The problem this solves
Reps research every account manually, fifteen minutes per prospect for data that should arrive automatically. Meanwhile the signals that indicate timing, target accounts visiting the site, champions changing jobs, a stack that matches your ICP, either are not captured at all or land in a report nobody actions. The pipeline runs on effort where it should run on signal.
How we work
We build the enrichment layer first: sources chosen for your actual market, Breeze Intelligence and Apollo on the commercial side, Polish public registries like GUS and KRS where they simply beat commercial databases for local company data, arranged in a waterfall so each record gets filled by the cheapest source that covers it. Enrichment runs on creation and on refresh cycles, engineered in Make, n8n, or custom Node and Python depending on volume.
Then activation, which is where most enrichment projects die: every signal gets a definition, a threshold, and a wired consequence. An ICP-fit company hitting the pricing page creates a task for the right owner with context attached. A champion job change triggers a play, not a note. Scoring consumes the enriched fields so prioritization reflects data, not tenure.
Everything lands in HubSpot as properties, tasks, and lists your team already works from, no new dashboard to ignore.
Deliverables
- Source strategy and waterfall design for your market
- Enrichment pipelines with creation triggers and refresh cycles
- Signal definitions with thresholds and owners
- Activation wiring: tasks, alerts, plays, and scoring inputs
- Coverage and fill-rate report after the initial pass
- Pipeline monitoring and a maintenance runbook
What buyers ask before scoping.
Which enrichment sources work for the Polish and EU market?
A mix, honestly weighted: commercial providers have thinner coverage here than in the US, which is why we test fill rates on a sample of your records before committing spend, and why Polish public registries such as GUS and KRS are part of the waterfall, for local company data they are often the most accurate source available.
What separates signal activation from just having intent data?
A wired consequence. Intent data in a dashboard is trivia; a signal is only real when it creates a task, an alert, or a play for a named owner within a defined time. We design every signal as trigger-condition-action-owner, and we retire signals nobody actions rather than letting them decay into noise.
How do we measure whether this system pays for itself?
Three honest numbers: research time per account before and after, fill rate on the fields sales actually uses, and what happens to signal-sourced tasks, worked or ignored, and with what conversion against the baseline. We set the measurement up as part of the build, because an enrichment bill without these numbers is just a subscription.
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