For AI Memory Platforms

AI Memory Platforms_

Your memory ingests emotional context. Do you know what was significant, why, or under what consent?

Memory platforms store millions of emotional moments. Without governance, each is a liability. DeepaData creates structured artifacts with salience scores, consent metadata, and provenance — ready for governed retrieval and re-ingestion.

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AI Memory & Recall Systems

Building memory layers for AI assistants, agents, or platforms that need to retrieve emotional context across conversations over time.

Agent Memory Providers

Creating infrastructure for agent memory — long-term context storage, retrieval systems, or memory-as-a-service for AI applications.

Start Here_

For memory platforms, start with Observe. Instead of retrieving by recency or keyword, retrieve by emotional significance. Observe Records give your retrieval engine structured signals about what actually matters.

Your first API call

curl -X POST https://www.deepadata.com/api/v1/observe \
  -H "Authorization: Bearer dda_live_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "I finally understood why I kept avoiding that conversation...",
    "subject_id": "user-12345",
    "session_id": "memory-session-001"
  }'
Observe Quick Start2 minutes to first record

Then add Safety for interaction safety checks. Add Govern to create portable .ddna artifacts that travel with your users across platforms.

How It Works

Five steps to governed memory_

Interpret. Seal. Govern. Share. Verify.

1

Interpret

EDM.json — Extract what was significant

2

Seal

.ddna — Signed by DeepaData

3

Govern

Metadata — Compliance in-band

4

Share

VitaPass — Moves with the subject

5

Verify

API — Confirm authenticity

What breaks without governed memory_

Memory platforms ingest emotional context at scale. Without structure, you can't govern what you store.

Memory ingests emotional context

But what was significant? What consent applied? Without structure, you're storing data you can't govern.

Retrieval by recency, not salience

Embeddings find similarity. They don't know what mattered. High-salience moments are buried in noise.

No audit trail for training data

Emotional context feeds your models. When regulators ask where it came from and under what consent, you have vectors — not provenance.

Context can't travel with consent

Users share emotional content. When they leave or revoke access, their emotional context stays in your memory — unstructured and ungoverned.

Platform Capabilities

Built for memory at scale_

Batch Upload

Process historical content in bulk. Years of emotional context become governed artifacts.

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Extract API

96-field EDM artifacts with emotional salience. Structured for deterministic retrieval.

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Issue / Seal

W3C Data Integrity Proofs. Artifacts structured for re-ingestion with provenance.

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The risk of ungoverned memory_

Emotional context at scale creates regulatory exposure. Without artifacts, you can't prove what you stored or why.

Ungoverned emotional context at scale

Memory systems ingest millions of emotional moments. Without governance, each one is a liability — processed without structure, stored without consent proof.

Training data provenance unknown

If emotional context feeds your models, EU AI Act requires transparency. Where did it come from? What consent applied? Vectors don't answer that.

Retrieval misses what matters

Semantic similarity finds words, not significance. Without salience-weighted artifacts, high-intensity moments are indistinguishable from noise.

Why DeepaData for AI memory_

Governed ingestion. Structured artifacts. Deterministic retrieval. Memory infrastructure your stack can trust.

Batch Upload at Scale

Ingest historical emotional context in bulk. Process up to 1,000 records per batch. Create governed artifacts from content that predates your integration.

Structured EDM Artifacts

96-field artifacts with emotional salience scores. Retrieve by what mattered, not what's recent. Deterministic retrieval your memory stack can trust.

Sealed .ddna for Re-ingestion

Artifacts structured for memory and training. W3C Data Integrity Proofs ensure nothing was modified. Provenance from capture to retrieval.

Governed Context at the Data Layer

Consent, jurisdiction, and retention metadata travel with every artifact. Governance happens in the artifact, not in application logic.

Architecture

How DeepaData integrates_

A governance layer between your ingestion pipeline and your memory. Every emotional moment structured and auditable.

Your Application

Therapy / Journaling / AI App

DeepaData API

EDM.json.ddnaVitaPass

Sealed Records

.ddna artifacts with W3C Data Integrity Proofs

Compliance

EU AI Act

Litigation

Evidence

Insight

Context

Portability

GDPR

→ Auditors / Regulators verify on demand

What measurably improves_

Transform raw emotional context into governed artifacts. Retrieval by salience. Provenance for training. Consent at the data layer.

  • Batch ingestion — governed artifacts from historical data at scale
  • Deterministic retrieval by emotional salience, not just recency
  • Structured EDM artifacts ready for re-ingestion into memory
  • Provenance trail for every emotional moment ingested
  • Consent metadata travels with artifacts
  • Training data with auditable governance

The Retrieval Problem

Salience, not similarity_

Without DeepaData

  • Retrieval by embedding similarity — misses what mattered
  • High-intensity moments buried in semantic noise
  • No provenance for training data governance

With DeepaData

  • Retrieval by emotional salience — what mattered surfaces
  • 96-field EDM artifacts structured for deterministic recall
  • Sealed .ddna with provenance from capture to training

Ready for governed AI memory?

Enterprise pricing available. Volume discounts for batch processing. Dedicated support and custom SLAs.

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