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HMD Digital
Italian SMEs and consultants screening open public incentives

Autobandi: AI agent for public incentive matching

Give the matching desk your company tax ID and a little context. It reads official company data from the business registry, compares it with the admission criteria of every open incentive in the national incentivi.gov.it catalog (open-data feed, ingested daily), and returns a shortlist with deadlines, official links and the reason for each exclusion. You decide what to pursue.

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Product view

The problem

Open incentives are public. Finding the ones that fit is not.

Public portals list hundreds of programmes. Eligibility depends on geography, size, sector codes, dates, and typology. Spreadsheets and generic chat tools either miss gates or invent fit. The source is the national incentivi.gov.it open-data catalog; the process pattern is what we take on elsewhere.

Before the agent

Too many open programmes to scan by hand
Hard eligibility gates buried in metadata
Generic AI that invents certainty
No clear rationale to hand a client
What the agent does

What Autobandi handles.

Start from a company ID

Conversational intake with optional clarifying questions (geography, size, sector) before matching.

Enrich, then match

Registry and web context when keys allow, then every open programme in the national incentivi.gov.it catalog, kept current by daily ingest.

Three filters, one shortlist

Deterministic qualify, intent/vector shortlist, then LLM reasoning with fitness and rationale.

You keep the judgment

Ranked matches with reasons; not autopilot applications.

Agent loop

The matching desk streams steps so you see clarify, enrich, qualify, shortlist, and reason.

Hard gates, not soft guesses

Built around tax ID, region, size, and sector codes; not a generic grant chatbot.

Where people stay in the loop

Matching runs in the agent. You review the shortlist.

The agent does the eligibility grind. You decide which programmes are worth pursuing.

  1. Company ID inTax ID plus optional open-text context
  2. PersonClarifyAgent asks only what it still needs
  3. EnrichRegistry / web context when keys allow
  4. MatchDeterministic, vector, then reason
  5. PersonYou reviewShortlist with rationale
Agent step A person decides
How it's managed

Watched, improved, accountable.

For Autobandi we tune gates, corpus coverage and reasoning filters so the shortlist stays explainable as programmes change.

Source labelledResults come from the national incentivi.gov.it catalog (open-data feed, ingested daily), with deadline and official link on every row.
Honest soft-skipsIf a registry or LLM key is missing, the loop continues with clear stubs; no invented certainty.
EU-minded processingAnalytics and models follow the same consent and EU host patterns as the other HMD agents.

Autobandi stack

Matching desk with an agent loop over the national incentivi.gov.it programme catalog.

  • Next.js
  • Supabase

Related process for your business

Eligibility work that eats consultant hours?

Same pattern wherever public programmes have hard gates and soft fit.

  • Regional calls
  • Innovation vouchers
  • Training funds
  • Green transition schemes
Talk about Autobandi

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