Pipesignal: Agentic AI for B2B Project Discovery

A product exploration of earlier industrial project discovery. I defined the commercial problem and built a concept around public project signals, structured enrichment and prioritisation. The demonstration shows a proposed workflow; it does not prove earlier discovery across markets or realised commercial value.

Evidence and scope

Founder-built product concept

Product strategy, concept and build

This is a product concept, not an independently validated client result. Earlier discovery, continuous monitoring, data accuracy and commercial conversion remain hypotheses to test; no pipeline or revenue figure is claimed.

Outcome and evidence

A product exploration of earlier industrial project discovery. I defined the commercial problem and built a concept around public project signals, structured enrichment and prioritisation. The demonstration shows a proposed workflow; it does not prove earlier discovery across markets or realised commercial value.

My role

I owned the product brief, commercial concept and prototype build. This is personal product work, distinct from an enterprise leadership mandate or a delivered client programme.

The problem

Industrial sellers can encounter a project only after specifications are settled. The concept asks whether public permits, investment announcements and other signals can support an earlier, better-informed research workflow.

The approach

  • Find public project signals and retain source references.
  • Organise project stage, geography and relevant stakeholders for review.
  • Use agent-assisted enrichment to propose priorities for a commercial user.

Constraints and governance

Public signals may be incomplete, stale or misleading. Project value and timing require source verification; contact research requires appropriate permissions. A working demonstration does not establish continuous production operation or dependable data quality.

Outcome and evidence

The result is a product concept and recorded demonstration. The next validation question is whether reviewed signals help a real commercial team identify relevant projects with acceptable accuracy and effort. No discovery-time improvement, pipeline gain or revenue result is asserted.

Outcome and evidence

  • Discovery objective: Pre-tender signals
  • Design pattern: Agent-assisted research
  • Evidence status: Personal concept / prototype

Sources

Canonical URL: https://juanbeltran.ch/portfolio/pipesignal-agentic-ai-for-autonomous-b2b-project-discovery