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Every agency retainer carries a hidden tax: the cost of knowledge that was true six months ago and is now wrong. As clients push agencies toward AI-assisted delivery, the quality of that delivery is capped by the accuracy of the internal knowledge feeding it, so documentation decay stops being a housekeeping problem and becomes a margin problem. Agencies that treat knowledge management as a maintenance discipline rather than a one-time migration will price and staff differently than those that do not.

Retrieval Decay Rate is the speed at which a knowledge base stops matching reality once the tools it describes change. Agencies feel this as rework: a new hire follows a two-quarter-old onboarding doc, a strategist quotes a retired rate card, an account lead rebuilds a deliverable that already existed. The framework says the maintenance cadence, not the authoring volume, sets the ceiling on KM value. Slite attacks decay by monitoring connected tools like Slack, GitHub, and Linear and routing suggested fixes to an owner for review, while Guru auto-verifies content as usage rises. The pressure is real: 83% of B2C marketing decision makers already work with AI agents, so clients expect cited, current answers rather than a wiki nobody trusts. Measure decay as the share of pages untouched in 90 days that still get read, then assign an owner per page.

Documentation Half-Life is the interval before a knowledge artifact stops matching reality: a pricing table, a client onboarding checklist, a platform access map. Agencies feel it as onboarding drag, because a new hire or a new account team inherits stale pages and burns billable hours reconciling them. The framework says to measure each artifact's half-life and set a review cadence shorter than it, rather than treating all docs as permanent. Slite's approach of monitoring connected tools like Slack, GitHub, and Linear to flag drift is one way to shorten the interval; Guru's auto-verification as usage rises is another. The strategic point is that a wiki nobody trusts costs more than no wiki, because delivery teams quietly rebuild answers in Slack threads and client retainer hours absorb the rework.

Onboarding Compression Ratio measures how many hours of ramp time a knowledge system removes per new hire, and it is the cleanest way for an agency to price a knowledge platform against a retainer. A 20-person agency hiring six people a year at 40 hours of ramp each spends 240 hours annually on repeated explanation. A centralized base that cuts ramp to 15 hours returns 150 hours, roughly $9,000 to $15,000 of billable capacity at typical blended rates. The ratio only holds when the base is the single source: Slab's Unified Search pulls answers from Slack, Google, and Asana so new hires stop asking account leads where things live, while Tettra's Kai bot answers repetitive questions in Slack channels before they reach a human. Guru takes the governance angle, auto-verifying content as usage rises so the ramp material does not rot between hires. Track the ratio quarterly; when it falls, the cause is usually sprawl, not headcount.

Write down the client context that only exists in people's heads before you buy or migrate any knowledge platform.

Audit content freshness and ownership before adding AI retrieval or expanding the knowledge base.

IF your agency's client context is scattered across Slack threads, Google Docs, and individual account leads' heads, and onboarding a new hire takes more than two weeks, THEN consolidate into one governed knowledge platform with a named owner and a review cadence. IF your team already retrieves answers reliably from the tools they work in daily, THEN keep knowledge federated and spend the budget on retrieval quality instead of migration.

A fixed-scope engagement that migrates a client's scattered documentation into one governed knowledge platform, then wires it into the tools account teams already use. Built for agencies whose delivery quality depends on every strategist finding the same answer.