One database engine that replaces your entire data stack — relational, graph, vector, time-series, document, geospatial, and more, with cross-modality queries and zero-rewrite migration.
One database engine that replaces your entire data stack — relational, graph, vector, time-series, document, geospatial, and more, with cross-modality queries and zero-rewrite migration.
Relational, graph, vector, time-series, document, geospatial, columnar, and more — one engine, one query language, one transaction, one audit trail. No ETL between databases.
Speaks PostgreSQL, MongoDB, Redis, Cypher, SPARQL, Cassandra CQL, Kafka, Elasticsearch, ClickHouse, and 50+ more wire protocols. Your existing tools and apps connect without a rewrite.
Join a graph traversal to a vector search to a time-series range query in one SQL statement — with single-phase commit, unified WAL, and cross-modal foreign key enforcement.
Every page carries structural hashes. Corruption is detectable in O(log n) and localizable in O(k log n). Server-grade data integrity on consumer hardware — no ECC required.
Not bitemporal — N-dimensional temporal. System time, valid time, plus user-defined dimensions like authorization time and settlement time. Every event traced through its full temporal context.
Manifolds, simplicial complexes, persistent homology, Betti numbers, Lie groups, fiber bundles — as first-class database objects with DDL, DML, and SQL query operators. No other database does this.
Most organizations run a Frankenstein data stack — PostgreSQL for relational data, Neo4j for graphs, Pinecone for vectors, InfluxDB for time-series, MongoDB for documents, Elasticsearch for search, Redis for cache, and a warehouse on top. Each has its own query language, its own migration path, its own scaling story, and its own bill. When you need to join a graph traversal to a vector search to a time-series range query, you build an ETL pipeline — and hope the data does not go stale between syncs.
GEOM-db is one database engine that handles 20+ data modalities natively: relational, graph, vector, temporal-causal, key-value, time-series, document, geospatial, columnar, wide-column, inverted index, event stream, and more. Cross-modality queries join a graph edge to a relational row to a vector embedding in a single SQL statement — with one transaction, one WAL, one MVCC snapshot, and one audit trail. And it speaks your existing database's wire protocol — PostgreSQL, MongoDB, Redis, Cypher, SPARQL, Cassandra CQL, Kafka — so your existing tools and applications connect without a rewrite.
Full SQL with ACID transactions, MVCC, and Merkle integrity — not a SQL compatibility layer over a NoSQL engine. Standard B+ tree storage with columnar scans for analytical workloads. Foreign keys, constraints, triggers, stored procedures. PostgreSQL wire protocol compatible — your existing SQL tools, ORMs, and BI dashboards connect without modification.
PostgreSQL with the pgvector extension gives you vector search. But it does not give you HNSW with quantization, cross-modal foreign keys, or graph traversal in the same transaction. GEOM-db does — natively.
TimescaleDB gives you time-series in PostgreSQL. But it does not give you Kafka wire protocol, cross-modal joins to your graph data, or Merkle integrity on every page. GEOM-db does — natively.
Stack three extensions and you have geospatial, graph, and relational. But each extension has its own indexing, its own query path, and its own failure mode. GEOM-db has one engine, one transaction, one snapshot.
Event streaming with Kafka wire protocol compatibility — topics, partitions, offsets, consumer groups, schema registry. Kafka, Pulsar, NATS JetStream, and Google PubSub wire compatible. When a stream event needs to update a relational record and trigger a graph traversal, it happens in one transaction — not across three systems with eventual consistency.
Wide-column storage with keyspaces, column families, cell timestamps, TTL, and tombstones. Cassandra CQL, HBase, Bigtable, and DynamoDB wire compatible. Your wide-column data and your relational data share the same transaction boundary — no separate Cassandra cluster.
GEOM-db speaks the PostgreSQL wire protocol. Your psql client, your SQLAlchemy ORM, your psycopg2 driver, your pgAdmin, your DBeaver connection — all work without modification. Your SQL queries work. Your stored procedures work (via Python/Julia server-side programmability). Your COPY commands work.
What you gain: Merkle integrity on every page, polytemporal tracking, cross-modality queries to graph and vector data, and HTAP without a separate warehouse. What you keep: every tool, every query, every connection string. The migration is a data export and import — not an application rewrite.
Find customers similar to Customer X who are connected to Company Y in the graph. In a traditional stack, this is a vector search in Pinecone, a graph traversal in Neo4j, and a join in application code — with three network round trips and no transactional guarantee.
In GEOM-db, this is one SQL statement. The vector search and the graph traversal execute as a unified plan — the HNSW index and the graph adjacency list are in the same engine, the same memory space, the same transaction. The result is exact, not eventually consistent.
Every modality — relational, graph, vector, time-series, document, geospatial, key-value, columnar, wide-column, event stream, financial, inverted index, blockchain, IoT, multimedia, ontology, RDF, cache — is an independent component. Each declares a ModalityStorageProfile: storage class (page-backed, segment-backed, SSTable-backed, mmap-backed, object-store-backed), WAL class, MVCC class, backup inclusion, encryption domain, spill policy, and exact-owner recheck requirements.
This is not a monolith with plugins. Each modality is a first-class engine citizen with its own DDL, DML, query operators, index types, and cost model entries. The optimizer composes execution plans across modalities — a graph traversal feeds its output into a vector similarity search, which feeds into a time-series range filter, all in one plan, one transaction, one commit. The composition is the architecture.
PostgreSQL is free. Neo4j Enterprise is not. Pinecone is not. Snowflake is not. Each database in your stack has its own pricing model, its own compute charges, and its own data transfer fees. GEOM-db is one license, one engine, one bill.
Each database needs a backup strategy, a monitoring dashboard, an on-call rotation, and an upgrade cycle. Six databases means six of each. GEOM-db means one of each — because it is one engine.
Every pair of databases needs a data synchronization pipeline. Six databases means fifteen pipelines. Each pipeline has its own failure mode, its own latency, and its own stale data risk. GEOM-db has zero pipelines — because the data is already in one engine.
GEOM-db speaks your existing database's wire protocol. Your tools, your queries, your ORMs connect without modification. The migration is a data import — not an application rewrite. Start with one modality, add more when ready.
Six bridging layers connect the relational SQL surface to the advanced modality world. B1: bootstrap advanced objects from SQL queries — CREATE SIMPLICIAL_COMPLEX FROM (SELECT ...), CREATE MANIFOLD FROM (SELECT ...), build HDC indexes from existing data. B2: augment tables with advanced column types — add HYPERVECTOR, MANIFOLD_POINT, PERSISTENCE_DIAGRAM columns to existing tables. B3: hybrid DML — INSERT with vector and geometry constructors, UPDATE with HDC operations, MERGE for streaming data.
B4: unified query language — hybrid predicates mixing relational, geometric, and HDC in one WHERE clause. B5: backend implementation — augmented system catalog, hybrid query planner with geometric and HDC access paths, wire protocol extensions. B6: federated materialized views with advanced indexes on foreign data. Migration is incremental: start with B1 on your existing data, add B2 columns when ready, use B4 queries when you need cross-modal joins.
One engine instead of six. PostgreSQL, MongoDB, Redis, and 60+ more wire protocols mean your existing tools connect without modification. Merkle integrity on every page. Snapshots in milliseconds. No ECC required.
Cross-modality queries in one SQL statement — join a graph traversal to a vector search to a time-series range query with one transaction and one commit. No ETL pipeline. No eventual consistency. No stale dashboards.
Vector similarity search with HNSW and quantization. Hyperdimensional computing with analogical reasoning in SQL. Your embeddings and your business data in one engine — no separate vector database to operate.
One bill, one deployment, one upgrade cycle. Multi-tenant SaaS with per-tenant isolation and billing. Air-gapped for classified environments. Edge-constrained for industrial sites. Same binary, every deployment.
Every database promises durability. Most deliver it with write-ahead logs and checksums. But when a page is corrupted by a hardware fault, a silent bit flip, or a storage controller bug, the checksum may not catch it — especially if the corruption happens in memory before the checksum is computed. GEOM-db uses a Merkle B+ tree as the foundation of its storage — not as a feature, but as the core data structure.
Every page carries a structural hash. Every parent node carries the hash of its children. Corruption is detectable in O(log n) — a single read verifies the entire path from root to leaf. And it works on consumer hardware — no ECC RAM required. The engine detects whether ECC is present and adjusts verification frequency automatically.
Most databases are not temporal at all — the current state is the only state. Some are bitemporal — tracking when data was valid in the real world and when the database learned about it. GEOM-db is polytemporal — it supports N user-defined temporal dimensions.
For a financial transaction, you might track system time (when the database recorded it), valid time (when the transaction occurred), authorization time (when it was approved), and settlement time (when it cleared). For a supply chain event, you might track system time, valid time, departure time, and arrival time. Every dimension is queryable independently — and the temporal context is stored efficiently with a context-aware pattern that avoids per-row redundancy.
Create manifolds with CREATE MANIFOLD — Euclidean, Riemannian, hyperbolic, spherical, Minkowski, or custom. Compute geodesic distances, curvature, parallel transport, exponential and logarithmic maps. Query with spatial operators like <-> (geodesic distance) and @> (containment).
3D asset ingestion — glTF, STL, OBJ, FBX, USD, STEP, IGES, PLY, IFC — automatically decomposed into queryable geometry primitives. A CAD model is a manifold with vertices and faces that can be queried, measured, and annotated. No separate CAD file server.
Run GEOM-db on your own servers, your own Kubernetes cluster, or your own bare metal. Container-native with multi-arch builds (AMD64, ARM64, ARM v7). Helm charts with CRDs for cluster, backup, restore, and tenant management. Hardware detection at startup — CPU features, GPU, NUMA, NVMe, RDMA — with automatic tuning. Your data never leaves your network.
Row-level security with spatial containment predicates — a user can only see rows within their geographic region. Column-level security with dynamic data masking. JWT, OAuth2, OIDC, API keys, mTLS client certificates. Per-tenant, per-role, per-column access control.
Leakproof operator classification — every predicate, operator, and function is classified as barrier-safe or barrier-unsafe. Non-leakproof functions cannot cross security barriers. Denied and missing targets are indistinguishable — existence is not leaked.
A tiered storage engine — hot data in a Merkle B+ tree in memory, warm data on disk in an LSM tree, cold data in object storage. Pages adapt to your workload — small for transactional, large for analytical scans. The buffer pool is container-aware and auto-tunes to your available memory, so running in Kubernetes does not mean fighting over RAM.
Every page carries a structural hash — a Merkle tree. Corruption is detected on read, not after a customer notices a wrong number. The engine detects whether your hardware has ECC memory and adjusts verification frequency accordingly — aggressive on consumer hardware, relaxed on server hardware. The overhead is negligible — a fraction of a percent of CPU.
Snapshots are instant — no copy-on-write, no page table manipulation, no fork. Snapshot a terabyte database in milliseconds. Works in containers, on Windows, on bare metal — because the snapshot is a version marker, not a process fork.
Consensus-based replication means your data is safely stored on multiple nodes before a transaction is committed. No external orchestrators, no split-brain, no manual failover scripts. When a primary node fails, the cluster elects a new one automatically — your application reconnects and keeps running.
Replication modes let you trade latency for durability per workload. Synchronous for financial data where every write must be confirmed by a majority. Asynchronous for analytics where throughput matters more. Rolling upgrades with zero downtime — patch one node at a time while the cluster stays live.
Per-tenant point-in-time recovery means you can restore a single customer's data without taking the database down. Cross-region backup replication means your disaster recovery site is always current. Encryption at rest with your key manager — AWS KMS, HashiCorp Vault, or your own HSM.
13 language SDKs: Rust, Python, TypeScript, Java, Go, C#, C++, Ruby, PHP, Elixir, Swift, Kotlin, Scala. Async-first with sync convenience. NumPy and Pandas integration in Python, serde in Rust, Float32Array in TypeScript. Schema-aware code generation. OTel trace propagation. Structured error types per wire protocol.
Every operation is available through SQL, CLI, SDK, REST, and gRPC — with enforced parity across all surfaces. The same operation produces the same result regardless of which interface you use.
Every data model is a first-class modality — not a plugin, not a compatibility layer. All share one transaction boundary. Relational, document, graph, vector, time-series, event stream, geospatial, 3D, key-value, cache, wide-column, columnar, financial, and blockchain — all native.
Cross-modality queries join a SQL table to a graph traversal to a vector search in one statement. No ETL. No pipeline. No stale data.
Your existing tools connect without modification. PostgreSQL, MongoDB, Redis, Cypher, SPARQL, CQL, Kafka, Elasticsearch, ClickHouse, and Snowflake — all speak their native wire protocols to GEOM-db.
No driver changes. No query rewrites. Point your existing application at GEOM-db and it just works.
Every pair of databases needs an ETL pipeline. More databases means more pipelines, more stale data, more failure modes. One GEOM-db engine replaces them all.
When your data lives in one engine, cross-modality queries are a single statement. When it lives in six, every join is a pipeline that breaks.
The data infrastructure industry sells you pieces. A relational database for your business data. A graph database for your relationships. A vector database for your embeddings. A time-series database for your metrics. A search engine for your text. A cache for your hot data. A warehouse for your analytics. Each has its own query language, its own scaling story, its own failure mode, and its own bill. And between each pair, you build an ETL pipeline — a fragile, eventually consistent data synchronization job that breaks when the schema changes and goes stale when the pipeline lags.
GEOM-db takes a different position. The data was never separate — your tools made it separate. A customer record, the graph of their relationships, the vector of their preferences, and the time-series of their interactions are all data about the same entity. They belong in the same engine, the same transaction, the same query. When you join a graph traversal to a vector search to a time-series range query, the result is exact — not eventually consistent, not stale, not a dashboard refresh away from reality.
That is what GEOM-db is. Not another database to add to your stack. A replacement for the stack itself.
Operator relevance: A platform owner can replace database sprawl with one engine while keeping every existing tool, client, and query — the migration is data, not application rewrite.