Marketing & Growthpitch deck memory for marketing agencies

Give Marketing Agencies Pitch Deck Memory That Builds Slides Faster

Pitch deck creation at agencies is slow because every deck starts close to scratch. The slides that worked, the patterns that landed, the approved language live in old decks the new business team digs through. MemoryLake gives marketing agencies pitch deck memory that accelerates every new deck.

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The problem: agency pitch decks rebuild from scratch every prospect

Each new pitch deck spends hours retrieving past slides, copying language, and rebuilding the credentials slide. The team's deck-building skill doesn't transfer to AI tools. New BD hires take months to ramp on deck quality.

How MemoryLake captures pitch deck memory

Slide pattern skill memory

Successful slide structures per pitch type.

Approved content memory

Credentials, case study language, positioning slides.

Per-vertical deck adaptation

Memory of what slides work for which verticals.

Cross-tool retrieval

Slide tools, brief docs, case study memory unified.

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How it works for agency pitch deck memory

Connect — Import past decks and authorize slide tools.
Structure — Slides, content, patterns become typed memory.
Reuse — Every new deck retrieves matching past patterns.

Before vs. after: agency pitch deck memory

	Without MemoryLake	With MemoryLake
Deck creation time	Hours per deck	Reduced sharply
Slide quality consistency	Drift	Memory-driven
New BD hire ramp	Months	Memory access
Cross-deck content reuse	Manual	Memory-loaded

Who this is for

Agency BD teams and creative directors building pitch decks frequently — where deck velocity is a constraint on new business throughput.

Runnable demoabout 3 minutes

Try this use case on your own account

A fictional agency is pitching an outdoor brand. Its three past decks (.pptx) and the debrief written after each pitch (.docx) are in one MemoryLake project. For every slot of the new deck (case study, credentials, process, pricing, team), MemoryLake returns the slide that answers it, cited as deck and slide number in the slide's own words. Category slides come from the same-category pitch, the rest from the newest pitch the agency won. Sentences from the debriefs, cited by page, reorder the outline. Refreshed case-study figures, pinned as approved content, get the old slide flagged. Every citation is then checked against the original file.

What you will see

Decks come back from search --types document as one passage per slide, with range = P<slide>[…] (slide number + box) and the slide's own text
Picked by what the team knows about each pitch, kept as Library attributes (lib upload --xattrs: category, won or lost); 5 of 5 slots right in 7 of 7 fresh runs
Approved figures pinned with fact add raise an m2d conflict against the old deck; its excerpt is matched back to slide 4 of the .pptx, and the outline marks that slide's numbers for replacement
Every citation re-checked against the downloaded .pptx/.docx (sha256 + that slide's XML, 8 of 8; the same quotes on the neighbouring slide all fail) and exported with the outline. 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/pitch-deck-memory
export MEMORYLAKE_API_KEY=sk-…
python3 demo.py          # or: python3 web/server.py

Open on GitHub Read the walkthrough