Give Agencies New Business Pitch Memory That Compounds Across Every Prospect
Agency new business teams pitch dozens of prospects a year. Each pitch generates learnings: what landed, what didn't, what objections recurred. AI tools used for pitch prep see one prospect at a time. MemoryLake gives agencies pitch memory that compounds across prospects and verticals.
Give Agencies New Business Pitch Memory That Compounds Across Every Prospect
Get Started FreeFree forever · No credit card required
The problem: agency pitch learnings don't transfer between prospects
You pitched a SaaS company last quarter and the pricing model objection killed the deal. This quarter you're pitching a similar SaaS — and the same objection is about to surface, except nobody on the new business team remembers the prior pitch.
How MemoryLake captures agency pitch memory
Pitch event memory
Every prospect's pitch, content, outcome captured.
Vertical-specific reflection memory
Patterns per industry surface.
Objection memory
Common objections per vertical with responses.
Cross-tool retrieval
CRM, slide tools, meeting notes unified.
Free forever · No credit card required
How it works for agency new business pitch memory
- Connect — Authorize CRM, slide tools, and meeting note sources.
- Structure — Each pitch becomes typed memory.
- Reuse — Pitch prep for new prospects retrieves similar prior pitches.
Before vs. after: agency new business pitch memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Prior pitch learnings reach new pitches | Manual | Memory-driven |
| Objection prep | Generic | Vertical-specific |
| New BD hire pitch ramp | Months | Memory access |
| Win rate over time | Variable | Compounds |
Who this is for
Agency new business and BD teams at 10-100 person agencies — where pitch volume is high and cumulative learning is supposed to be the moat.
Try this use case on your own account
Pitch memory should tell a new business team which objections keep coming up. Six SaaS pitches this year: three prospects pushed back on pricing, and only Quorvia raised a security objection, but that one objection was captured three times, by the CRM debrief ("Quorvia"), the meeting notes ("Project Lantern") and the Slack recap ("the payments prospect"). Each capture becomes its own fact, and the pitch assistant counts nicknames as prospects: "Quorvia and the payments prospect", "at least two different prospects". The same pitches captured once each never do that. The app resolving every capture to its CRM opportunity and writing one record per pitch gets it right.
What you will see
- The same six pitches into three memories: A every capture as it arrived (
conv msg append, extracted), B each pitch captured once (the control: the same text without the two repeats), C one record per pitch, joined by the capture's CRM link and written withfact add - Over 11 runs the assistant counted the one objection as more than one prospect, or listed it as recurring, A 91 / 217 answers, B 0 / 211
- Right answers: no memory 6 / 220, A 67 / 217, B 126 / 211, C 210 / 217; on "how many prospects raised a security concern" B said it did not know 42 / 43 times, C was right 41 / 41
- Five questions through MemoryRouter (
router chat --boundary) with an empty-attachment baseline, a no-memory arm, a grader with offline cases (python3 demo.py selftest), 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/pitch-objection-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
Related use cases
Frequently asked questions
Integrations?
Integrations?
HubSpot, Salesforce, Pipedrive, Notion, custom — all supported.
Privacy on competitive intel?
Privacy on competitive intel?
AES-256 E2E.
Free tier?
Free tier?
Yes — for agencies.