One platform for the full life of a large enterprise application.

Analytical workloads run against Vertica, ClickHouse or BigQuery with native SQL generation — no generic dialect for the engine to translate, no third-party connector to maintain. Cubes, dimensions and measures are defined as Dictionary metadata; reports, KPI surfaces and interactive dashboards are composed inside the platform from the same authoring model that produces the operational application. Drill-down, drill-across, slice-and-dice, cross-filtering — the interactive analytics surface a business analyst expects: click a slice of a pie, every related chart, table and KPI on the dashboard updates against the same governed query. The capability set matches and in places exceeds what PowerBI, Tableau and Looker deliver. The differentiator is the deployment model : analytics runs on the customer's own infrastructure, behind the customer's own security perimeter, governed by the same row-level and column-level security that protects every OLTP transaction — at a fraction of the per-seat licence cost a SaaS BI stack would charge for the same audience.

Studio is the operator's cockpit for the running platform — an AWS-console parallel for an enterprise application estate. User and role administration with eight authentication mechanisms unified under one dispatcher, MFA policies per department, certificate keystores per user, instant offboarding that kills database, API and session access in one action. Service and resource control: cluster topology, database fleet, connection-pool tiers, replica routing, multi-tenant isolation enforced at the JDBC layer. AI governance: provider configuration, per-company and per-user budget caps, model-selection policy, live cost dashboards by user, department and model. Scheduled automation, integration credentials, certificate rotation. Audit trails — every login, every query, every transaction, every AI invocation — captured and queryable as SQL.

Eight authentication mechanisms unified under a single dynamic dispatcher: HTTP Basic, Digest, Form + 2FA (TOTP, Email OTP, SMS), JWT Bearer, OAuth 2.0, OpenID Connect, SAML 2.0, mTLS. Mix LDAP, SSO and certificates in one installation, per request. Audit is mandatory — every login, every REST call, every DML write, every AI invocation, every active session — captured and queryable as standard SQL. The Instance Database at jdbc:axional:memory:instance exposes JVM, JDBC, cache, statistics and machine schemas as SQL tables; SELECT * FROM jvm.memory replaces the external APM tier.

The substrate matters as much as the model. Because every form, screen, role, query and batch is a database record — not a file — AI agents author the same graph developers do, not a file tree they have to parse. Multi-provider chat across OpenAI, Anthropic, Ollama, Google Vertex, IBM Watson and Cohere — swap providers via configuration, not code. Built-in RAG over the customer's choice of vector store (Qdrant, Milvus, PostgreSQL pgvector or Redis). Natural-language agents emit XDBL — the platform's XSD-described query grammar, not raw SQL ; agents don't learn seven SQL dialects, they write one XDBL and the compiler emits the rest with row- and column-level security injected at the same step. The agent works one abstraction level above SQL ; the platform handles engine, dialect, isolation and security. A native MCP server so external assistants drive the platform without bypassing it.