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 strategic value an agency captures when its agent memory layer exports cleanly, measured as the gap between what client context is worth inside a tool and what survives leaving it. Agencies accumulate client-specific architecture, brand rules, and debugging history across long-running retainers, and that accumulated context is the real deliverable asset. When the memory layer is a closed silo, switching tools means rebuilding months of context from scratch, so the premium collapses to zero and the vendor captures the rent. Knownbase stores decisions, debugging discoveries, and constraints as project notes retrievable across sessions and tools, which keeps the archive portable. Cogni runs retrieval through an entity graph with spreading activation and no LLM in the path, so connected facts stay model-agnostic. Bourdon federates one memory across Claude, Codex, Cursor, and Copilot, so a fact learned in one tool is recognized in the others. Before standardizing, test one export: pull a client's memory out and read it without the original tool.

Recall Latency Tax is the hidden cost of how long an agent takes to surface a fact it already learned. Every re-prompt, re-explanation, and context paste is a tax paid in senior hours, and it compounds across a retainer because the same client architecture gets re-taught every session. The framework separates two retrieval paths: recognition-first federation, where a fact learned by one agent is instantly available to others, versus search-based lookup, where the agent must be told what to look for. Bourdon's recognition-first design pushes recall latency toward zero across Claude, Codex, Cursor, Copilot, and Devin, while Knownbase organizes notes by project and tag so retrieval depends on correct querying. For an agency running five client accounts through three tools each, the difference shows up as fewer re-briefs per deliverable and more margin per retainer hour. Audit recall latency before you audit model quality.

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 on any agent memory layer, export a full client knowledge set, re-import it into a second tool, and confirm the entity relationships survive the round trip.

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.

The architecture choice matters more than the vendor shortlist: graph-based recall (Cogni) compounds cross-client reasoning, archive-based memory (Knownbase) compounds project audit trails, and federated recognition (Bourdon) compounds speed across a multi-tool stack. Pick the one whose memory shape matches how your agency actually reuses knowledge, because a memory layer that stores the wrong shape of context just makes re-prompting faster. Whichever you choose, test the export path before you commit a retainer's worth of institutional knowledge to it.

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.