How it works

Every conversation becomes structured knowledge

d33pmemory's proprietary extraction pipeline transforms raw dialogue into a multi-layered knowledge architecture. Our confidence-scored model distinguishes between verified facts and probabilistic inferences — creating an ever-evolving cognitive profile that gets smarter with every interaction.

Extraction pipeline

Raw dialogue passes through our extraction engine that distills conversations into structured knowledge — facts, relationships, events, preferences, and behavioral patterns.

Step 01

Entity resolution

Disambiguate identities and concepts across fragmented conversations using multi-vector similarity.

Step 02

Knowledge extraction

Identify facts, relationships, events, preferences, and patterns. Each tagged as stated or inferred.

Step 03

Confidence scoring

Every memory gets a certainty score (0.0–1.0) with full provenance — which interaction, when, what was said.

Step 04

Context compilation

When an agent needs context, assemble the exact memories that matter — packed into a token budget.

Memory object
{
  "type": "fact",
  "content": "User follows a gluten-free diet",
  "source": "stated",
  "confidence": 0.95,
  "category": "health/dietary",
  "tags": ["gluten-free", "dietary"],
  "scope": "shared",
  "contributed_by": "slack-bot"
}

Core capabilities

Multi-layer cognitive model

Episodic, semantic, and procedural memory layers — mirroring how humans actually organize knowledge.

Confidence tracking

Every memory has a certainty score (0.0–1.0) that evolves as evidence accumulates. Stated vs inferred is always clear.

Automatic consolidation

Corroborated memories strengthen. Stale ones decay. Contradictions resolve through our consolidation engine.

Semantic recall

Search by meaning, not keywords. Describe what you need and get the most relevant memories ranked by similarity.

Context compilation

Don't dump old messages. Compile the exact memories that matter — 142 tokens replacing 15,000.

Evidence chains

Every fact has a provenance trail — which interaction, when, and whether it was stated or inferred.

Five types of knowledge

Our extraction engine categorizes every piece of knowledge, each with confidence and provenance.

FactUser follows a gluten-free diet0.95stated
RelationshipEmma is a frequent dining companion0.88inferred
EventDinner booked at Trattoria Milano for Feb 150.92stated
PreferencePrefers Italian restaurants0.74inferred
PatternBooks restaurants for 2 on weekends0.68inferred

Beyond vector search

Standard RAG

Linear token growth
Ephemeral retention
No contradiction handling
Context = message dump
No confidence or provenance

d33pmemory

99% context compression
Persistent evolving memory
Auto conflict resolution
Compiled context payloads
Confidence + evidence chains

Ready to try it?

Free to start. Two endpoints to learn.

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