Give Agencies Case Study Memory That Makes Every Win Reusable in New Business
Every agency win should produce a reusable case study. Most don't — the project's metrics, story, and assets scatter and the BD team can't reach them when pitching similar work. MemoryLake gives agencies case study memory the new business team queries on demand.
Give Agencies Case Study Memory That Makes Every Win Reusable in New Business
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The problem: agency case study assets fragment and vanish
The brand launch for Client A drove 40% lift. The metric lives in one Slack message. The before-and-after creative lives in a shared drive nobody opens. When BD pitches similar work, they paraphrase the case study from memory — losing the punch the real numbers and assets would have had.
How MemoryLake captures agency case study memory
Per-project case study memory
Metrics, story, assets in one structured record.
Vertical and outcome tagging
Retrieve "fintech launches that drove signups" instantly.
Reflection memory for what worked
Patterns across wins surface.
Cross-tool retrieval
Project tools, drive, BD tools unified.
Free forever · No credit card required
How it works for agency case study memory
- Connect — Authorize project, asset, and BD tools.
- Structure — Each project's outcome becomes typed case study memory.
- Reuse — BD retrieves matching case studies for prospect pitches.
Before vs. after: agency case study memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Reusable case study quality | Variable | Memory-driven |
| BD access to wins | Manual scavenge | Memory query |
| Asset availability for pitches | Drive search | Memory-loaded |
| Cross-win pattern detection | Manual | Reflection memory |
Who this is for
Agency leaders and BD teams at 10-100 person agencies — where wins are not turning into BD assets fast enough.
Try this use case on your own account
An agency holds a wrap-up call at the end of every client campaign, and the new-business team writes case studies from what those calls left in memory. Six calls, four figures each, all said the same way: "Cost per lead came in at 18.40 dollars." Most calls are extracted whole, but now and then a whole call leaves nothing behind (the transcript is there, extraction reports finished, the project has no fact from it), and many kept figures come back without the client's name. The case-study assistant then says "Unknown", or gives another client's figure. Each conversation has a receipt: the app audits every call against it and pins the missing or unnamed lines word for word, with the client, campaign, date and speaker.
What you will see
- Six GROUP calls in one project; a per-call receipt audit (
conv fact-actions) that checks every figure said on the call against the facts that call left, and whether they name the client; the audited copy gets the extracted facts (fact list→fact add) plus the pinned lines - Over 7 runs, 9 of 42 calls left nothing at all; of 168 figures said, 101 were kept and 33 in a fact naming the client
- Figure questions answered right: as extracted 150 / 236 (82 "Unknown", 4 another client's figure, plus 16 where nothing was attached), with the pinned lines 252 / 252. On figures already kept with their client both scored 48 / 48: pinning cost nothing
- Twelve case-study questions through MemoryRouter (
router chat --boundary) with an empty-attachment baseline per question, plusmemoryandasksubcommands 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/wrap-up-call-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Integrations?
Integrations?
Asana, Notion, Drive, BD CRM tools, custom — supported.
Privacy on client outcomes?
Privacy on client outcomes?
AES-256 E2E with per-client controls.
Free tier?
Free tier?
Yes — for small agencies.