Financial desk·One morning·2026-06-10

Setting the standard for memory.

Seventeen overnight inputs hit your credit desk this morning. Four of them touched the book. MI knew which four before anyone opened them.

MemoryIntelligence is the memory API for financial desks. Deterministic extraction, zero LLMs in the write path, audit as a byproduct.

Watch the morning Read the standard
Live

06:15. The desk comes online.
The world is already moving.

Press play. Notifications arrive from the desk's real stack, each one becomes a sealed memory, and MI surfaces what touches the position before anyone has read a thing.

Look at the desk of a credit trader
Desk
Credit · EU financials
Position
$1.84B DB · 87% utilized
Catalyst
EU stress test · tomorrow
PM call
12 min
The desk
06:15AM
idle, press play
MI Memory Intelligence · listening
active 0 captured · 0 weighted · 0 clustered
Extract. Weight. Cluster. Sign. The four things that turn a notification into a memory.POST /v1/process → UMO
extract→ weight (temporal · entity · source)→ cluster→ sign + chain
awaiting first signal…
Signal MI surfaced this before the PM call
Now ask it · mi.ask()POST /v1/memories/query
12 cited receipts · 4 clusters · 1,112 tokens to answer · every claim hash-chained · see the math ↓
Illustrative sources. MI deploys inside your perimeter; licensed market data never leaves your stack.

Different desk, same standard output

What happens next

From twelve inputs to one decision.

The captures you just watched become the desk's working memory. Two moves, no clicks.

01 · Connect
Follow the connections.

This is how a UMO shows up in the signal output: the sealed decision at the center, every related claim attached with a typed relationship. Click any memory to see exactly what it surfaced. Click the decision to see why it locked.

Every decision carries its evidence.
Claim constellation · liveGET /v1/memories/{id}/related
Decision log · written automatically
Trim 5% before results. Hedge with 5y CDS.
LOCKED umo_01c4f2a9 · 06:23:41 UTC
Reinforces
CET1 ratio resilient under adverse case
umo_7c12d3a8 · Outlook · JPM
Reinforces
Sector buffers built ahead of stress test
umo_13f49e8b · Internal Research
Contradicts
22% downgrade probability in 90 days
umo_f421c809 · Outlook · Goldman
Corroborates
Adverse scenario tightened by EBA
umo_7bf3bf0d · Bloomberg
Connects to
Jan 15: trimmed 5% at 82%, outperformed 18bps
umo_a91b40fc · the desk's own trade
click any memory · what it surfaced · click the decision · why it locked
02 · Seal
Decision sealed.

The desk makes the call. MI captures it, the hash chain seals it, and the audit assembles itself as a side effect of working.

Audit becomes a byproduct, never a project.
Capture receiptGET /v1/memories/{id}/proof
StatusLOCKED
UMOumo_01c4f2a9
Decisiontrim 5% · hedge 5y CDS
Hash0d9e3903…eccf271f
Chainintact · 12 links
Linked toJun 3 RC · Jan 15 trade · Jun 8 EBA
Why5 weighted claims → trace the evidence
Sealed2026-06-10 06:23:41 UTC
The standard output · GET /v1/memories/{id} 
{
  "umo_id": "01c4f2a9-9e17-4ab2-b3f1-d0639879ed6e",
  "source": "desk-decision",
  "captured_at": "2026-06-10T06:23:41Z",
  "summary": "Trim 5% before stress test results.
              Hedge with 5y CDS. Re-review after.",
  "entities": [
    { "text": "Deutsche Bank", "type": "ORG" },
    { "text": "5%", "type": "PERCENT" },
    { "text": "5y CDS", "type": "PRODUCT" }
  ],
  "svo_triples": [
    { "subject": "desk", "verb": "TRIM",
      "object": "DB exposure 5%",
      "polarity": "affirmed", "confidence": 0.93 }
  ],
  "related": [
    { "umo": "7c12d3a8", "rel": "reinforces" },
    { "umo": "f421c809", "rel": "contradicts" },
    { "umo": "7bf3bf0d", "rel": "corroborates" },
    { "umo": "a91b40fc", "rel": "connects_to" }
  ],
  "provenance": {
    "semantic_hash": "0d9e3903…eccf271f",
    "hash_chain": "intact · 12 links",
    "lineage": "capture → extract → weight → cluster → sign"
  },
  "quality_score": 0.87,
  "validation_status": "valid"
}
Tokens · compute · cost

Meaning costs less than context.

Without MI, every question re-reads the raw haystack through a model. With MI it reads a handful of signed receipts. The with-MI number below is counted live from this page's own payload.

Without MI
ILLUSTRATIVE
question → re-read 30 days of raw context (emails · threads · filings) 48,000 tokens
→ LLM reads everything → answer
repeated on every question
cost scales with questions × context size, every time
With MI
MEASURED
capture → deterministic extraction, once per input 0 LLM tokens
question → cluster query over signed meaning ~1,400 tokens
→ cited answer with hash receipts
extraction runs once per input · questions read signed meaning, never the haystack
Questions per day, per desk120
Raw context per question, tokens
Input price, $ per 1M tokens
Without MI1,200,000 tokens per day
With MI43× less35,000 tokens per day
34×
fewer input tokens per question. The relationship holds at any scale: without MI the bill grows with questions times context, with MI it grows with meaning alone, because extraction already happened once, deterministically, at capture.
$108.00 per month without MI · $3.15 with MI · $104.85 saved per desk per month
How these numbers are made: with-MI tokens are counted from this page's own payload, twelve summaries plus citations and overhead, at four characters per token · raw context is a stated assumption, adjust it above · price defaults to $3 per million input tokens · dollars assume 30 days · institution view assumes 250 desks · capture-side extraction runs deterministically with zero LLM tokens.
The first thirty minutes

In numbers.

0
memories captured by MI across six sources
0
touch the desk's $1.84B Deutsche Bank position
0
contradicting signal surfaced before anyone opened an email
12
LLMs in the write path. The standard means deterministic.

Memory without meaning
is just storage.

Join the beta Read the standard
Beta access opens in small batches. A real person replies.
Deploys inside your perimeter · raw content discarded after extraction · hash-chained audit export.