45% faster cycles. $1.2M in caught mistakes. In 90 days.
Eight product squads, a 40-person sales org, three program managers, a 12-person marketing team. On paper one company. In practice four operating systems — and the seams between them were where everything broke.
What we found in the first two weeks
We don’t start with tools. We start with the Toil Audit. This time we ran it differently — instead of mapping where AI tools were sprawling, we mapped where context disappears between functions.
Product was running on stale specs
PRDs went stale within three weeks of being written. Decisions lived in Slack threads with 14 messages of context, zero written rationale, and no link back to the PRD they shaped. New PMs took 90+ days to ship their first owned PRD because tribal knowledge wasn’t anywhere they could find it.
Sales was selling features that didn’t exist
AEs were making customer commitments in Gong calls and Salesforce notes that never flowed back to Product. The pitch deck was six months out of date. The same objection was answered eight different ways by eight different reps.
Program Management was a status-collection job
Three program managers were spending 60% of their week chasing updates across Linear, Aha!, Slack, Asana, Google Docs, and Notion. Launch readiness reviews ran on screenshots and DMs. By the time the deck was ready, half the data was stale.
Marketing was promising more than Product was shipping
PMM wrote messaging based on its interpretation of Slack threads. Campaign assets shipped before features were finalized. One landing page promised three capabilities. The feature shipped with one of them.
Six different “sources of truth.” None of them true at the same time.
The 90-day rebuild
Days 1–30 · Stand up one shared context layer
Team OS repo. A context/ tree covering customers, decisions, PRDs, commitments, launches, messaging, glossary, and stakeholders. Versioned in Git. Edited and queried through Claude Code. MCP connectors wired in: Aha!, Linear, Slack (read), Gong, Salesforce, Figma, Snowflake, Google Drive, Notion. Read once. Available everywhere.
First cross-functional agent live by week three: the Commitment Ledger. It watches Gong calls, Salesforce opportunity notes, and flagged Slack channels for customer-facing commitments, drafts a structured record, and routes to the relevant PM for acknowledgment. The $1.2M-churn failure mode, eliminated as a class.
Days 31–60 · Rebuild the workflows that cross function boundaries
Each function got its own hero agents — wins compound because they share the same context layer.
Product: Insight Synthesizer (Gong + Intercom themes → existing PRDs), Decision Logger (90-second decision capture anchored to PRDs), Spec Drift Watcher (daily PRD-vs-tickets diff), Auto-Debrief (pre-populated retros).
Sales: Commitment Ledger, Live Pitch Deck (regenerates weekly), Objection Answer Engine (canonical answers per objection), Deal Risk Surfacer.
Program Management: Launch Readiness Agent (live single view from Aha! + Linear + Figma + tickets), Cross-Squad Dependency Watcher, Stakeholder Brief Generator, Risk Register Live View.
Marketing: Messaging Drift Detector, Launch Asset Generator, Customer Story Surfacer, Roadmap-to-Campaign Linker.
Days 61–90 · Make it run without us
Staleness alerts. Gap detection. Usage tracking on the context files. Every agent has a human-in-the-loop approval gate. Cohort training, role-specific — each function rebuilt a real artifact through their agents in the room. By day 90 every function had two or three power users running the system after we left.
The repo is the deliverable. Every artifact, prompt, agent, and playbook lives in the Team OS repo. The team owns it. We leave. The system stays.
What came out the other side
Numbers from the engagement, measured at day 90 and verified at day 180.
The executive QBR went from four function-specific status decks to a single one-page memo, generated from the Team OS repo, with cycle time, commitment coverage, launch readiness, and the dollar value of mistakes the system caught before they shipped.
Illustrative composite based on common patterns we see, not a single named client. Numbers are representative.