SignalLeak

A useful buying signal may be buried in a job posting, a company announcement, or an operational change. Monitoring each source manually takes time, and the same event can appear in several places. Collecting more information is only useful if someone can understand why it matters.

The interesting challenge is the whole path from information to action. SignalLeak gives me a way to work across problem discovery, data architecture, SaaS UX, and technical implementation within one connected product.

The product scope connects multi-source discovery, normalization, relevance filtering, company identification, and deduplication. Opportunities can then be organized for review, with relevant contacts and downstream outreach preparation considered as separate steps.

Collection brings in public information. Normalization gives discoveries a consistent structure. Relevance filtering narrows the candidate set, while company identification and deduplication establish what the opportunity refers to and whether it is already known. Provider diagnostics make the behavior of those sources visible.

A discovery is not automatically an opportunity. The experience needs to distinguish collected material from relevant signals and make the reasoning legible. Contact discovery and outreach preparation belong downstream, where there is enough context to decide what happens next.

Different providers can return incomplete, repeated, or noisy information. The work involves data workflows, diagnostic visibility, AI-assisted analysis, and iterative filtering. The interface has to communicate those limits without making the user inspect the entire pipeline.

Actively building and refining the discovery-to-opportunity workflow. This case study describes the product direction and work in progress; it does not claim customer traction or business outcomes.