Explore the Future of AI Agents

Teaches an agent to write the expansion documents for an existing customer from its record: a whitespace brief that ranks only what the customer has asked for or repeatedly absorbed, then a mutual action plan or buyer page when asked. Repair comes before expansion on a wounded account, and a thin record gets questions rather than tiers.

Teaches an agent to turn a seller's calendar, open deals and tasks into a one-page morning read: where the book stands, three outbound moves naming the person and the ask, prep for each booked meeting, the deals left alone and why, and the rows in the CRM not to trust.

Teaches an agent to judge every open deal in a CRM export as on-track, stalled or at-risk from evidence rather than stage labels, prescribe the next evidence-producing move for each, and flag pipeline anomalies: coverage by owner, sandbagging candidates, forecast and record-integrity issues, and failure patterns that repeat.

Teaches an agent to cross-examine a forecast before the call: a coverage waterfall with the arithmetic shown, an anomaly table ranked by dollar exposure with one question per row, and a short narrative naming the biggest risk to the number. It refuses to back a quota out of pipeline or to add up mixed currencies without a rate.

Teaches an agent to ingest raw win/loss inputs — CRM loss-reason tags, rep notes, post-mortem interview excerpts, and competitor-mention logs — and produce a structured debrief report.

Teaches an agent to take a structured set of gathered competitor or market signals and produce a framed competitive watch brief for a strategic audience.

Convert Word documents into readable Markdown so legal ops finance and research teams can analyze document content without reformatting by hand.

Convert Excel workbooks into readable Markdown so analysts and operations teams can review spreadsheet content without manual extraction.

Estimate breach exposure sensitive data impact and regulatory consequences so security and leadership teams can prioritize hardening before incidents happen.

Score how ready a repo is for AI-assisted work so platform and enablement teams can prioritize the highest-leverage setup gaps.

Add tool policies approvals and audit trails to AI agents so enterprise teams can ship governed automations with clearer controls.

Create repo-grounded threat models so security, platform, and engineering teams can review trust boundaries and abuse paths with less guesswork.