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HOW IT WORKS

MemoryIntelligence™
finds what happened.

Keyword Search No Results
Email “Re: Johnson — loved the crew, thanks for the quick start!” Sounds great. Zero signal about a problem.
CRM Johnson tagged “VIP referral source” after the spring picnic photo. Positive. Unrelated to coatings.
Reviews Five-star Google note mentions “polite team.” Keyword “issue” never appears, so this ranks anyway.
✗
Still nothing useful. Three tools, three partial hits, no shared story. Try different keywords, or ask someone who was there.
Search Found
Johnson job: quality concern
3 sources · 3 receipts · Apr 1–3
MI™ Receipted
What happened
Crew noted texture problem on 2nd floor. Client expressed dissatisfaction. Warranty call logged same week.
Sources
3
Job date
Apr 1
Status
Open
Where MI™ found it
Job note · Marcus WebbManual: “texture came out rough on the 2nd floor coat” · Apr 1
Text · Johnson to MarcusAuto: “not quite what we expected” · Apr 2
QuickBooks · warranty callAuto · Apr 3, $0 billed
None of these sources contain the word “issue.” MI™ connected manual and auto captures to find it anyway.
Did you know?

For this job, field notes never used the word “issue,” but they did describe texture and a warranty touch-up. That is the same story keyword search keeps missing.

The difference is structure.

How
This
Works 50 memories. 5 questions. Each MI™ result includes a relevance score, extracted entities, and a provenance hash. The LLM column generates. The MI™ column retrieves. No generation anywhere in the MI™ column.
LLM Alone
MI™ Alone (no LLM)
LLM + MI™

THE TLDR

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.

BUILDING CONTEXT

The setup has never been easier. The answers never clearer. The receipts never optional.

Even with full leadership buy-in and a solid process, getting your org ready for trusted AI is brutal: align exports, owners, and taxonomies across tools, launch a wiki or data project, then pay again every time someone leaves or a system changes. Most teams skip the hard part and paste into a chatbot. Better than nothing. You still cannot show where an answer came from or what changed since last week.

The usual path

Months of context work before anyone searches

  • Spreadsheet audits and folder rules that drift by Friday
  • Internal wiki pushes, consultants, and re-tagging sprints
  • LLM summaries that sound right with no source trail
  • Maintenance cost every time a tool or owner changes

MI™ in one sweep

Structured memory before the dashboard matters

  • Point MI™ at the files and channels you already run
  • One ingest pass turns history into receipted memory objects
  • Search and proof work the same way on day one and day five hundred
  • The UI is the easy part. The hard part is already done.

That is what makes the principal dashboard, the job search, and the compliance view feel instant. Not a prettier shell on the same mess. See the four setup steps for connect, ingest, capture, and search.

BECAUSE IT IS STRUCTURED

Your data is capable.

MI™ does not replace your systems. It gives scattered records one standard shape so capture, ask, verify, explain, and forget all behave the same way.

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("Kasey 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")

UNDER THE HOOD

MI™ > AI

MI™ retrieves what you saved, with receipts first. Models are for fluent wording, not for proof.

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

How you actually get set up

4 steps to search results
that actually mean something.

After memory is structured, setup is connect, catch-up ingest, and always-on capture. Then search and proof behave the same on day one and year two.

5 min
↓ 40% time searching
↑ 80% receipted answers
↑ 100% search accuracy
STEP 01
5 min

Connect
your toolsConnect your tools

SharePoint, Drive, Slack, email.
Point MI™ at what you already use.

ONE-TIME SETUP
STEP 02
↓ 40% time searching

Catch up your
existing dataCatch up your existing data

MI™ ingests your historical files. Years of job notes, invoices, emails, structured and receipted automatically.

RUNS AUTOMATICALLY
STEP 03
↑ 80% receipted answers

Auto-capture
from here onAuto-capture from here on

The listener runs in the background. Every new job, note, and customer interaction gets captured automatically.

ALWAYS ON + MANUAL CAPTURE
STEP 04
↑ 100% search accuracy

MI™ Search
is liveMI™ Search is live

Search so good you think it is AI. It is not.

SEARCH ALL FILES
RECEIPTED RESULTS
CROSS-SOURCE CONNECTIONS
FULL HISTORY SEARCHABLE
ZERO HALLUCINATIONS
VOICE MEMO CAPTURE
+ ADD AI ON TOP

Turn the tools you already run into memory you can query with receipts.

Join the Beta Why it matters