works

Built to expand, proven in four places.

ForeSkill reads the labour market as a system of flows between territories. The architecture is territory- and profession-agnostic: each geography is activated on partner request, and every corridor is built around the same underlying data model.

Coverage

Demand-driven today. Transferable to anything.

The four geographies below are what's live today — activated on partner request, not the limit of what ForeSkill can read. The platform covers selected destination and origin countries, and selected occupational domains (for example ICT, manufacturing and construction). The architecture is territory- and profession-agnostic: any origin–destination corridor, and any occupational domain, can be brought online. What we cover next is decided by our partners.

Live today, across four geographies with distinct roles.

Piedmont → Italy is the destination context; Colombia and Uganda are countries of origin; Germany is the system benchmark.

Piedmont → Italy

Destination context (calibration)

Provincial granularity, 500+ documents processed, 2020–2025 time series with projections to 2030. The context on which the architecture was built and validated, and already extended across the whole of Italy.

Colombia

Country of origin (first international expansion)

The most complete survey of the Colombian labour market (GEIH 2025) — 12-month coverage, 33 departments, urban/rural disaggregation, ISIC Rev. 4 sector analysis. The GEIH Intelligence module enables microdata-based analysis at departmental granularity.

Uganda

Country of origin (in integration)

The project's second priority country of origin — analysing available skills and mapping diaspora communities, integrating Ugandan institutional sources (UBOS).

Germany

System benchmark

The German system of dual training, qualification recognition, and labour-corridor management (BA, BIBB, IAB) is a methodological benchmark for practices transferable across Europe.

The model — Knowledge-as-a-Service (KaaS)

A research partnership, not a subscription.

ForeSkill is delivered as Knowledge-as-a-Service — KaaS. Beyond software-as-a-service: you don't licence a tool, you commission knowledge. Partners receive analytical outputs — grounded in curated sources and traceable to origin — through a platform that keeps evolving with their questions.

SaaS

Software you run.

You licence a tool and take on the work of feeding it data, framing the questions, interpreting the outputs, and keeping it aligned with your context over time. The platform is generic; the analytical capacity is yours to build.

KaaS

Answers you can act on — and the analytical capacity behind them.

You commission knowledge. Curated institutional sources, an evolving analytical pipeline, and a source-traceable agent are delivered as a service — configured to your territory and your questions, and refined with you as conditions change.

SaaSKaaS
Data & sourcesYou source, load and keep the data current yourself.Curated institutional sources, kept current as part of the service.
InterpretationYou frame the questions and interpret the outputs.Analytical outputs delivered ready to act on.
Maintenance over timeRealigning the tool to your context is your team's ongoing work.The pipeline evolves with you, as conditions and priorities change.
AccountabilityResults are only as good as the team running the tool.IZILab is accountable for the accuracy and traceability of every output.
How we work together

From a scoped pilot to a continuous intelligence partnership.

What you get

  • A territorial mismatch report.
  • A configured ForeSkill instance with dashboard access.
  • A source-traceable, trilingual analytical agent.
  • A corridor feasibility analysis.
  • A training and qualification-recognition roadmap.
  • Policy briefs for institutional stakeholders.

Governance — Your data and every output belong to you. The platform, models and IP stay with IZILab; each partner works in a private, isolated instance.

Start with a scoped pilot

A bounded engagement built around one territory or corridor and one or two research questions. You receive the analytical outputs and a configured ForeSkill instance; IZILab handles all implementation.

Grow into a continuous intelligence partnership

Ongoing monitoring, dashboards and agentic analysis, regular source updates, and a co-design roadmap shaped by your priorities.

"You are not buying a finished product — you become part of a shared, evolving cognitive infrastructure."
Contact

Get in touch.

Tell us about your territory or corridor, and we'll show you what ForeSkill can read. Your message goes to info@izilab.it.