Your whole market, mapped into your product.
Not a CSV of leads — your actual data model, populated, so the data is usable inside the product and not just by the sales team.
Marketplaces and vertical SaaS with a supply-side problem: the businesses you want on the platform have to fill in a long profile before they get any value, and most quit halfway. Also B2B teams whose pipeline currently starts with somebody manually googling companies into a spreadsheet.
- Your market in your schema — every business collected and mapped to your product's own fields, not just firmographics but the domain data your product runs on (for a venue: capacity, spaces, amenities, pricing, availability)
- AI gap-filling — where public data is missing, an inference step proposes a value that fits that specific business, so profiles are complete rather than half-empty
- Suggestions, never silent writes — everything inferred is surfaced as an editable suggestion someone confirms; nothing unverified is published as fact
- Confidence scoring and provenance — you can see what was scraped, what was inferred, and how sure the system is about each field
- Sales-ready contacts from the same dataset, delivered into your CRM (HubSpot, Pipedrive, whatever you run)
- Optional onboarding pre-fill — because the data sits in your schema, it can populate your signup flow so new customers confirm rather than type
- Optional scheduled refresh, because data decays
- 01
Schema session
I read your data model and onboarding flow, and we agree which fields are worth populating.
- 02
Pilot slice
A few hundred businesses first, so you can judge quality before committing to the full market.
- 03
Full build
Collection, extraction, mapping, inference, validation.
- 04
Wiring in
Contacts delivered to your CRM, optional pre-fill surfaced in onboarding, pipeline documented for your team.
How VenuePlace got its supply side
VenuePlace (venueplace.com) is a venue marketplace covering Estonia, and no data vendor maintains a usable list of Estonian event venues. So I built the dataset myself: a crawler that collected 8,000+ venues from listing sites, maps and the venues' own websites, then an extraction step that pulled every page into the fields VenuePlace actually runs on — spaces, capacities, amenities, pricing, availability.
The raw data was then refined rather than dumped: names and addresses deduplicated and normalised, categories mapped to the product taxonomy, and every venue enriched with the extra fields the marketplace needs, contact person included. Where public information was missing, an AI inference step proposed a value that fits that specific venue, surfaced as a suggestion with a confidence score and its source, never written as fact.
Because everything was mapped to VenuePlace's own schema from the start, a finished record could be published straight onto the marketplace as a venue page — no re-keying, no import spreadsheet. The same records feed the sales pipeline in Pipedrive today, every venue with its contact person, and are what the venue onboarding pre-fill is being built on, so a new owner confirms their details rather than types them.
A 30-minute call to see whether this is the right shape for your project — no deck, no pitch.
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