01
Cost in the right order
Fast programmatic checks reject weak matches before LLM analysis. AI is only used where it adds value.
ghstops.com
1,300+ targeted prospects — from idea to a working prospecting engine in three days
B2B prospecting can consume days of manual work: companies are found one by one, details are copied into spreadsheets, and list quality is only discovered after outreach begins. Ghstops needed a repeatable way to find suitable Finnish businesses, enrich their data, and measure how outreach progressed.
Snaips built a profile-driven prospecting system for ghstops.com, combining targeting, company discovery, website analysis, quality assessment, background processing, and results tracking in one workflow. The first end-to-end foundation was built and tested over three calendar days, 17–19 March 2026. Outreach and CRM capabilities continued to evolve afterwards.
1 300+
prospects discovered
453
businesses contacted
49.2 %
email open rate
20.8 %
landing-page view rate
Manual approach
Searches, spreadsheets, and hard-to-evaluate quality.
- Finding companies and copying details by hand
- Duplicate and unreachable businesses in the list
- Paid AI analysis spent on weak matches
- Little visibility into which target segment performs
The Snaips approach
A repeatable process from discovery to measurable outreach.
- Reusable profiles by industry and country
- Automated filtering, enrichment, and deduplication
- Low-cost rule-based scoring before AI
- Tracking for opens, page visits, and opt-outs
Results as of 18 August 2026: More than 1,300 prospects were found through 16 Finnish industry profiles. Outreach to 453 businesses produced 223 email opens and 94 visits to a personalized landing page. The system recorded 27 opt-outs, or 6.0%. These figures describe this implementation's measured snapshot, not a general performance guarantee.
.NET 9
Blazor
EF Core
SQL Server
Google Places API
Playwright
Background workers
LLM qualification
Workflow
How the prospecting engine works
Five stages turn a target segment into a measurable prospect list.
- 1
Profile-based targeting
The admin defines industry, country, language, and search terms as a reusable profile.
- 2
Geographic discovery
Google Places search uses country bounds and pagination to stay within the selected market.
- 3
Filtering and deduplication
The system skips closed businesses, entries without websites, and directory-style URLs, then detects duplicates by Place ID, website, and company-city pair.
- 4
Website enrichment
Playwright renders the site. Content is distilled, contact and social details are extracted, and the match is scored with rules before paid AI analysis.
- 5
From background work to results
A queued windows worker runs jobs reliably, promising matches are qualified, and outreach opens, page visits, and opt-outs are recorded.
Core design
More than a scraping script
The value comes from making the entire process work as one manageable system.
02
Reliable background processing
A database queue, work leases, duplicate-run protection, and clear success and failure states make the long-running process observable.
03
Responsible follow-up
Each prospect carries a retention-expiry date, and opt-outs suppress later automated outreach. Results remain visible by target segment in the same admin view.
The bottom line.
For this implementation, Snaips did not deliver a standalone search script. The same technology partner designed the targeting, data model, integrations, quality gates, background processing, admin interface, and metrics. The first working foundation was delivered in three calendar days and later became the base for a broader outreach and CRM workflow.
Does your business need a similar tool built around its own process?