one atomic substrate
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.
A member of the NVIDIA Inception program and the AI Council of India (IAMAI). Learn more →
Teams building AI-native products on OriginChain.
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.
Relational rows, vector search, graph traversal, full-text and the
natural-language /ask — plus the auth,
replication and durability to run them in production. One endpoint,
one SDK, one bill.
Rows, vector embeddings, full-text postings and graph edges commit together in one write-ahead log — atomic by construction, no reconciliation.
Users, roles, row-level security and column masking — built into the same write.
Correlated EXISTS, aggregates and CASE — real SQL over the substrate.
4 distance metrics × 4 index variants. HNSW with metadata filters.
9 algorithms with per-hop EXPLAIN — BFS, paths, weighted Dijkstra.
BM25 ranking, phrase queries and 18-language stemming.
Ask in plain language; OriginChain routes it to the right shape.
Sharded, replicated, consensus-backed. One write, quorum-durable.
Use one shape or all five. One substrate. One atomic write.
A single write commits rows, embeddings, postings and graph edges atomically — every shape queries the same consistent corpus.
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.
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.
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.
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.
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.
