Sales Technology_Parallaxis Growth PlatformLead research automation

Building an evidence-first prospect research pipeline_

Parallaxis assembled a research pipeline using metasearch, focused crawling, content extraction, entity resolution, and structured evidence storage before contact details are promoted to outreach. The system produces sma

B2B prospect research
Metasearch and targeted crawling
Content extraction

Challenge_

Generic lead lists supplied domains and titles but little evidence that a company had the operational problem Parallaxis could solve. Manual research produced better context but did not scale.

Approach_

Parallaxis assembled a research pipeline using metasearch, focused crawling, content extraction, entity resolution, and structured evidence storage before contact details are promoted to outreach.

Outcome_

The system produces smaller, explainable prospect sets with a reason to contact each company instead of treating every scraped address as a lead.

Overview

Generic lead lists supplied domains and titles but little evidence that a company had the operational problem Parallaxis could solve. Manual research produced better context but did not scale.

Parallaxis assembled a research pipeline using metasearch, focused crawling, content extraction, entity resolution, and structured evidence storage before contact details are promoted to outreach.

The engineering decision

Research and outreach are separate stages. The pipeline stores source URLs and evidence, deduplicates companies, scores problem signals, and requires human review before messaging.

How the system works

The implementation separates intake, validation, state changes, side effects, and reporting. That separation makes failures visible and allows one layer to change without rewriting the entire workflow.

Operational users see explicit statuses and exceptions; technical teams retain identifiers, timestamps, versions, and logs needed to reproduce a result.

Outcome