Search: Identifying Companies That Match GTM Intent

Lead generation is fundamentally a search problem. But instead of scanning structured datasets, we search the entire internet for companies exhibiting leading indicators aligned with a customer’s GTM strategy.

This requires semantic understanding, not keyword matching.

Signals show up as patterns, context, organizational changes, or strategic moves — all of which need interpretation rather than literal text matching.

Search becomes a meaning-first operation.

Sort: Prioritizing Accounts Using Live Activity

Account prioritization maps directly to a sorting problem.

Most CRMs are bloated with accounts that look identical on paper but behave very differently in the real world. Sorting these accounts based on live external activity determines where attention compounds and where it evaporates.

A dynamic sorting layer turns a static list into a ranked execution path that reflects real-time momentum.

Applying CS Fundamentals to GTM

When Fenil and I were undergrads grinding through algorithms, neither of us imagined we’d spend our careers applying those same principles to go-to-market systems. Yet the parallels are unmistakable.

The problems remain the same - search efficiently and sort intelligently - but the inputs are now messy, unstructured, and constantly evolving.

The fundamentals survived. The domain shifted.

GTM as an Algorithmic System

Search uncovers relevance.
Sort allocates attention.

Together, they define how precisely a GTM engine operates.
Better algorithms lead to better execution.

GTM teams win when search and sort primitives operate on semantic signals and live activity rather than static lists.