One source of truth.
Every query shape.
One endpoint. Five query patterns. One engine. Rows, vector
embeddings, full-text postings and graph edges commit together in one
atomic write - every write instantly visible to SQL, vector, graph,
full-text and the natural-language /ask alike.
SELECT symbol, close FROM prices WHERE date > $1 01 topk(embedding, :q, 10) WHERE sector = 'tech' 02 MATCH (a)-[:REFERRED]->(b) RETURN b 03 search('quarterly earnings beat') 04 /ask "top movers this week" 05 
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Teams building AI-native products on OriginChain.
Stop stitching four systems for one AI feature.
Most modern AI stacks fan a single user action across four databases - a relational store, a vector index, a search engine, and a graph. Each has its own bearer, its own SDK, its own billing line, and its own way of being out of sync at 3 am. OriginChain replaces all four with one managed database. One write lands every shape atomically.
Retire four contracts. Onboard one. Stop coordinating four maintenance windows for a single feature release.
The row, its embedding, its full-text postings, and its graph edges commit together in one atomic write. No reconciliation jobs. No "the search index is 8 hours behind" disclaimers.
One SDK, one auth model, one observability surface. New AI features land without a Debezium pipeline + four retry queues + a 3 am reconciliation cron.
One source of truth. Every query shape.
A single write commits rows, embeddings, postings and graph edges atomically — every shape queries the same consistent corpus.
no "index is 8 hours behind"
Latency you can budget against on a managed database.
Concrete p99 numbers, not averages - the ceiling your app can plan around for SLAs, agent loops, and user-facing reads on a managed database.
End-to-end p99 in-region for typed queries against your managed database.
After the first ask of a shape, the plan is durably cached and every repeat skips compile.
recall@10 = 0.979 at 100M on BIGANN (real data, published ground truth) — DiskANN-class leader-band accuracy, on a single box.
First ask of a brand-new shape from another continent - including the round trip.
p99 measured at the API edge · in-region unless noted · vector topk is the 100M-vector benchmark — IVF-PQ on BIGANN (real data, published ground truth), recall@10 = 0.979 at p99 333 ms on a single box · the HNSW path serves smaller corpora at single-digit-millisecond p99
Ship faster on a database built for AI.
OriginChain is the AI-native database for teams that need SQL, vector, full-text, graph, and natural-language queries on one managed endpoint. Single tenant, region-isolated, fully hosted - provision in under two minutes, query in milliseconds.
Notes from building a one-engine database.
How a graph database works — and when it beats joins
Relational databases store relationships as data you re-derive at query time with joins. Graph databases store them as structure you walk. That single difference decides which questions stay fast as data grows — and it's why fraud rings, recommendations, and agent memory are graph problems.
How vector search actually works — from embeddings to HNSW
Embeddings turn meaning into geometry; vector search turns geometry back into answers. A plain-language walk through similarity metrics, why brute force dies at scale, how HNSW graphs make approximate search fast, and what quantization actually trades away.
The Token Economy Has Arrived — And Your AI Bill Is About to Meet Your CFO
AI token usage has crossed from a technical detail to a strategic cost center — and "token maxing" is now one of the largest hidden line items at scale. OriginChain Contextual Intelligence (OCCI) cuts token consumption 30–50% while improving outcomes, by making context — not the model — the lever.
Built by a deep-tech team engineering the data foundation for AI.
Ninety seconds to an endpoint. No stack to wire up.
Pick a region, choose your configuration, and we provision a dedicated single-tenant instance in roughly ninety seconds. The first query you send is the first query we'll show you how to write - in English.
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