Give Agencies Client Context Memory That Works Across Every AI Tool
Agencies juggle dozens of clients, each with their own brand voice, history, and preferences. AI tools forget which client they're working for the moment a session ends. MemoryLake gives agencies a client context memory layer — so every AI tool, every account manager, and every new hire walks into the same context the client expects.
Give Agencies Client Context Memory That Works Across Every AI Tool
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The problem: agencies re-brief AI on every client every time
The brand voice you trained the AI on yesterday for Client A is gone today. Switching from Client B's deck to Client C's copy means re-pasting the brand guide, voice samples, and approved phrases. Account managers burn hours each week being human context-loaders.
How MemoryLake solves client context memory for agencies
One memory namespace per client
Brand guidelines, approved language, past campaigns, and key contacts live in a scoped memory.
Skill memory for client deliverables
"How we write a launch email for Client X." Define once. Every AI tool follows.
Cross-tool consistency
The brand voice the AI uses in ChatGPT for copy matches what Claude uses for strategy docs.
Provenance for every approved asset
Every piece of memory links back to its source: a signed-off brief, an approved campaign, a brand audit.
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How it works for agencies
- Connect — Import client briefs, brand guides, approved campaigns, and key stakeholder profiles.
- Structure — MemoryLake organizes each into background, facts, events, and skill memory.
- Reuse — Any AI tool retrieves the relevant client memory for the task at hand.
Before vs. after: agency client context
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| AM switches between clients | 20 min re-loading context | Memory loads instantly |
| New AI tool added to stack | Re-train on every client | Memory works across tools |
| Onboarding a new account manager | Weeks of immersion | Day-one access to client memory |
| Off-brand AI output | Common | Blocked by brand skill memory |
Who this is for
Marketing, creative, and digital agencies running AI workflows across 10+ active clients — where switching context is constant and brand consistency directly affects retention.
Try this use case on your own account
An agency keeps one brief per client in shared memory, and its account assistant answers from them through MemoryRouter: "what is the approved tagline for Brightwater Foods?" is right every time. But the account managers say "Project Kestrel", an internal codename that appears in no brief. Asked by codename, MemoryRouter attaches another client's brief or nothing, and the model answers with that client's tagline or an invented one, under the name it was asked about. Putting the agency's alias registry in the system prompt tells the model who Kestrel is, but retrieval still runs on "Kestrel", so the right brief never arrives. Rewriting the alias to the client's name in the app before the call fixes retrieval; showing an answer only when something was attached removes the last invented replies.
What you will see
- Six client briefs imported into MemoryLake and the alias registry stored as six facts with
fact add;fact list --query "Project Kestrel"returns the one fact naming Brightwater Foods, whilesearchwith the question as typed left Brightwater's brief out of its top 3 in 13 of 14 lookups - A checker (
router messages) shows what MemoryRouter attaches for the two codenames: the right client's brief in 0 of 28 copies, another client's in 15, nothing in 13 - Six alias questions, 3 answers each through
router chat --boundary(gpt-4o-mini), over 7 runs: as typed 59 of 126 right (codenames 0 of 42), with the registry in the system prompt 56 of 126 (codenames 0 of 42), alias rewritten by the app 116 of 126, rewritten and shown only when prompt tokens exceed the no-boundary baseline 125 of 126 (the last one held back); every invented answer had nothing attached - Plus
aliasandbriefssubcommands and a local web UI with the registry lookup next to what search returns
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/client-alias-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Can two account managers share the same client memory?
Can two account managers share the same client memory?
Yes. Each client memory has fine-grained access control. Multiple AMs can read; specific roles can write.
How do I keep client data isolated?
How do I keep client data isolated?
Per-client memory namespaces enforce isolation. End-to-end AES-256 encryption protects every namespace.
What happens when a client offboards?
What happens when a client offboards?
One-click full export of their memory; one-click permanent deletion with no backup retention.