Your data stack is a Frankenstein — PostgreSQL, Neo4j, Pinecone, InfluxDB, MongoDB, Elasticsearch, Redis, and a warehouse on top. GEOM-db replaces them all with one engine. Lynceus makes the data observable.
Six databases, six query languages, six failure modes
Most organizations run 6-10 different databases — PostgreSQL for relational, Neo4j for graphs, Pinecone for vectors, InfluxDB for time-series, MongoDB for documents, Elasticsearch for search, Redis for cache, and a data warehouse on top. Each has its own query language, its own scaling story, its own backup strategy, 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. Every pair of databases needs its own pipeline. More databases means more pipelines, more stale data, more failure modes.
PostgreSQL, Neo4j, Pinecone, InfluxDB, MongoDB, ES, Redis
Cross-modality queries, no pipelines
Your existing tools connect without modification
p99, cross-modality joins
GEOM-db handles 20+ data modalities natively — relational, document, graph, vector, temporal-causal, key-value, time-series, geospatial, columnar, wide-column, inverted index, event stream, financial, and blockchain. 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.
Your existing tools connect without modification — no driver changes, no query rewrites.
GEOM-db replaces the database stack. Lynceus makes the data observable. Prometheus governs AI access to it.
Replace 6-10 databases with one engine. 20+ modalities, 65+ wire protocols, cross-modality queries in one SQL statement. Zero ETL. Zero pipeline maintenance. Zero stale data.
Real-time observability across your data infrastructure — query latency, replication health, cache hit rates, and anomaly detection. 99% of queries under 2ms, full distribution visible.
Every AI model that touches your data is governed — model version, input context, output, and decision boundary, all cryptographically signed. Per-tenant isolation at the cache-salt level.
Source control for data schemas, migration management, and CI/CD for database changes. One audit trail across all data infrastructure changes — who changed what, when, and why.
The hardest part of data modernization is not choosing the new database — it is migrating without breaking the applications that depend on the old one. GEOM-db speaks the same wire protocol as your existing database, which means your applications connect without modification.
No driver changes. No query rewrites. Point your existing application at GEOM-db and it just works. Migration is a cutover, not a project.
From storage to governance — one platform for your entire data lifecycle.
Multi-modality database — 20+ data models, 65+ wire protocols, cross-modality queries, Merkle integrity, polytemporal audit trails.
Observability — metrics, logs, traces, profiles, events, topology, security, and business KPIs. One platform, one query language, one timeline.
Governed AI inference — model lineage, per-tenant isolation, token accounting, causal audit trails. Deploy AI that your compliance team can sign off on.
Data DevOps — schema versioning, migration CI/CD, and one audit trail across all data infrastructure changes.
Architecture to delivery
Map your operating constraints with our solutions team and define the right delivery path.