Icarus GrowthFoundational Implementations

AI / 04 of 08

Memory Systems

Pull your active client-facing Slack channels, email chains, and Zoom calls into a system that can be queried by your agents so that no context is lost and the entire team can work from a central source of truth.

How it runs

Two stores, not one · one store is why it degrades

Sources

Slack, email and call transcripts

Extract

LLM extracts facts and events

State

What is true now, overwritten

Events

What happened, when: append-only

continues

Query

One query layer over both

Outcome

Agents and the team query it

What it is

A memory system reads your client-facing conversations and writes them into a queryable store split two ways: what's true now, and a timestamped log of what happened.

You need this if answering "what did we agree in March" means scrolling a channel for twenty minutes, because context isn't lost occasionally, it's lost structurally.

You pay for it in repeated questions, contradicted promises, and clients explaining themselves twice.

If your team is already good at notes, this stops that discipline evaporating when the person who took them leaves.

The actual build

Memory Systems — the as-built system.

What it does for you

01

Anyone can answer a client-history question in seconds instead of re-reading a quarter of Slack

so handovers stop costing a week.

02

Every agent you build inherits the full account history for free

which is the difference between a useful assistant and a demo.

03

Client knowledge stops being one person's asset and becomes the company's

so staff turnover no longer resets relationships to zero.

Built and running

Shipped as part of the Zoom Call Agent build at School of Bots, rather than as a standalone project.

How to build it

  1. Pick your sources: client Slack channels, email threads and call transcripts
  2. Split storage in two, with a state store for facts that get replaced and an append-only event log for things that happened, timestamped and tagged
  3. Extract with an LLM but write deterministically, through one function that takes structured output and writes rows
  4. Scope every record by client from day one, because retrofitting scoping is a rebuild
  5. Put one query layer over both stores and point your agents at it

Want this one built into your business?

Thirty minutes, no deck. We map what you run today and tell you what it would take.

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