Give Strategy Teams AI That Builds Cumulative Competitor Knowledge
Competitive intelligence is not a one-time project — it's a knowledge base that should compound over months. MemoryLake stores competitor research persistently, flags when new sources contradict prior findings, and makes your entire competitive picture queryable from any AI tool your team uses.
Give Strategy Teams AI That Builds Cumulative Competitor Knowledge
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The Memory Problem
Competitive research is time-consuming to build and fragile to maintain. An analyst spends two hours pulling together a competitor's pricing, positioning, and product moves — then that context lives in a single AI chat session that disappears the next day. The next time someone needs competitive data, they start from scratch or pull from a static document that hasn't been updated in months. When two sources disagree on a competitor claim, nobody notices.
What MemoryLake Does Differently
Competitor knowledge that compounds over time
Fact Memory stores competitor intelligence with full source attribution and conflict detection. New research builds on prior findings rather than replacing them. Your competitive picture grows more complete with every session, not more fragmented.
Automatic flags when sources conflict
When a new source contradicts a stored competitor claim — different pricing, contradictory roadmap signals, conflicting market share data — MemoryLake flags the conflict and shows you both sources. You decide what to trust; the system makes sure you see the discrepancy.
Research history you can actually query
Conversation Memory makes every prior competitive research session permanently searchable. Find the analysis you ran on a specific competitor three months ago, pull all sessions where a competitor's enterprise tier was discussed, or retrieve the context from a particular analyst's deep-dive.
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How It Works
- Connect — Bring in research from SEC EDGAR filings (3M+ built-in), news sources, analyst reports, and internal data via Google Workspace, Dropbox, REST API, or direct input. MemoryLake's D1 Engine parses complex PDFs and financial documents automatically.
- Structure — Competitor facts, research sessions, and market events are stored in the appropriate memory type with conflict detection running automatically as new data arrives.
- Reuse — Any team member using any supported AI model — Claude, ChatGPT, Gemini, Perplexity — queries from the same shared competitive memory. No more duplicate research across the team.
Before & After
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Continuing competitor research | Rebuild context from scratch each session | Full research history available immediately |
| Conflicting competitor data | May go unnoticed; last source wins | Conflict detection flags discrepancy with both sources |
| Team sharing competitive intel | Static docs and Slack threads | Shared queryable memory across all team members |
| SEC filing and financial research | Manual search and document prep | 3M+ EDGAR filings accessible as native dataset |
Built For
MemoryLake is built for strategy teams, product marketers, and competitive intelligence analysts who need to maintain an accurate, current, and queryable picture of competitor positioning, pricing, product moves, and market signals over time. It is particularly useful for teams that pull from multiple sources — public filings, analyst reports, customer feedback, news — where conflicting signals are common and source attribution matters.
Try this use case on your own account
The sales team keeps its competitive-intelligence pack in shared memory: six win-rate charts, one rival and one year each, every point labelled. The rival and the year are only in each chart's title. On one page the index splits the title off, and MemoryRouter attaches the right page as eight numbers that belong to nobody: under the slide export's file names (ci-pack-p2.pdf) the assistant says it has no figure, or that Brisk was ahead when Halvard was. The same bytes named halvard-win-rate-2026.pdf come back right — the file name is the one label every attached entry carries. A page whose year the index misread fails under both names.
What you will see
- Six chart PDFs (Halvard, Brisk, Ostrava × 2025, 2026; 48 numbers, all different) stored twice, byte for byte: under the slide export's names and named for rival, metric and year, one project and one boundary each; the rep's assistant is
router chat --boundary(gpt-4o-mini, temperature 0) search --types documentshows each page's chart item: page 2's title was split off in 18 of 18 imports, page 5's year read as 2023 in 18 of 18; the other four kept their title every time- Six questions, 3 answers each, over 9 runs: as exported 46 of 162 right, named files 102 of 162 — “who was ahead in July” 2 of 27 (19 named Brisk) vs 27 of 27; Ostrava March 2025 (the misread page) 0 of 27 under both
- A checker call (Claude copies out what MemoryRouter attached) shows page 2 arriving with all eight values and no rival or year in its text, every time — only the file label tells them apart. Plus a
pagesubcommand 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/win-rate-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
What built-in data sources are relevant for competitive intelligence?
What built-in data sources are relevant for competitive intelligence?
MemoryLake includes 3M+ SEC EDGAR filings as a native dataset — useful for public company financials, segment reporting, and strategic disclosures. You can also bring in proprietary research, analyst reports, and news feeds via REST API and store findings in persistent Fact Memory.
How does conflict detection work when two sources disagree on a competitor claim?
How does conflict detection work when two sources disagree on a competitor claim?
When a new piece of information contradicts a fact already stored in memory — for example, two sources reporting different pricing for a competitor's enterprise tier — MemoryLake flags the conflict and retains both claims with their respective source attributions. You review the discrepancy and decide how to resolve it. Nothing is silently overwritten.
Can different people on the team contribute to and access the same competitive memory?
Can different people on the team contribute to and access the same competitive memory?
Yes. MemoryLake supports shared team memory with role-based access control. An analyst's research session automatically becomes available to the strategy director, product manager, or sales team based on the permissions you configure. All contributions are tracked with full audit trails.