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A product from Somewhere
UMO · Standard
Raw text, audio, documents, APIs
Accepts · Layer 01
Confidence: 0.97Receipted
UMO · Preference
await mi.capture({ content, intent, tags })
API · Manual capture
Source: docsEncrypted
UMO · Relationship
Who said it, when, where
UMO shape · Provenance
Provenance: Multi-source
UMO · Timeline
await mi.listen({ source: 'slack', filter: 'decisions' })
API · Auto listening
Hash: 8f3a...c2Portable
UMO · Insight
Extract what was meant, not just said
Algorithm · Layer 02
Confidence: 0.94Queryable
UMO · Context
Encryption, receipts, semantic extraction
Algorithm output
Source: Layer 02
UMO · Approval
mi.capture() mi.query() mi.recall()
API · Layer 03
ReceiptedE2E
UMO · Pattern
Historical ingestion, bulk import
Capture mode · Legacy data
Auto-enriched
UMO · Reference
Structured, receipted, queryable memory
Pipeline output
Linked: 3 UMOs
UMO · Sentiment
Three layers create complete pipeline
Architecture
Confidence: 0.89
UMO · Dependency
Connecting the dots across sessions
Product principle
Status: Live
UMO · Identity
Not another knowledge graph
Positioning
MI™ is you.
UMO · Rhythm
Cross-referencing at capture time
Algorithm · Auto-enriched
Confidence: 0.91
UMO · Trigger
Paving a road that leads nowhere
Product principle
PatternEnriched
UMO · Commitment
Data went in raw, comes out understood
Pipeline promise
Hash: 2d9b...f4
UMO · Preference
REST + API, three lines of code
API · Integration
Source: Multi
UMO · Decision
Patented algorithm, one API call
UMO spec
Receipted
UMO · Context
Portable across every tool, every session
Feature
Linked: 2 UMOs
UMO · Insight
Invisible context becomes part of memory
Algorithm · Semantic
Queryable
HOW IT WORKS

Not another
knowledge graph.

Moving data closer together without improving the data first is like paving a road that leads nowhere.

Private Structured Provenance Portable Permanent

HOW MI™ WORKS

Five operations.
Everything your data needs.

Not a black box. Five specific things MI™ does with every piece of data you give it. Each one does exactly what it says.

01 · CAPTURE

Capture

Put something in. A note, a document, a meeting, a decision. MI™ ingests it, structures it, and gives it a cryptographic receipt so you can always prove it existed exactly as written.

mi.capture("Sarah prefers Mindful Gray SW 7016")

02 · ASK

Ask

Query your data in plain language. Not keywords. Not file names. Ask what you actually want to know and MI™ finds the answer across everything you have ever captured.

mi.ask("What color did we use on the Henderson exterior?")

03 · VERIFY

Verify

Prove it. Every memory has a three-part hash chain: a fingerprint of the exact words, a fingerprint of the meaning, and a combined chain hash. If anything was changed, the hash will not match. If it matches, nothing changed.

mi.verify("umo_01KN3FZ5B012YNHGR7JF")

04 · EXPLAIN

Explain

See inside the machine. Explain shows you exactly what MI™ understood when it processed a memory: entities extracted, relationships mapped, quality scores assigned. No black box. Full transparency at every stage.

mi.explain("umo_01KN3FZ5B012YNHGR7JF")

05 · FORGET

Forget

Remove it. Completely. And get a receipt proving the removal happened at a specific timestamp. You can prove something was deleted without having to keep the thing you deleted. For regulated environments, this is essential.

mi.forget("umo_01KN3FZ5B012YNHGR7JF")

MI™ Search puts all five of these in a search bar. Type a question. The answer comes back receipted.

Get beta access →

The difference is structure.

How
This
Works To test MemoryIntelligence™, we created 50 memories from meeting notes, strategy memos, engineering logs, customer calls, and founder journals. We then asked 5 questions spanning different business functions to show how MI shows up across an organization. Each result includes a relevance score, extracted entities, and a provenance hash for verification. The LLM column shows what a language model produces from the same raw text. The MI column shows the actual structured results from the API. No generation. No LLM anywhere in the MI column.
LLM Alone
MI™ Alone (no LLM)
LLM + MI™

The LLM guesses.

It sounds fluent and comprehensive. But are you confident enough to bring this to your CEO and explain why the team made a specific decision? There are no sources, no timestamps, no trail. It is confident without proof.

MI™ retrieves.

Ranked results. Relevance scores. Provenance hashes. Entity extraction. No generation, no hallucination. Every result is a discrete, verifiable memory object. Structured evidence, not prose.

Together, they remember.

The LLM generates a fluent answer. MI grounds every claim in a specific, scored, verifiable memory. You get readability and accountability in the same response. That is the product.

HOW IT WORKS

Under the hood.

One search bar across the places your work already lives. Answers tied to real files and receipts. MI™ is memory infrastructure first: you do not need generative AI for the system to be useful, and you can add a model later if policy allows.

01
THE
STANDARD
What Shape Memory Takes
The Standard is our patent-pending Unified Memory Object (UMO). It gives your data structure before anything else touches it.

  • Who said it
  • When they said it
  • Where they said it
Every memory takes the same shape so when you query it directly (or layer an optional model on top where policy allows), your memories start to create a narrative instead of isolated data points that don't know about each other.
Accepts: Raw text, audio, documents, APIs
02
THE
ALGORITHM
What Happens Inside
Once your data has a recognizable shape (aka a "memory"), MI™ can now extract what was meant, not just what was said. MI™ then uses that meaning to create relationships between your memories.

It assigns provenance (the place of origin or earliest known history of something), timestamps this moment in time and creates a tangible record of data that otherwise went undetected. The invisible context between your data points becomes part of the memory itself. Over time, the narrative of your work and life become more and more enriched. Everything becomes more meaningful.
Encryption · Receipts · Semantic extraction · Cross-referencing
03
THE
API
What you get on the other side
Structured, receipted, queryable memory objects. Ready to use in three lines of code. Portable across every tool, every session, every context. Your data went in raw. It comes out understood.

THREE CAPTURE MODES:
  1. Historical Ingestion
    Example: import CRM exports + chat logs.
  2. Manual Capture
    Example: save a key decision from a meeting.
  3. Automatic Listening
    Example: monitor Slack and email for signals.
mi.capture() · mi.query() · mi.recall() · REST + API

Not generative AI

So no, it’s not another AI tool.

It’s actually not even AI at all.

Look what can happen when your data means something. Why generate something when your data is already telling the full story.

How people already explain us

Stripe turned payments into one API the whole industry could build on.

MI™ does the same for memory, one standard shape so every system can store, query, and receipt what you already know.

Stripe · payments rail  →  MI™ · memory rail

SQL gave databases a shared language for asking questions.

MI™ gives your scattered files and conversations a shared structure for search, so answers come from receipted data, not from a model guessing.

SQL · query language  →  MI™ · memory standard

Git made every change attributable to a commit.

MI™ makes every answer attributable to a source, with cryptographic receipts so audit-heavy teams can show where information came from without turning on generative AI.

Git · provenance per commit  →  MI™ · provenance per memory

Same playbook as infrastructure that quietly became universal: one contract, integrations everywhere, less bespoke glue. Memory is the piece that was still missing.

MemorySpace · Custom interaction layer · Beta waitlist
Query: When can I use a dedicated UI tailored to how we capture and query memory?

MemorySpace is for teams that need a custom interaction layer on top of the standard: your workflows, your capture patterns, and plain-language search over receipted memory. Join the waitlist; we will notify you when the beta opens.