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B2B SaaS
2025

Leadscraper

GDPR-compliant B2B lead generation for the DACH market. AI agents source and qualify leads in real time.

leadscraper.de — website in browser frame
High CPL
Low Lead Quality
Inconsistent Conversion
High CPL
Low Lead Quality
Inconsistent Conversion
High CPL
Low Lead Quality
Inconsistent Conversion
High CPL
Low Lead Quality
Inconsistent Conversion
High CPL
Low Lead Quality
Inconsistent Conversion
High CPL
Low Lead Quality
Inconsistent Conversion
High CPL
Low Lead Quality
High CPL
Low Lead Quality
Inconsistent Conversion
High CPL

Fact sheet

Status
Active
Started
2025
TARGET MARKET
B2B sales teams and founders in Germany, Austria, and Switzerland
Business model
B2B SaaS — monthly subscription
Users / reach
Sales teams and founders in DACH
Stack
AI Agents, Custom Data Pipeline, Webflow, Stripe
Problem

B2B lead data is stale.

Most lead-gen tools in DACH scrape outdated databases, treat GDPR as an afterthought, and dump generic contact lists on sales teams. Reps end up burning hours qualifying leads that were never qualified to begin with — and accountability for data quality sits nowhere.

Approach

AI agents that source leads in real time.

Leadscraper sources, enriches and qualifies leads on demand. Every search runs against the public web at the moment of the query rather than against a stored database, GDPR compliance is built into the pipeline, and pricing per credit means cost follows actual usage. Sales teams get a shorter list of leads that are actually worth contacting.

Where it came from

Leadscraper exists because of a frustration I had as a buyer. Every B2B lead tool I tried in the DACH market sold me a database: someone else's contacts, of unknown age, with a GDPR story that got vaguer the more you asked. Sales teams then spent their days qualifying leads that were never qualified to begin with. The idea was to flip the order. Don't store the market and sell slices of it. Search the public web at the moment someone asks, and hand back a small list where every single data point has a source you can click.

How it runs

The core is a set of AI agents that take a query, work through public sources in real time, and assemble companies and decision makers with provenance attached. That last word matters more than anything else in the product. We don't buy identities from third-party people databases, the Apollo model, because it is exactly what we position against. Third parties appear in one narrow role only: verifying that an email address we derived ourselves actually delivers, under a data processing agreement that's listed publicly. If we can't prove a data point on a public source, we don't deliver it. That's a code invariant, not a policy document.

Around the core there's a credit-based subscription for self-service teams, a help centre that answers most support questions before they're asked, and a managed service for companies that want us to take responsibility for the whole market approach: which segment, which companies, which decision maker, what signal, what message, which channel. Every agent run is traced end to end, so when a customer asks why a result showed up, we can answer instead of guessing.

What I learned

Two things that cost real money. First, data quality is a customer-satisfaction problem before it's a revenue problem, and I've had to keep framing it that way internally when the revenue framing was more tempting. Second, the GDPR position is only a strength if the legal documentation says exactly what the marketing says. We audited our own processor list this year and found gaps between the two. Closing them was unglamorous work and it's the work that makes the positioning true.

Status

Active and the primary revenue product of the company. Engineering capacity is bought per scope rather than employed, which is how every product here is built. Details on pricing and plans live on the product site, not here.