Justin Press

The emphasis on removing friction shows. What was the hardest feature you chose not to include once early feedback came in?

Framing AI search as a distinct surface makes sense. How are customers using your insights differently than classic rank tracking when deciding what to prioritize next?

Love the focus on actionability versus theoretical TAMs. Curious how you think about overcounting when firmographic filters look right on paper but buying intent or budget reality lags behind the dataset.

If a rejected candidate challenges a decision, what level of auditability does RecruitEasy preserve around why the model surfaced or filtered them?

IVR fatigue is real. Interested about how you’re balancing automation speed with escalation so customers still feel heard when the issue falls outside a scripted path.

The refresh-fatigue insight resonates. How do you decide which filters matter most early on without overwhelming users who just want signal fast?

Hallucination-free is table stakes once AI touches financial reporting, interested in how you enforce constraints when data inputs change mid-cycle.

Abstracting data access into typed tools instead of raw SQL is a smart constraint. How do you handle permissions and environment separation when the same agent operates across staging and prod?

Interesting framing around end-to-end knowledge work. Curious how you’re thinking about traceability as insights move from raw inputs to polished exports, especially when summaries are reused or shared externally.