MemoryLake
Marketing & Growtheditorial memory for publishing teams

Give Publishing Teams Editorial Memory That Holds Across Every Writer

Style guides go stale. Source databases live in spreadsheets. The editor's verbal corrections never make it into the AI tools. MemoryLake gives publishing teams an editorial memory layer — style rules, source authority, prior coverage, and editor decisions — that every writer and every AI tool retrieves on demand.

Day 1“Source databases live in spreadsheets.”✓ Got it, I will remember.Day 7 — new session“Same task again — can you keep the context?”× Sure — what was the context again?(forgot every detail you taught it)+ MEMORYLAKE LAYERMemory auto-loadedStyle as Skill memorySource authority as Fact memoryPrior coverage as Event memorySESSION OUTPUT✓ Same prompt, on-brand answerNo re-briefing required.

Give Publishing Teams Editorial Memory That Holds Across Every Writer

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The problem: editorial knowledge fragments by the time it matters

The senior editor flagged the same Oxford comma issue in three drafts this week. The fact-checker re-verifies sources already cleared last month. AP Stylebook updates ship; the team's AI tools never see them. The editorial memory exists — distributed across heads, threads, and PDFs nobody opens.

How MemoryLake solves editorial memory for publishing teams

Style as Skill memory

Style as Skill memory

House style rules — comma usage, hyphenation, banned phrases — stored as callable skill memory.

MEMORYSource authority as Fact m…

Source authority as Fact memory

Trusted sources, recent retractions, expert quotes — all versioned and retrievable.

MEMORYPrior coverage as Event memory

Prior coverage as Event memory

Searchable timeline of past stories so writers don't re-cover the same beat without context.

Reflection memory captures editor corrections

Reflection memory captures editor corrections

Pattern in editor feedback becomes guidance the AI applies to future drafts.

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How it works for editorial memory

  1. Connect — Import the style guide, source database, past articles, and editor feedback.
  2. Structure — Each becomes typed memory: skills, facts, events, reflections.
  3. Reuse — Writers and AI drafting tools retrieve the relevant editorial memory per piece.

Before vs. after: editorial memory

Without MemoryLakeWith MemoryLake
New writer's first draftHeavy red-pen passHouse style auto-applied
Re-covering a beatNo memory of past anglesEvent memory of prior coverage
Source flagged as unreliable last monthStill appears in draftsMemory marks it untrusted
Style guide updateUpdates lag in AI toolsMemory updates in real time

Who this is for

Newsroom editors, publication leads, and content teams at media organizations producing high volumes of AI-assisted content — where style, source integrity, and prior coverage must hold across writers and AI tools.

Runnable demoabout 7 minutes

Try this use case on your own account

The Tidewater Ledger keeps every published story in one Library folder, tagged at upload with date, desk and author, and indexed so writers can ask what the paper has already covered. The archive is bigger than one page. The demo reconciles the folder against the index page by page, retracts a whole source folder with one tag, and checks a new writer's draft for prior coverage, retracted sources and house style.

What you will see

  • 55 stories uploaded with lib upload --xattrs. One lib list call returns 50 (asking for 51 is refused). Every page until the continuation token is null returns all 55, and the 5 oldest are the ones a single call misses
  • proj doc list returns 20 by default, so a one-call reconciliation reports 35 stories unindexed that are all indexed. Paging both lists gives 55 = 55, then names the one late story so only it is imported
  • A standards editor tags sources/harbor-authority/ as retracted with one lib xattr set. Files don't inherit folder tags (0 of 3 carry it), so the demo walks parent_item_id and finds 3 of 3 retracted
  • The draft check: 2 retracted sources, 4 house-style problems from rules stored as facts with a check pattern in metadata (0 flags on published stories), and prior coverage dated from the tags. Plus 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/editorial-memory
export MEMORYLAKE_API_KEY=sk-…
python3 demo.py          # or: python3 web/server.py
Open on GitHubRead the walkthrough

Watch it run · real, unedited recordings

CLI demo7:54Web demo8:17

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Frequently asked questions

Can we restrict who edits the style memory?

Yes. Editors-only write scope; writers read-only. Audit trail per change.

Does it integrate with our CMS?

Through REST API or via the AI tools your CMS uses. Direct connectors for major CMS platforms are available.

How does it handle conflicting style decisions?

Conflict detection surfaces the contradiction. Editors review and resolve; the memory carries the rationale forward.

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