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aswritten[ai]

Collective Memory for AI-Native Teams

At the cursor institutional knowledge Day 1->exit.

Collaborative, Git-Native AI Worldview Across Teams from Coding to Content.
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How long does it take to set up a new employee’s[1]client AI to give the same answers yours does?

  1. client

    employee, client, investor, partner, or really anyone you’re working with.

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You can’t. It’s impossible.

Your AI has observed 2 years of conversations. Their AI has observed nothing.

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New people have no idea what to do

When they join your company

You spend all day answering questions

Instead of doing your job

Their AI could answer if it had collective memory.

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The five costs of fragmented AI memory

Total: $1.6M+/year for 10-person team

get a free audit of your AI fragmentation costs ->

  1. Onboarding Crisis

    New people have no idea what to do. 3–6 months @ 50% productivity. $214K/year for team adding 5 people.
  1. Knowledge Loss

    When employees leave, 2 years of AI context leaves with them. Gone forever. $90K/year in replacement productivity loss.
  1. Constant Interruptions

    You spend all day answering questions AI should answer. 10 hours/week per senior person. $400K/year in senior time.
  1. Vendor Lock-In

    Stuck with one AI tool even when better ones exist. Can’t use Claude for reasoning, Cursor for coding. $780K/year using wrong tools.
  1. Fragmentation

    Sales AI knows customer needs, Product doesn’t. Engineering knows constraints, Sales doesn’t. Different answers to same questions. $120K/year in rework.
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aswritten[ai]

Collective memory you build together

read the case study ->

Your AI observes what you work on. When something’s important, you draft a memory together. All your team’s AIs work from collective memory, as written.

Not surveillance. Intentional curation. Not every conversation—just what you decided was worth writing to memory.

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How it works

Watch a 5-min walkthrough of collective memory in action ->

The flow:

  1. AI detects novelty → 2. AI prompts: "Should I write this to memory?” → 3. Draft memory together (collaborative) → 4. Commit to the collective memory (git-native) → 5. All team AIs remember, as written.

Example: After strategy call, AI says “I observed we’ve decided to pivot to healthcare. Should we write this to collective memory?” You say yes, draft memory, commit. 10 minutes later, Product asks their AI ”What’s our healthcare positioning?" and gets the answer immediately.

Your data, your control: Git-native repository you own. Version-controlled. Export anytime. Never locked in.

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One Worldview, Aligned Output Across Teams

AI-Assisted Development - Coding agents (Cursor, Claude Code, GitHub Copilot) read compiled snapshot. Understand ADRs, architecture decisions, customer context while implementing. Features align to org direction automatically. PM freed from constant context questions. Rework rate: 15% → <5%.

Executive Assistance - Strategy discussions become queryable memories. Sales, Product, Engineering work from same context. New hires productive day 1 (AI remembers everything across teams, as written). Knowledge persists when people leave (zero loss). Onboarding: 3–6 months → day 1. Senior time freed: 8 hours/week.

Content Generation - Speeches, presentations, blog posts auto-generate from organizational worldview. When worldview changes, all materials regenerate automatically. Brand voice consistent. One worldview, branch for scenarios (no version confusion). Idea → updated materials: days → 15 minutes.

Organizational Change Tracking - Every written change creates queryable organizational history. “What changed this quarter?” → AI generates report from commit history. Auto-generate updates for VCs, board, advisors, clients. Track strategic progress, verify execution against stated goals. Board deck preparation: days → 15 minutes.

Get the technical architecture & security docs ->

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Who this is for

See how agencies implement collective memory ->

Small teams that move fast:

  • Creative agencies (process is the product)
  • Consulting firms (methodology must persist)
  • Digital agencies (client knowledge critical)
  • Eventually: Developer tools, SaaS, any technical team

For VCs/Board/Advisors:

Portfolio companies auto-generate updates from written organizational state. “What changed this quarter?” → queryable, not just reported. Verify execution against stated strategy. Board prep: days → 15 minutes.

Not enterprise.

This is anti-enterprise. Small teams need speed, not bureaucracy.

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Implementation: 30–60 days to org-wide

Get a custom pilot proposal ->

Phase 1 (30 days) - Pilot with 5–10 people, one team. Connect AIs (5 min/person), write decisions, measure results.

Phase 2 (30 days) - Add 2–3 more roles. Network effects kick in. “Sales saw engineering constraints immediately.”

Phase 3 - Org-wide. All roles connected. New hires day 1 productive. Departures = zero knowledge loss.

Pricing: Pilot free/low-cost (your API keys). Team (5–15 people) $2–5K/month. Org-wide (15–50 people) $10–20K/month. Value anchor: You’re losing $1.6M/year. What’s 1% of that worth?

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Ready to try it?

Day 1-exit institutional knowledge, as written.

30 days. Your API keys. Your data.

Talk to Scarlet about your pilot ->

Or reach out | scarlet@aswritten.ai

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