Give Real Estate Teams AI That Remembers Every Client, Deal, and Property
When a client calls back three weeks later, your AI should already know they want a three-bedroom in the north end, under $800K, and walked away from two offers last spring. MemoryLake stores client history, deal timelines, and market research so your AI picks up exactly where you left off. No re-briefing. No lost context.
Give Real Estate Teams AI That Remembers Every Client, Deal, and Property
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The Memory Problem
Real estate work happens across weeks and months — client calls, property tours, offer negotiations, market comparisons. Every time you open a new AI session, that history is gone. You re-explain client preferences, re-paste market data, and rebuild deal context from scratch, while the client assumes you remember everything.
What MemoryLake Does Differently
Client preferences that persist
Fact Memory stores each client's must-haves, deal-breakers, budget range, and preferred neighborhoods with source attribution. When a matching property comes up months later, your AI already knows the criteria.
Deal timelines that build automatically
Event Memory tracks every offer, counteroffer, inspection, and closing in chronological order. Ask your AI "where did the Hendersons deal stall?" and get a precise answer, not a blank response.
Full client conversation history
Conversation Memory makes every prior AI session permanently searchable. Pull up what was discussed with a buyer three months ago without digging through notes or email threads.
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How It Works
- Connect — Link MemoryLake to your existing tools via REST API, Google Workspace, or Dropbox. Log client calls, property research, and deal notes directly into memory.
- Structure — MemoryLake automatically assigns memory type: client facts go into Fact Memory, deal milestones into Event Memory, session notes into Conversation Memory.
- Reuse — Any AI model you use — ChatGPT, Claude, Gemini — pulls from the same persistent memory. Client context follows you across every tool and every session.
Before & After
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Client call after 3-week gap | Re-explain preferences from scratch | AI recalls full client profile instantly |
| Tracking offer history | Search emails and paper notes | Event Memory shows complete deal timeline |
| Market research between sessions | Paste data into every new chat | Research stored and queryable anytime |
| Team handoff between agents | Buyer knowledge lives only in one agent's head | Shared memory accessible to the whole team |
Built For
MemoryLake is built for real estate agents managing active buyer and seller pipelines, brokers overseeing multiple agents and client relationships, and property analysts doing ongoing market research across neighborhoods and asset classes. If your work spans weeks or months and involves returning clients, persistent memory is not optional — it's the difference between professional recall and starting over.
Try this use case on your own account
A buyer's agency lets MemoryLake remember its client intakes, and the agent's assistant writes to listing agents through MemoryRouter. Each client said one thing in confidence: a divorce behind a closing deadline, a real price ceiling, a pregnancy. Extraction keeps it as an ordinary fact, and the emails to the other side repeat it. A rule in the prompt halves the leaks; marking the facts PRIVATE makes them worse. Moving the marked facts to a team-only project stops the leak, but the model then invents a reason from the client's other facts. One content-free stub fixes most of that, and the team still knows everything.
What you will see
- Each intake is a DIRECT conversation; facts land on the client's actor, and every draft is
router chat --boundaryover that actor only. A no-memory control (no boundary) leaks 0 of 108 drafts - As remembered: 28 of 108 drafts state the secret ("to ensure the property is not classified as marital property"); a "never share" rule in the system prompt: 15 of 108
- An actor-scoped
fact instructionthat prefixes private factsPRIVATE:marks them whenever extraction records them, and 64 of 108 drafts leak: the label is just text to the model - The app moves them with
fact list --query PRIVATE:to a team-only project: 0 leaks, but 53 of 108 drafts invent a reason (her mother, a job relocation); a stub fact cuts that to 14, and a team boundary (actor + team-only notes) still answers 54 of 54. Plusmemoryanddraftsubcommands 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/private-remark-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
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Frequently asked questions
Does MemoryLake work with the AI tools I already use?
Does MemoryLake work with the AI tools I already use?
Yes. MemoryLake supports ChatGPT, Claude, Gemini, and any model accessible via API endpoint. You don't have to switch tools — MemoryLake adds persistent memory to whatever AI you're already using.
How is client data protected?
How is client data protected?
All data is encrypted with AES-256 and end-to-end encryption in transit. MemoryLake is ISO 27001 certified, SOC 2 Type II compliant, and meets GDPR and CCPA requirements. Client information stays within your account and is never used to train external models.
Can multiple agents on a team share the same client memory?
Can multiple agents on a team share the same client memory?
Yes. MemoryLake supports shared team memory with role-based access control. A buyer's agent can log client preferences that a showing agent or transaction coordinator can immediately access — with full audit trails for every memory read and write.