Within

Within launched with a deliberately minimal MVP: a web-based screen-recording tool that captured employee workflows and surfaced task patterns through anonymized "Signals." They shipped this core loop within months, deliberately excluding sophisticated AI agent recommendations, complex integrations, and enterprise permission systems that competitors prioritized. This constraint forced them to validate the fundamental insight—that companies needed visibility into actual work patterns before optimizing them—before building expensive infrastructure around it. The no-installation requirement proved critical. Early customers at OpenAI and DoorDash could onboard teams in days rather than weeks, generating immediate workflow data that validated the mapping thesis. By staying laser-focused on observation rather than prescription, Within gathered signal-rich datasets that later powered their agent-allocation features. This sequencing—visibility first, optimization second—attracted enterprise customers skeptical of AI transformation claims, since Within demonstrated understanding before promising transformation.