For municipalities
Six weeks, not six months.
See the signal early and you have a six-week window. Work from annual statistics and you have a six-month crisis.
Not a failure of your team. A failure of the instrumentation, and fixable.
Why this matters now
Born outside Spain
25.1%
More than one in four Catalonia residents, 2.04 million people.
SOURCE: IDESCAT · 2025
Catalan municipalities
36
Where the foreign-born share of residents already exceeds 25%.
SOURCE: IDESCAT · 2025
Statistical lag
12–24 mo
Typical age of the national data a municipal director is working from.
SOURCE: INE AND IDESCAT RELEASE CALENDARS · 2026 · MODELLED
Module 02
The Social Observatory
Neighbourhood-level early-warning intelligence. Monitors integration signals, cohesion risk indicators, and service demand shifts, and surfaces the change while there is still a window to respond.
What a Monday morning looks like
Each row is a neighbourhood, a signal, a timestamp, and a confidence value. The platform says what moved and which indicators drove it. Your team decides.
Observatory · alert feed · week 34
Sample dataBarri A
Social services first-contact volume up against the twelve-week baseline, concentrated in two service points.
Barri D
School enrolment enquiries outside the ordinary window, sustained across three reporting cycles.
Barri F
Employment office registrations rising while vacancy postings in the same sector also rise.
Barri B
Housing advice contacts returned to baseline after six weeks above it.
Barri F sits below the reporting threshold this week. It is shown, flagged, and excluded from the composite index. There is no setting that hides it.
The neighbourhood view
A single-hue ramp of five steps, against pinned boundary definitions. Where a neighbourhood has insufficient data, the map says so rather than interpolating across the gap. A map that guesses is worse than a map with a hole in it.
Composite cohesion index · by neighbourhood
Sample data- Lowest
- Low
- Mid
- High
- Highest
- Action window open
- Insufficient data, never interpolated
Where the data comes from
- Municipal administrative data, anonymised at the ingestion boundary
- INE, Idescat, SEPE, and Social Security open data
- PostGIS with OpenStreetMap, Catalan cadastral, and Idescat boundaries, version-pinned
Indicators below the confidence threshold are excluded from the composite cohesion index and flagged in the report, not quietly dropped. The report says which ones and why.
Show the data table
| Period | Published statistics, months old | Observatory signal, months old |
|---|---|---|
| Q1 | 14 | 1 |
| Q2 | 16 | 1 |
| Now | 12 | 1 |
| Q4 | 15 | 1 |
| Q1 next year | 17 | 1 |
Module 04
The Policy Simulator
Agent-based simulation that lets a municipality model the effect of an integration policy decision before making it.
One worked scenario
A municipality is deciding where to place additional language-support capacity for the coming academic year. It has enough funding for one site, and three candidate districts.
The simulator models each placement against the current cohort distribution, existing service capacity, and travel time from residence to site. It returns the projected share of the eligible cohort reached within twelve months under each option, with the assumptions stated on the same view.
The director takes both the result and the assumptions to the council. The council decides. The platform did not.
Every simulation output is dashed in charts and captioned modelled. A simulation is never presented as a measurement.
Placement scenario · projected cohort reach
Sample dataShow the data table
| Placement option | Projected cohort reach at 12 months |
|---|---|
| District A | 52% |
| District B | 68% |
| District C | 41% |
| No change | 29% |
What a pilot delivers
The report is the product.
A pilot is designed backwards from a publishable impact report. Every artefact below is committed in the engagement, with the week it lands and the person it is written for.
| Deliverable | Delivered | Format | Written for |
|---|---|---|---|
| Diagnostic report | Week 2 | PDF and working session | Innovation director, DPO |
| Baseline report | Week 6 | PDF with accessible data tables | Social services chief, innovation director |
| Weekly signal report | Weekly from week 7 | PDF and platform view | Operational team |
| Monthly analytical report | Monthly from week 10 | PDF with sources and limitations | Innovation director, council briefing |
| Methodology and limitations record | Week 12 | PDF, versioned | DPO, academic reviewers, auditor |
| Impact report, ESF+ ready | Week 24 | PDF and structured data export | Funder, managing authority, council |
Source: Humaniwork pilot engagement specification, 2026
Procurement and funding
Five routes to fund this, and what we do on each.
We prepare the application. Your team signs it.
Red.es and SEDIA run design contests that procure a pilot directly, in the €50,000 to €150,000 band, without a full open tender.
What we do
We are registered on Red.es and SEDIA and hold a prepared application template. We draft the technical annex, the compliance section, and the evaluation criteria mapping. Your team signs it.
Employment, inclusion, and skills programmes, routed through the Generalitat and the Spanish managing authority.
What we do
The pilot is designed backwards from an ESF+-ready report. We provide the indicator framework, the baseline methodology, and the output documentation in the format the managing authority asks for.
Integration measures at municipal and regional level.
What we do
We supply the evidence base and the monitoring framework for the application, and the reporting artefacts for the drawdown.
The Catalan neighbourhood regeneration instrument, which requires documented diagnosis at neighbourhood level.
What we do
The Observatory produces exactly the neighbourhood-level diagnosis the instrument requires, with the method and the limitations included.
National programme requiring documented integration and cohesion outcomes.
What we do
We map the Observatory indicator set onto the plan reporting requirements and produce the outcome documentation.
Built for the DPO
Compliance is the doorway, not the disclaimer.
Your Data Protection Officer will read this before you reply to us. It is written for them.
Anonymisation at ingestion
No individual-level data reaches the AI layer.
No individual-level data enters the AI processing layer. Analysis runs on aggregated and pseudonymised data, and the rule is enforced at the pipeline boundary rather than by convention.EU data residency
EU infrastructure, Catalan residency for Catalan deployments.
Infrastructure in EU jurisdiction, with Catalan data residency for Catalan deployments. The sub-processor list is published and versioned.Per-module EU AI Act classification
Classified per module, before deployment, because they differ.
The classification is documented per module before deployment, because the modules are not equivalent. Employment-related matching is treated under the high-risk obligations. The civic analysis modules are advisory decision-support for institutions.A human decides
The platform ranks and explains. A person decides.
The platform ranks, explains, and projects. A qualified human makes every hiring and every policy decision, and the decision is logged against their identity.DPA before ingestion
Signed, and your DPO briefed, before a record moves.
A signed Data Processing Agreement and a briefed Data Protection Officer before a single record moves. The system refuses to create a data source without a recorded DPA reference.Exportable audit trail
Every output keeps what produced it, and exports.
Every match, alert, and projection persists its inputs, model version, driving features, and confidence value. Exportable for labour inspection and public audit.
EU AI Act classification, per module
| Module | Classification | What that means in practice |
|---|---|---|
| 01 Workforce Matching | High-risk obligations | Employment-related matching is treated under the high-risk obligations: risk management, data governance, logging, human oversight, transparency, and accuracy documentation. The classification and its reasoning are versioned in the repository and ship with every release. |
| 02 Social Observatory | Advisory decision-support | Civic analysis is advisory decision-support for an institution. It produces no individual-level output and no automated decision. The classification and its reasoning are versioned in the repository. |
| 03 Integration Intelligence | Advisory decision-support | Advisory decision-support at cohort level. No individual-level output, no automated decision. Classification versioned in the repository. |
| 04 Policy Simulator | Advisory decision-support | Simulation output is advisory decision-support and is labelled modelled everywhere it appears. Classification versioned in the repository. |
| 05 Employer Intelligence | Advisory decision-support | Cohort-level analytics with an aggregation floor applied at query time. It produces no individual-level risk score and cannot be filtered down to one. Classification versioned in the repository. |
Source: Humaniwork compliance repository, versioned per release, 2026
What you keep
You keep everything.
Whatever we build from your data, you keep. The raw files, the derived datasets, the methodology, and the platform access. If you stopped working with us tomorrow, you would still hold everything we produced together.
A consultancy keeps the model
A software vendor keeps the data
You keep both
| What you get | Format | When |
|---|---|---|
| Every raw and derived dataset we build from your data | CSV or Excel, with a data dictionary | Rolling, from the first pipeline run |
| The indicator and index definitions, in full | Written methodology document | Diagnostic phase, before anything is computed |
| Data pipeline documentation and runbook | Technical PDF and configuration | On pipeline delivery |
| Every intelligence brief and report | PDF, bilingual as standard | On the delivery cadence |
| Model parameters and configuration | Documented and transferred to your records | Handover phase |
| Platform access for your named users | Live, role-based | From setup onward |
| Knowledge transfer workshop | Half a day, with your team | Handover phase |
And, for this engagement specifically
- The pre-filled EU fund reporting templates, ready for the application rather than summarised for it.
- The neighbourhood cohesion index as a dataset, in CSV, with the visualisations.
Pricing
What it costs, stated plainly.
We publish the band rather than a starting-from figure, because a procurement officer who cannot budget for a thing cannot begin the process for it.
Annual platform licence
€60,000 – €150,000
Scaled on resident population and the modules deployed.
- Social Observatory across the agreed neighbourhood boundaries
- Policy Simulator running the reviewed parameter set
- Weekly signal report and monthly analytical report
- Data Processing Agreement, DPIA support, and the per-module AI Act classification pack
- Onboarding, indicator design, and named analyst support
Pilot and GovTech contest engagement
€50,000 – €150,000
Single-municipality pilot, typically procured through a GovTech Design Contest.
- Two-week Diagnostic Sprint
- Observatory deployed on real municipal data
- A published, ESF+-ready impact report at the end of the engagement
- Support on the contest or funding application itself
The GovTech Design Contest band (€50K–€150K) is set by Red.es and SEDIA, not by us.
All figures are annual and all-inclusive. Onboarding is included. There are no discounts, no bundles, and no limited-time pricing.
Reviewed 2026-01 · EUR
Start with a diagnostic conversation.
Not a demo. A conversation about what you are trying to decide, what timeline you are working to, and what data you already hold. If the answer is that the platform cannot help you yet, we would rather say that in the first conversation than in month four.