MemoryIntelligence™, Competitor Analysis

Not a knowledge graph.
Not a vector database.

MemoryIntelligence™ is the only solution that addresses all dimensions of the memory standard, then we take it even further.

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CAPTURE STORAGE OWNERSHIP PROVENANCE RETRIEVAL
auto vs. manual local vs. cloud who controls receipts, when/how semantic, cross-app, persistent
FROM A DATA PERSPECTIVE
  • MemoryIntelligence™
  • Vector Store / RAG
  • Semantic Search
  • Knowledge Graph
  • NotebookLM

The memory standard is judged across five dimensions. Most tools hit one or two. MI™ hits all five — and goes further: 95% token reduction, five dimensions covered, first memory in minutes.

Not a vector database · Not a knowledge graph · Not a search tool · Not a note-taking app ·Memory has no standard. We built one. · Not a vector database · Not a knowledge graph · Not a search tool · Not a note-taking app ·Memory has no standard. We built one. · Not a vector database · Not a knowledge graph · Not a search tool · Not a note-taking app ·Memory has no standard. We built one. · Not a vector database · Not a knowledge graph · Not a search tool · Not a note-taking app ·Memory has no standard. We built one. ·
The Infrastructure Spectrum

How the approach compares

Most tools stop at layer 2 or 3. MI starts where they end.

Infra 01
Vector Store / RAG
"Here's a chunk of text that matched your query."
e.g. Pinecone, Weaviate, basic RAG
Converts data into numerical embeddings and retrieves the closest match. Fast, but context-free. Hits 1–2 dimensions.
✗ No temporal context · ✗ No structure or meaning · ✗ No verification
Infra 02
Semantic Search
"Here's what you wrote, and roughly when."
e.g. Obsidian + plugins, NotebookLM
Meaning-based retrieval that understands intent. Better recall, but still dependent on what you manually put in. Hits 1–2 dimensions.
✗ Manual input required · ✗ Single source only · ✗ No provenance
Infra 03
Knowledge Graph
"Here's what you wrote, and here's how it connects to something else."
e.g. Neo4j, Diffbot, custom graph DBs
Maps entities and relationships across data. Powerful for structured domains, but brittle with real-world unstructured input. Hits 1–2 dimensions.
✗ Expensive to maintain · ✗ Breaks on messy data · ✗ No receipts or rights layer
MemoryIntelligence™
"What you captured, when you captured it, verified. Structured automatically across every source, retrievable in milliseconds, across every dimension."
Patent Pending
Structures, compresses, and receipts every piece of data locally. No cloud. No manual effort. All five dimensions.
✓ Auto-capture · ✓ Verifiable data receipts · ✓ Cross-app, local-first
Head to Head

How the tools compare

Click any tool to see how it stacks up against MemoryIntelligence™.

MemoryIntelligence™
Competitor
Obsidian
A second brain for your notes — you still have to build it yourself.
What it is
Local, private markdown note-taking with graph-based linking. Manual, always.
Overlap
✓ Local first✓ Data ownership~ Linking
Falls short
✗ 100% manual✗ No auto-capture✗ No provenance✗ No receipts✗ No cross-app✗ Single silo
Bottom line
A filing cabinet you stock yourself. Most vaults become graveyards within months.
Layer
Infra 02: Semantic search, fully manual
MemoryIntelligence™
MI™
Everything Obsidian does, and what it never could.
What it is
Auto-capture, structure, and retrieve memory across all your apps. Local-first. Zero maintenance.
Matches
✓ Local first✓ Data ownership✓ Note linking✓ Privacy
Goes further
+ Auto-capture+ Provenance receipts+ Cross-app memory+ Zero maintenance+ Unified
Bottom line
Memory that stocks itself. 95% token reduction. Five dimensions.
Layer
MI™: All 5 dimensions, patent-pending
Competitor
NotebookLM
Google reads everything you upload. Session ends, memory is gone.
What it is
Google's AI notebook. Upload documents, ask questions. Google owns the session and your data.
Overlap
~ Document retrieval~ Q&A interface
Falls short
✗ Cloud only✗ Google owns data✗ No provenance✗ Session-based✗ No persistence✗ No dev API
Bottom line
Every upload is a data gift to Google. Every session ends with everything forgotten.
Layer
Infra 02: Semantic search inside a Google silo
MemoryIntelligence™
MI™
Everything NotebookLM does, without giving your data to Google.
What it is
100% local memory with persistent retrieval, developer API, and verifiable provenance.
Matches
✓ Document retrieval✓ Q&A interface✓ Source grounding
Goes further
+ 100% local+ Persistent memory+ Grows over time+ Developer API+ No hallucination
Bottom line
NLM is a session. MI is memory. NLM forgets when you close the tab. MI never does.
Layer
MI™: All 5 dimensions, patent-pending
Competitor
Omni
BI dashboards. Not in the memory business at all.
What it is
Business intelligence and visualization. Think Looker or Tableau. Dashboards, SQL, structured analytics.
Overlap
✗ Minimal Different layer entirely.
Falls short
✗ Not memory✗ Requires SQL✗ Requires data team✗ Cloud only✗ No unstructured
Bottom line
Omni shows you the data. MI is the reason there's anything worth showing.
Layer
Visualization layer, not the memory standard
MemoryIntelligence™
MI™
The memory standard Omni sits on top of — if it could.
What it is
Memory infrastructure for any developer. No data team. No SQL. No warehouse.
Matches
~ Data pipeline thinking~ Structured output
Goes further
+ Unstructured data+ No SQL needed+ Auto-capture+ Edge processing+ Provenance
Bottom line
MI could be the memory engine powering a BI tool like Omni.
Layer
MI™: All 5 dimensions, patent-pending
Competitor
dbt Labs
SQL-based transformation for warehouses. Not built for humans.
What it is
Data transformation tool for engineers. Cleans and models structured data in your cloud warehouse.
Overlap
~ Data structuring~ Pipeline thinking
Falls short
✗ Enterprise only✗ Requires SQL✗ Cloud warehouse✗ No provenance✗ No retrieval✗ No edge
Bottom line
Takes weeks to set up, requires a specialist, and still can't process a Slack message.
Layer
Infra 01/02: Transformation for warehoused structured data
MemoryIntelligence™
MI™
Both structure raw data. MI does it without SQL, a warehouse, or a data team.
What it is
Auto-structure any data type. npm install and go. Under an hour to first memory.
Matches
✓ Data structuring✓ Pipeline thinking✓ Data lineage
Goes further
+ No SQL needed+ Unstructured data+ Edge processing+ Provenance+ Up in an hour
Bottom line
Conversations, notes, documents. Human memory, not rows and columns.
Layer
MI™: All 5 dimensions, patent-pending
Full Comparison

All tools, all dimensions

Where the key differentiators actually land.

Dimension MI™ Obsidian NotebookLM Omni dbt Labs
Local / Edge Processing ✓ 100% local ✓ Yes ✗ Cloud (Google) ✗ Cloud ✗ Cloud warehouse
Auto-capture ✓ Auto + manual ✗ Manual only ~ Upload required ✗ Manual ~ Pipeline setup
Provenance / Receipts ✓ Full data receipts ✗ None ✗ None ✗ None ~ Lineage only
Unstructured data ✓ All formats ✓ Markdown ✓ Docs/PDFs ✗ Structured only ✗ Structured only
Semantic retrieval ✓ 451% improvement ~ Plugin required ✓ AI-powered ✗ SQL only ✗ Not a retrieval tool
Cross-app memory ✓ Any connected source ✗ Silo ✗ Silo ✗ Silo ~ Warehouse only
Developer API ✓ npm install ✗ No ✗ No ~ API ✓ Yes (SQL)
Per-query cost ✓ $0.00 Free Free (Google) Subscription Subscription
Data ownership ✓ Always yours ✓ Yours ✗ Google's ✗ Vendor ✗ Warehouse vendor
Requires technical team ✓ One developer No No Yes Yes (data eng)

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