Customer Supportmacro and template memory for support teams

Give Support Teams Macro and Template Memory That Stays Current and AI Can Use

Support teams build macros and templates over years. They go stale. AI support tools can't use them at runtime. MemoryLake turns macros and templates into AI-readable memory that stays versioned and current.

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The problem: support macros go stale and AI tools ignore them

The macro library has 80 saved replies. Half reference policies that changed last quarter. The AI assistant in Zendesk doesn't even read the macros. Agents handpick or skip them, defeating the standardization the macros existed to provide.

How MemoryLake captures macro memory

Macro as skill memory

Each template stored with usage rules.

Versioned with effective dates

Updates propagate immediately.

Conflict detection on stale macros

When a policy changes, dependent macros flag.

Cross-tool retrieval

Zendesk, Intercom, custom support tools unified.

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How it works for macro and template memory

Connect — Import macros from support tools.
Structure — Each becomes versioned skill memory.
Reuse — Support AI tools call macros by name; updates propagate.

Before vs. after: support macro memory

	Without MemoryLake	With MemoryLake
Macro freshness	Goes stale	Versioned and current
AI tool uses macros	Rarely	Native
Cross-tool macro consistency	Variable	Unified
Audit macro version used	Limited	Memory provenance

Who this is for

Support team leads at 5-100 person support orgs — where macro libraries exist but aren't keeping pace with AI assistance.

Runnable demoabout 6 minutes

Try this use case on your own account

A fictional outdoor-gear shop moves 7 saved replies from Zendesk and Intercom into one MemoryLake project, next to the support policy they are supposed to follow. Each macro is one fact, with its name, source tool and the policy sections it depends on in its metadata. MemoryLake's contradiction detector checks every write: it flags a macro that disagrees with the policy and quotes the policy back, a macro that contradicts itself, and two tools' macros that disagree with each other. The support lead settles each with a different strategy. Then the policy changes: the new document alone flags nothing, so the macros that depend on the changed sections are re-filed, and the shipping macro is flagged against the new policy and fixed.

What you will see

Three kinds of conflict on write (fact conflict list): macro vs policy with the policy excerpt (m2d), a macro against itself (self), Zendesk vs Intercom (m2m)
All four strategies: edit_fact in place, trust_document + version 2, trust_fact (the server rewrites the macro, so the approved wording is restored; fact trace shows all three versions) and dismiss on the conflict the fix made stale
A new policy document flags nothing by itself; re-filing the dependent macros does, and the shipping macro is flagged against v4 (11 of 11 runs)
A support assistant pulls the current macro by name (metadata.macro) or by question (search --types fact, 3 of 3); every resolution read back with fact conflict get as an audit file. 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/macro-memory
export MEMORYLAKE_API_KEY=sk-…
python3 demo.py          # or: python3 web/server.py

Open on GitHub Read the walkthrough