Text embeddings understand words.
They don't understand consequences.
Our JSON embeddings cut search errors in half.
{
"id": "tx_881",
"amount": 500.00,
"currency": "USD",
"status": "success"
}
{
"id": "tx_992",
"amount": 500.00,
"currency": "JPY", // 100x Value Difference
"status": "success"
}
* Treats currencies as synonyms. Fails to detect value anomaly.
* Recognizes "JPY" is operationally distant from "USD".
We don't just embed text. Our engine analyzes the topology of your schema to generate high-fidelity, structure-aware vectors that standard models cannot produce.
You send standard JSON events. No normalization, no flattening, no prompt engineering required.
Our proprietary model projects your data into a 1,552-dimensional manifold. It enforces geometric separation between operationally distinct values (like currencies or status codes) that semantic models treat as synonyms.
You receive a standard float array compatible with Pinecone, Weaviate, or pgvector. Drop it into your existing stack to instantly upgrade search relevance and anomaly detection.
When the difference between "USD" and "JPY" isn't just semantic—it's operational.
Build similarity search that understands currency conversions and merchant categories as interconnected signals—not just string matches.
Embed audit events for semantic search. Find policy violations that rule engines miss because they cross field boundaries.
Embed telemetry payloads where unit changes (°C vs °F) have operational consequences that text models ignore.
FHIR and HL7 messages where dosage units (mg vs mcg) and measurement contexts require structure-aware understanding.
Cluster and search structured logs where severity levels and error codes have hierarchical relationships beyond string similarity.
Intelligent transaction matching that understands payment rails and settlement windows as first-class features.
Stop paying for storage. Pay for intelligence.
Structure-aware embeddings at scale.
Per 1 Event/Sec unit.
~$9.60 per million scored events.