Knownbase

Knownbase's $5 Solo plan converts repetitive context-setting into a per-project retainer, letting agencies charge clients $5-15/month for persistent AI memory. For dev shops with 3+ concurrent codebases, this eliminates the cost of re-explaining architectural decisions, turning a $2.50 Go tier expense into a recurring margin stream.

The Knownbase Context Persistence Curve maps the relationship between the depth of project memory stored in Knownbase and the value an agency can extract from AI-assisted development. At the low end, agencies with fewer than two concurrent client codebases see minimal benefit, as context loss is infrequent and the $5/month Solo plan may suffice. As agencies scale to 3+ simultaneous projects, the cost of re-explaining architectural decisions and debugging discoveries to Claude Code or Cursor grows exponentially. By adopting Knownbase's Go plan at $2.50/month per project, agencies can store up to 3,000 notes per project, ensuring agents retrieve relevant constraints on demand. This persistence reduces billable hours spent on context-setting, allowing agencies to offer fixed-fee retainers with higher margins. For example, a dev shop managing five client codebases can resell Knownbase as a $15/month per-project add-on, turning a $12.50 cost into a profitable service while improving delivery consistency.

Memory Portability Premium is the principle that an agent memory layer is worth more to an agency when its contents can be exported, re-indexed, and replayed somewhere else. Persistent memory compounds: every client architecture decision, brand rule, and debugging discovery an agent stores makes the next session cheaper to run. But that compounding only belongs to the agency if the store is portable. A memory layer that holds context in a proprietary index converts institutional knowledge into a hostage; switching tools means re-teaching every agent what the last one already knew. The premium is the delta between a memory layer you can walk away from and one you cannot. Knownbase organizes notes by project and tag, which makes bulk export tractable, while Bourdon federates one memory across Claude, Codex, and Cursor accounts, so the same fact survives a tool swap. Audit export format before you audit recall quality.

Recall Latency Budget treats the time an agent spends re-establishing context as a line item you can measure and cap, not an invisible tax. Every session that starts cold forces the agent to re-read brand guidelines, re-derive architectural decisions, or re-ask a client's tone rules, and each of those steps burns tokens, wall-clock time, and human review hours. The budget is the acceptable ceiling on that re-establishment cost per workflow. Recognition-first systems such as Bourdon push recall toward near zero by federating memory across Claude, Codex, and Cursor, while retrieval-based layers such as Knownbase and Cogni trade a small lookup step for structured, queryable archives. For agencies running retainers, the budget compounds: a 20-minute re-brief across 40 monthly agent runs is roughly 13 hours of billable capacity. Set the ceiling before you scale agent count, because a memory layer that adds latency per hop quietly erodes the margin you automated to protect.

Adopt Knownbase only when your agency juggles at least three concurrent client codebases with active AI agent usage, and you can package it as a per-project retainer.

Before standardizing any client account on one memory layer, export the full store to a portable format and re-import it into a second tool, and only then make it the system of record.

IF your agency runs 3+ concurrent client codebases with Claude Code, Cursor, or Codex agents, THEN Knownbase's Go plan at $2.50/month for 10 projects and 3k notes is a low-cost fix for context loss. IF you manage fewer than two simultaneous projects or your team rarely uses AI coding agents, THEN skip it, as the Free tier's 3-project limit and 300 notes cap won't justify the setup overhead.

These three solve different halves of the same agency problem: Bourdon optimizes for instant recognition across a multi-tool stack, Cogni for reasoning over linked client facts, and Knownbase for a durable per-project record. The decision hinges less on feature lists than on whether your delivery model needs speed of recall, depth of inference, or an auditable archive, and on how cleanly each layer exports if you switch. Whichever you pick, test the export path before you migrate a single client account, because a memory layer that traps institutional knowledge converts a tooling decision into a retention risk.

A structured onboarding playbook that configures Knownbase for a client's AI coding agents, establishes note taxonomy, and trains the team to maintain persistent project memory across sessions.