Your AI agents
forget everything.
Memory Layer gives AI agents persistent, living context that syncs across sessions, machines, and tools. Stop re-explaining. Start compounding.
THE PROBLEM
Context loss is the silent tax
on AI productivity
Time wasted re-onboarding
Every new session starts at zero. You re-explain the project, re-state decisions, re-describe architecture. Every single time.
Context lost between machines
Work on your laptop, switch to your desktop — everything you built in that session is invisible on the other machine.
Projects × sessions = chaos
5 projects, 6 sessions a day, 2 machines. That's 60 context resets per week. Decisions get lost. Work gets repeated.
HOW IT WORKS
Two files. One source of truth.
Zero re-onboarding.
Memory Layer maintains a living project memory that every AI session reads from and writes back to — automatically.
Session starts
Your agent reads the project's living memory — goals, decisions, architecture, and recent history. Full context in seconds.
Agent works
Fully informed by every prior session. No re-explaining. No contradicting past decisions. No losing track of what was done.
Memory synthesizes
When the session ends, memory is rewritten — concise, current, ready. The next session on any machine picks up right where you left off.
Background sync
A scheduled task scans for new work every 15 minutes, synthesizes context, and keeps every machine converged through one source of truth.
REAL RESULTS
Built internally, measured in production
AI-assisted productivity
Context-loss errors
Re-onboarding time
Projects with full visibility
WHO IT'S FOR
Anyone running AI agents
across multiple contexts
AI consulting firms
Juggling 5-10 client projects with AI agents? Memory Layer ensures every session knows the full history — no matter who or what worked on it last.
Founders & solopreneurs
Building multiple products at once? Your AI should remember what you decided last week without you pasting a wall of context every morning.
Dev teams & agencies
Multiple developers using AI coding assistants on the same codebase? Memory Layer keeps architectural decisions consistent across all of them.
Product teams
Research, specs, sprint planning, retros — all feeding into a living memory that makes every AI interaction smarter than the last.
WHY NOT JUST USE...
Memory Layer vs. the alternatives
| Feature | Memory Layer | RAG / Vector DBs | Notion AI / Mem.ai | Manual prompts |
|---|---|---|---|---|
| Auto-synthesized living context | ✓ | ✗ | ✗ | ✗ |
| Multi-machine sync | ✓ | ✗ | ✓ | ✗ |
| Append-only audit trail | ✓ | ✗ | ✗ | ✗ |
| Works with any AI tool | ✓ | ✗ | ✗ | ✓ |
| No infra required | ✓ | ✗ | ✓ | ✓ |
| Keeps context concise (<800 words) | ✓ | ✗ | ✗ | ✗ |
| Zero-effort after setup | ✓ | ✗ | ✗ | ✗ |
“We were losing 20-30% of our AI productivity to re-onboarding. Now every session starts with full context. It's like giving your agents an institutional memory.”
Stop re-explaining.
Start compounding.
Join the waitlist for early access. Be the first to get Memory Layer when it launches.
No spam. Just launch updates and early access.