Give Marketing Teams AI That Carries Campaign Context From Brief to Launch
Marketing teams lose weeks of AI-assisted work every time a session closes. Brand voice guidelines, audience research, campaign performance context, competitive positioning — none of it follows your team from one AI conversation to the next. MemoryLake gives marketing and growth teams persistent shared memory across ChatGPT, Claude, Gemini, and every other model in your stack, so the AI that helped you build the brief last month already knows the brand when you return for copy. The result is consistent output without the constant re-briefing.
Give Marketing Teams AI That Carries Campaign Context From Brief to Launch
Get Started FreeFree forever · No credit card required
The Memory Problem
A growth marketer spends a full session getting their AI calibrated: target persona, tone guardrails, what the last three campaigns tested, what didn't convert. They close the tab and that context is gone. The next session — or the next team member — starts over. Brand voice drift creeps in. Campaign learnings don't compound. The AI that helped you write last quarter's highest-performing email has no memory of it.
What MemoryLake Does Differently
Brand Voice That Holds Across Every Session
Skill Memory stores your brand voice framework, tone guardrails, and messaging hierarchy as reusable workflows. Every AI session starts with the same foundation, regardless of which model or team member is working.
Campaign History That Compounds
Conversation Memory makes every AI-assisted campaign session permanently searchable. Retrieve what you tested in Q3, why you pivoted the positioning, and what the data showed — without hunting through Slack threads or shared docs.
Shared Team Memory Without Silos
One marketer's audience research becomes the whole team's context. Role-based access control keeps strategic roadmap data gated while making brand and campaign context universally available across the team.
Free forever · No credit card required
How It Works
- Connect — Link your marketing team's AI tools via MCP or REST API, and connect Google Workspace or Office 365 so campaign briefs, audience docs, and research files are part of the memory layer.
- Structure — Brand voice and content frameworks go into Skill Memory. Campaign history sessions go into Conversation Memory. Competitive intelligence and audience facts go into Fact Memory with version tracking.
- Reuse — When a copywriter opens a new AI session for a campaign, they get instant access to the brand framework, prior campaign learnings, and audience research — already structured and retrievable in milliseconds.
Before & After
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Starting a new campaign brief | Re-explain brand voice, audience, and prior learnings every session | AI opens with full brand context, audience profiles, and campaign history |
| Cross-team handoffs | Each marketer has their own AI context; no shared foundation | Shared team memory means any marketer picks up where the last left off |
| Brand voice consistency | Drifts across sessions and team members | Skill Memory enforces consistent tone and messaging guardrails |
| Campaign learning retention | Lives in one person's AI chat history, then disappears | Permanently searchable Conversation Memory across the whole team |
Built For
MemoryLake is built for marketing teams, growth teams, and brand strategists who run AI-assisted workflows at volume and lose context every time a session ends or a team member transitions onto a project. It's especially useful for teams managing multiple campaigns simultaneously, agencies running creative for several clients, and growth teams doing rapid test-and-learn cycles where prior experiment data needs to be accessible in every future session.
Try this use case on your own account
A growth team saves its monthly review decks — native PowerPoint charts, a data label on every bar — into shared memory. The chart's numbers are stored in the .pptx itself, but the index treats the chart as a picture: a figure with empty text and a vision summary that names the extremes (and once in a while the wrong metric). That summary is all MemoryRouter attaches. Numbers typed into the speaker notes are not indexed at all. Read the chart data out of the deck into a CSV and the same questions come back right.
What you will see
- Two review decks (20 chart values) kept three ways, one project and one boundary each: as saved, with every number also in the speaker notes, and as saved plus each chart's own data read out of the .pptx by a standard-library function in the demo; the marketing assistant is
router chat --boundary(gpt-4o-mini, temperature 0) search --types documentshows what the index kept: the chart's figure text was empty in every deck import, the summary held 4 of 20 values and described the conversion-rate chart as “website visits”; the speaker notes were nowhere in the index- Six questions, 3 answers each, over 6 runs: decks as saved 18 of 108 right, numbers in the notes 18 of 108 (the only right answers: the top three the summary happens to name), decks + chart data 92 of 108
- A checker call (Claude copies out what MemoryRouter attached) shows the notes were never attached and separates the CSV boundary's misses as “attached and still wrong”. Plus a
decksubcommand and a local web UI
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/deck-chart-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
Related use cases
Frequently asked questions
We already have a brand style guide in a shared Google Doc. Why isn't that enough?
We already have a brand style guide in a shared Google Doc. Why isn't that enough?
A style guide your team pastes into a prompt every session is better than nothing, but it doesn't capture the institutional knowledge that accumulates over time — why certain decisions were made, what was tested and failed, how the audience responded to specific framings. MemoryLake stores that evolving context so your AI gets smarter about your brand with every session, not just as literate as the last doc you updated.
How does shared memory work when different marketers have different AI tool preferences?
How does shared memory work when different marketers have different AI tool preferences?
MemoryLake sits beneath the model layer. Whether one marketer uses Claude and another uses ChatGPT, they both read from and write to the same shared memory store. The memory is model-agnostic, so a brand framework stored from a Claude session is retrievable in a ChatGPT session without any manual transfer.
Will this create conflicts if multiple people update campaign context at the same time?
Will this create conflicts if multiple people update campaign context at the same time?
Fact Memory in MemoryLake includes built-in conflict detection. If two team members log contradictory data about an audience segment or campaign outcome, MemoryLake flags the discrepancy rather than silently overwriting it. Git-like versioning means you can see who updated what and when, and roll back if needed.