BandiRadar

Open-source engine that monitors Italian public funding opportunities (public tenders, grants, incentives), normalizes them into one canonical model, and ranks them against a company profile with a two-stage matcher.

Runs offline, zero secrets · 3 live key-less sources · optional LLM Stage-2 · MIT

Features

• Two-stage matcher — a deterministic prefilter + LLM relevance scoring, with a zero-secrets offline heuristic fallback (the LLM is optional).
• 3 live, key-less sources — TED (EU), incentivi.gov.it (national), Regione Lombardia (regional); plus a bundled ANAC OCDS mapper.
• ANAC historical-benchmark enrichment — value/volume/seasonality context per CPV division, optionally attached to matches.
• watch monitor loop (new/amended deltas) + JSON/RSS export.
• CLI + MCP server — drive it from a shell or from an AI agent.
• Fully offline on --sample — every demo and the whole test suite run with no network and no secrets.

Table of contents

• Quickstart
• Works across company types
• How it works
• Stage 2: LLM scoring
• Sources
• Intelligence and benchmarks
• Watch and export
• AI agents (MCP)
• Status
• Open core vs Pro
• Roadmap
• Contributing
• License · Data and licenses

Quickstart

30 seconds, offline, no keys:

uv sync
uv run bandiradar match --profile data/profiles/mayai.yaml --sample

Real output on the bundled sample data:

3 matching opportunities for 'MayAI':

#1  score 76  [closing_soon]  Fornitura di licenze software e servizi cloud GDPR-compliant
     issuer: Regione Lazio (Lazio)   deadline: 2026-06-08
     why: CPV prefix match (depth 2); capability overlap: cloud, digitalizzazione, gdpr, processi, software; within profile value range
     https://example.invalid/anac/notice/ocds-bandi-0002

#2  score 72  [open]  Servizi di analisi dati e machine learning per la PA centrale
     issuer: Ministero dell'Economia e delle Finanze (Lazio)   deadline: 2026-12-01
     why: CPV prefix match (depth 2); capability overlap: automazione, data, dati, learning, machine; within profile value range
     https://example.invalid/anac/notice/ocds-bandi-0004

#3  score 66  [open]  Servizi di sviluppo e manutenzione software gestionale comunale
     issuer: Comune di Roma Capitale (Lazio)   deadline: 2026-09-15
     why: CPV prefix match (depth 2); capability overlap: dati, software; within profile value range
     https://example.invalid/anac/notice/ocds-bandi-0001

Add --json for machine-readable output. These ANAC sample URLs are synthetic (example.invalid) — see Status.

Works across company types

BandiRadar isn't tuned to one company — it runs any profile against every source. bandiradar batch runs the bundled profile suite and compares results. Real output on --sample (offline heuristic):

PROFILE                          #  TOP MATCH (score)                      BY SOURCE
------------------------------------------------------------------------------------
Consulenza Strategica S.r.l.     8  Servizi di consulenza organizza… (72)  anac:2 incentivi:5 ted:1
Costruzioni Lombarde S.r.l.      2  LAVORI DI FORMAZIONE MANUTENZIO… (56)  lombardia:1 ted:1
Trattoria & Bottega S.r.l.       2  Manifestazione d'interesse per … (55)  incentivi:2
Manifattura Esempio S.r.l.       3  Fornitura di macchinari industr… (76)  anac:1 incentivi:2
MayAI                            5  Fornitura di licenze software e… (76)  anac:3 incentivi:1 ted:1
MedForniture Lombardia S.r.l.    3  FORNITURA DI DISPOSITIVI PER EN… (76)  lombardia:3
Studio Associato Commercialis…   4  Fornitura di licenze software e… (50)  anac:1 incentivi:2 ted:1

The suite spans distinct Italian SME segments — AI/software (MayAI), manufacturing, medical-devices (Lombardy), accounting, construction, hospitality/retail (keyword-driven, no CPV), and consultancy. Counts are real matches on the tiny bundled sample; a segment can legitimately show few hits when the sample doesn't cover it. Keyword/capability overlap ignores a curated list of generic procurement filler (lavori, servizi, fornitura, manutenzione, …), so matches reflect sector-bearing terms rather than boilerplate.

uv run bandiradar batch --sample              # human comparison table
uv run bandiradar batch --sample --json       # machine-readable
uv run bandiradar batch --sample --csv out.csv

With an LLM key the same table gets sharper scores and ranking — see Stage 2: LLM scoring.

How it works

        ┌─────────┐   ┌───────────┐   ┌────────┐   ┌────────┐   ┌──────────┐
 sources│ INGEST  │──▶│ NORMALIZE │──▶│ STORE  │──▶│ MATCH  │──▶│ DELIVER  │
        └─────────┘   └───────────┘   └────────┘   └────────┘   └──────────┘
            fetch        raw→canonical   sqlite       2 stages     cli/mcp
                                         + dedupe                  (dashboard=pro)

Italian public funding is scattered across dozens of fragmented sources. BandiRadar pulls opportunities from those sources, maps each into a single canonical Opportunity model, and surfaces the few that matter for a given company — with reasons and deadlines. Matching is two stages:

1. Deterministic prefilter — a pure, explainable function (region/geo, value range, deadline, exclusions, and a relevance signal: the opportunity's CPV codes prefix-matched against the profile's cpv_interests, or a keyword overlap). Cuts thousands of rows to dozens. No LLM, no network.
2. LLM relevance — scores the survivors 0–100 with reasons, matched capabilities, eligibility flags, and risk notes. It ships with a zero-secrets offline fallback (a deterministic heuristic), so the whole thing runs in CI and in agent dev loops without any API key.

A thin core service layer orchestrates the pipeline; the CLI and MCP server are shells over it with no business logic. Storage is stdlib SQLite with change detection: a changed content_hash bumps the version, marks the row amended, and makes it re-notifiable (a tender rettifica should re-notify). See ARCHITECTURE.md for the full design.

Stage 2: LLM scoring

Stage 2 is off by default (zero secrets → deterministic offline heuristic). To enable real LLM relevance scoring:

uv sync --extra anthropic        # or: --extra openai  (optional SDKs)
# in .env (gitignored):
#   BANDIRADAR_LLM_PROVIDER=anthropic        # or openai
#   ANTHROPIC_API_KEY=sk-ant-...             # or OPENAI_API_KEY=...
#   BANDIRADAR_LLM_MODEL=...                 # optional; defaults to a cheap Haiku-class model

.env is auto-loaded — no manual export. With no key (or no SDK), the engine falls back to the heuristic, so CI and offline runs need nothing. When the LLM path is active you'll see a one-time scoring via anthropic:<model> on stderr.

The LLM is more discriminating than the heuristic — same prefiltered set, sharper scores/ranking (bandiradar batch --sample):

                                  heuristic        LLM (anthropic, Haiku)
Costruzioni  (real construction)    56               92   ← genuine match promoted
Costruzioni  (IT doc-digitization)  (kept ~36)       15   ← cross-sector match demoted
MayAI        top match            software-licenses  ML/data tender (88)
Studio comm. software-licenses      50               25   ← weak fit penalized
MedForniture medical devices        76               92   ← strong sector fit held

Sources

Source	What it delivers	Live fetch
incentivi	incentivi.gov.it (MIMIT) — the national catalogue of business incentives / grants (kind="incentive"), national and regional. The grant side, and the source a digital SME profile actually matches.	✅ Wired — the official IODL open-data export, no API key.
ted	TED — Tenders Electronic Daily, the EU's portal for above-threshold, OPEN, biddable tenders (includes large Italian public tenders).	✅ Wired — anonymous, no API key.
lombardia	Regione Lombardia — regional / sub-threshold public tenders (kind="tender"), from the Osservatorio Regionale (Socrata SODA). The first regional source; carries CPV, value, and province.	✅ Wired — Socrata SODA, no API key.
anac	ANAC / PNCP open-contracting (OCDS) data — primarily historical / award records, a separate analytics track rather than open calls.	⏳ Mapper + fixture done; live fetch() not wired.

uv run bandiradar fetch --source incentivi --sample   # offline, bundled real capture
uv run bandiradar match --profile data/profiles/mayai.yaml --source incentivi --sample
uv run bandiradar match --profile data/profiles/mayai.yaml --source ted --sample

The --sample fixtures are real captures (data/fixtures/*.json). incentivi exercises the canonical superset on the grant side — no CPV, a funding range, and an eligibility text the matcher reads. TED carries above-threshold contracts often far larger than a micro-SME's range, so a small profile matches only the few that fit — which is why incentive/national/regional sources matter too.

A regional example (data/profiles/medtech_lombardia.yaml, a Lombardy medical-devices distributor) matches open Lombardy tenders, while the Lazio-only MayAI profile correctly drops them — regional filtering in action:

uv run bandiradar match --profile data/profiles/medtech_lombardia.yaml --source lombardia --sample
# -> 3 open medical-device tenders (region match, CPV 33*, within value range)
uv run bandiradar match --profile data/profiles/mayai.yaml --source lombardia --sample
# -> No matching opportunities (Lazio profile, Lombardy bandi dropped on region)

Source data licensing is consolidated under Data and licenses.

Intelligence and benchmarks

A separate track (not the matcher) ingests ANAC historical OCDS data — awarded public contracts — and computes compact benchmarks per CPV-division × region: award value distribution (median, p25/p75, min/max), volume, seasonality (by year), and distinct-supplier counts.

uv run bandiradar benchmarks build --sample          # offline, bundled real capture
uv run bandiradar benchmarks show --cpv 45           # region falls back to national

Real output on the bundled sample:

CPV division 45  [national]
  awards (count): 22   distinct suppliers: 21
  value EUR: median 470,768  p25 121,649  p75 1,594,879
  range: 68,117 – 11,369,083
  by year: 2022:22

Honest data caveats:

• The dataset is retrospective — awarded contracts (> €40k), not open calls.
• It has awards + suppliers but no tenderers list, so we cannot derive a "number of bidders". We derive value/volume/seasonality/supplier counts only.
• The release addresses carry city + postal code but no region/NUTS, so benchmarks are national-only for now (region stays None); the model and aggregation already support regional buckets for when a region-bearing source arrives.

Enrichment: benchmarks in the matcher

The benchmarks are optional matcher enrichment (injected like the score cache — the matcher works fine without them). Add --with-benchmarks to match and each scored opportunity gets, for its CPV division, a historical-context reason plus a value-sanity risk note when it declares an estimated value:

uv run bandiradar benchmarks build --sample
uv run bandiradar match --profile data/profiles/mayai.yaml --source ted --sample --with-benchmarks

#1  score 44  [open]  Italia – Servizi di gestione dati – SERVIZIO DI GESTIONE ... ROCCA IMPERIALE (CS)
     why: CPV prefix match (depth 2); capability overlap: dati; eu scope; ANAC history (CPV 72, national): 8 awards, median EUR 104,326, p25-p75 EUR 71,619-183,410
     https://ted.europa.eu/en/notice/-/detail/376324-2026

Value-sanity triggers when the opportunity declares a value — e.g. on the ANAC sample (--source anac --with-benchmarks):

#  Servizi di analisi dati e machine learning per la PA centrale
   why: ...; ANAC history (CPV 72, national): 8 awards, median EUR 104,326, p25-p75 EUR 71,619-183,410
   risk: estimated value EUR 250,000 is above the historical p75 EUR 183,410 for this category

Enrichment is append-only on a copy of the cached match: the cache always stores the bare match, so repeated runs never double-append. The search_opportunities MCP tool takes the same with_benchmarks flag. ANAC data licensing is under Data and licenses.

Watch and export

watch is a monitor loop: it fetches, applies the storage change-detection, and reports only matches whose opportunity is new or amended since the last watch run (a per-profile marker is persisted; --since overrides it).

# 1st run: all current matches are "new"; a 2nd run reports nothing new
uv run bandiradar watch --profile data/profiles/mayai.yaml --source incentivi --sample
# write a feed instead of printing
uv run bandiradar watch --profile data/profiles/mayai.yaml --source incentivi --sample --rss ~/feed.xml

export is the full, non-delta dump of current matches (--json or --rss PATH).

Scheduling is your cron — this is open-core (single-user/local). For example:

0 8 * * *  cd /path/to/bandiradar && uv run bandiradar watch --profile mine.yaml --rss ~/feed.xml

Managed delivery (WhatsApp/email/alerts), scheduling SaaS, and multi-tenant hosting live in bandiradar-pro.

AI agents (MCP)

BandiRadar ships a thin MCP server (FastMCP), so you can drive it from Claude. Six tools:

list_sources · fetch_opportunities · search_opportunities · score_opportunity · get_matches · get_profile

uv run bandiradar mcp

Registration and an offline example session are in docs/MCP.md.

Status

• ✅ Offline, zero-secret — every demo above and the whole test suite run with no network and no API key.
• ✅ 3 live key-less sources — incentivi, ted, lombardia. --sample keeps them offline against recorded real captures.
• ✅ Stage-2 LLM scoring is wired and working (optional); with no key it transparently uses the offline heuristic — a proxy, not real semantic relevance.
• ⏳ Live ANAC/PNCP fetch is pending — the mapper + fixture are done and tested, but fetch() raises NotImplementedError until the open-data endpoint is confirmed against current docs. The ANAC sample URLs are synthetic.

Open core vs Pro

Anything a single user can run locally is open. Anything managed, multi-client, or a delivery channel lives in the private bandiradar-pro, which depends on this package — never the reverse.

	bandiradar (this repo, MIT)	bandiradar-pro (private)
Engine (ingest/normalize/match)	✅	imports it
Source framework (Source interface + registry)	✅
Reference adapters (ANAC, incentivi.gov.it)	✅
Two-stage matcher (incl. offline fallback)	✅
CLI + MCP server	✅
Dashboard (web UI)		✅
Premium / regional source adapters		✅
Delivery channels (WhatsApp, email, alerts)		✅
Multi-tenant, managed hosting, scheduling SaaS		✅

Roadmap

Shipped

• Canonical model + Source framework + two-stage matcher (deterministic prefilter + LLM relevance with a zero-secrets offline fallback) + SQLite with change-detection + CLI + MCP server.
• Live sources: TED (EU open tenders), incentivi.gov.it (national incentives), and Regione Lombardia (first regional source), all key-less; ANAC OCDS mapper bundled.
• Intelligence track: ANAC historical benchmarks + optional matcher enrichment (--with-benchmarks).
• watch monitor loop (new/amended deltas) + JSON/RSS export.

Upcoming

• Live ANAC/PNCP fetch (confirm the open-data endpoint first).
• Embeddings-based prefilter; more community/regional source adapters (via the Source framework — Lombardy is the first; other regions welcome).
• bandiradar-pro (private): dashboard, WhatsApp/email delivery, scheduling SaaS, multi-tenant hosting.

Contributing

Every source is fetch + a pure to_opportunities, plus a recorded fixture and a test — adding one is a new file, no core changes. See CONTRIBUTING.md and the add-a-source skill (skills/add-a-source/) for the full copy-pasteable template; the playbook also lives in CLAUDE.md ("How to add a new Source").

License

MIT © MayAI — see LICENSE.

Data and licenses

BandiRadar consumes public open data; each source keeps its own licence, which its operator requires you to honor:

• incentivi.gov.it (IODL 2.0) — published by the Ministero delle Imprese e del Made in Italy under the Italian Open Data License v2.0 (attribution required). The live incentivi fetch hits the same open-data export endpoint the portal's own "Scarica dataset" button uses (no separate static file; the download is built client-side from that endpoint).
• Regione Lombardia (CC0 1.0) — dataset k6cb-4hbm (Bandi di gara — Osservatorio Regionale), via the dati.lombardia.it Socrata SODA API.
• ANAC public contracts (CC BY 4.0) — via the Open Contracting Data mirror; the intelligence track streams the gzipped JSONL memory-safely.