Semantic search finds what's similar. EDM encodes what mattered — 57 affective fields structured at capture time, before retrieval ever happens.
“Retrieval quality — your system surfaces what was recent, not what mattered. The moment that actually answers the query has different words entirely.”
“Context relevance — your AI knows what the user said. It doesn't know what it meant to them, how significant it was, or whether it shaped who they are.”
“Memory coherence — a crisis and a casual mention are stored the same way. Without a significance signal, every moment is equal.”
“Personalisation depth — context resets or stays shallow. Narrative arcs, identity threads, and emotional weight are lost in inference the moment the session ends.”
One API call. Raw text in. Structured emotional record out — retrievable by what mattered, not just what was said.
Raw input
EDM artifact
transformation · pride · enduring · continuity · optimism — none of these appeared in the source text. The artifact found them in the meaning.
Recall · Retrieval · Agent memory
Your memory stack retrieves by recency or similarity. EDM artifacts give it a third signal — meaning. Retrieve what mattered, not just what was recent.
Learn MoreJournaling · Companion AI · Legacy platforms
Meaningful moments preserved before they're lost in inference. What mattered, structured and signed.
Learn MoreTherapy · Coaching · Telehealth
Narrative arcs and affective themes that build over months — not just between sessions. Know where this person is in their story, not just what they said last week.
Learn MoreHR Tech · Wellbeing · Enterprise AI
Interpret, don't infer. EU AI Act compliant by architecture — not by policy.
Learn MoreBring your retrieval stack. EDM artifacts enrich it — whichever paradigm you use.
Other systems score relevance at retrieval time. EDM encodes significance at capture time — so every retrieval paradigm gets a richer signal to work with.
Embedding-based retrieval
Retrieve by resonance — not recency or keyword match
Temporal knowledge graph
Traverse entity relationships and temporal edges as a network of meaning
Agentic reasoning
Reason over who this person is — not just what they said
Episodic memory
Significance that strengthens, decays, or re-emerges — encoded in the episodic record, not computed at query time
EDM captures what most formats miss: not just what was said, but what it meant — as expressed context, not inferred state. It is non-inferential, governance-first, and portable. Published on Zenodo with a DOI and MIT licensed. Implement it yourself, or use our hosted infrastructure.
The standard is open. Build on it freely, self-seal locally, integrate into any memory stack — no DeepaData dependency required. Come to DeepaData when you want a registry entry, a VitaPass address for your user, or a certified artifact that any third party can verify.
Start with 24 fields. Scale to 96. Three profiles for every use case.
EDM is to affective context what JSON is to structured data. DeepaData is a commercial implementation — the standard belongs to everyone.
Each record carries a cryptographic proof (eddsa-jcs-2022). Verify any artifact independently.
EDM v0.7 is published on Zenodo with DOI 10.5281/zenodo.19211903 — citable and permanent.
90% enum alignment. 77% semantic match across 3 LLM providers.
We do not train models on your emotional data. Artifacts are processed, sealed, and returned.
MAU-based pricing for apps. Per-event for platforms. Self-seal always free.
Free
$0
100 MAU · 1,000 calls
Growth
$0.05/MAU/month
Up to 10,000 MAU · max $500/mo
ScaleRecommended
$0.04/MAU/month
Up to 100,000 MAU · max $4,000/mo
1,000 extractions free. No credit card required. Or work with us as a design partner and help shape what gets built next.
Get an API key and start in minutes. 100 monthly active users, 1,000 extractions included.
+ REQUEST API KEYBuilding something where emotional context matters? We're working with a small group of teams to refine the platform. Direct access, priority support, and roadmap input.
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