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See howMaya holds the brand's decisions, preferences, and CAC ceilings across every conversation. Every other agent reads from Maya before acting and write back when something changes.
TikTok tested in August at $72 CAC against a $65 cap. Killed after 3 weeks. Six months later, somebody pitches TikTok again. The budget gets re-burned.
Monday's insight is gone by Thursday. The constraint mentioned last month never makes it into this month's plan. Each conversation starts from scratch.
Account managers turn over. New hires take three months to find the existing decisions. Brand-finance reconciliation history walks out the door with the previous team.
Generic LLMs do not know the CAC ceiling, the channel constraints, or the gradual-change preference. Every prompt re-litigates the basics.
Each capability maps to a specific job: hold the brand history, extract decisions automatically, gate writes through human review, isolate per-account, and serve the rest of the agents.
Conversations, decisions, and outcomes preserved with the context that made them. Searchable across months, not just last week.
Example. Recalled the August TikTok kill within 4 seconds of the new pitch
Decisions, preferences, CAC ceilings, and channel constraints get pulled from conversations and proposed as memory candidates.
Example. Extracted a $65 prospecting CAC ceiling from one Slack thread
No agent writes to memory silently. Proposed updates queue for review, get approved or rejected, and only then become canonical.
Example. 12 candidate writes proposed last week; 9 approved, 3 edited
Memory is scoped to the company. Agencies running multiple brands never see context bleed across accounts.
Example. Two accounts in one workspace, zero shared retrievals
Felix factors in CAC ceilings. Sam runs scenarios within stored constraints. Parker reads the brand-finance reconciliation history. Every agent reads first, writes second.
Example. Sam declined a +40% Meta scenario; Maya held the 20%/week ceiling
One workweek, hour by hour. Specific events vary by account.
Felix asks for the prospecting CAC ceiling before sizing the 8-week forecast. Maya returns $65, last confirmed 11 days ago.
New hire pitches a TikTok test. Maya surfaces the August kill at $72 CAC and the recent Sam scenario showing CAC may now sit at $58.
CFO confirms a Q2 promo window. Maya queues a write proposal: promo dates, expected lift band, owner. Awaiting approval.
Operator reviews the proposal in Slack, edits the lift band from 18 to 14 percent, and approves. Memory commits to the canonical store.
Sam loads the +40% Meta scenario request. Maya returns the 20%/week gradual-change preference; Sam splits the move into two stages.
Parker reconciles platform ROAS against the P&L. Maya stores the 22 percent Google brand-cannibalization figure with source links.
Weekly recap to Slack. 12 candidate writes proposed, 9 approved, 3 edited. 47 retrievals served to other agents.
Maya runs on a retrieval-augmented memory store with human-approval workflow on every write. Reads are streamed to the agents; writes pass through review.
Every agent's activity. Felix's forecast scores, Parker's reconciliation calls, Sam's scenario inputs, Olivia's creative outcomes, Dana's reconciled tables, Dex's delivery logs.
All 6 other agents stream context here
Every agent on demand. Retrieval queries answered in milliseconds with the relevant decisions, constraints, and prior outcomes attached to each fact.
Sub-second retrieval, every agent on demand
Felix, Parker, Sam, Olivia, Dana, Dex, and Max. Reads first, writes second. The connective tissue across the whole team.
Every agent reads from and writes to Maya
Retrieval-augmented memory store with embedding-based search and a human-approval workflow gating every write. Per-brand isolation enforced at the storage layer.
Retrieval-augmented store, human-approval on writes
Ready when you are
The pilot connects your accounts to every agent. Maya starts indexing on day one and grows sharper every cycle.
Looking for something specific? Felix factors in CAC ceilings →, Sam runs scenarios within constraints → or Parker reads reconciliation history →.
