Your data is fully isolated — every customer gets dedicated infrastructure with complete separation at the database, storage, and compute level. No shared tables, no row-level filtering. Your data never touches another customer's.

Access control is enforced at the data layer, not just the application layer. You control who can access which entities, which properties, and which operations — down to the column level. This applies to both people and AI. Groups and roles let you define exactly what each team member or service can see and do.

Every action is logged in a full audit trail — who accessed what, when, and what changed. API keys are scoped with specific permissions and rate limits, so external integrations only get the access they need.

The platform runs on Google Cloud with encryption at rest and in transit, automated backups with point-in-time recovery, and network-level protections including WAF and DDoS mitigation.

Agimus connects to a wide variety of databases, ERPs and CRMs, APIs, data lakehouses, cloud storage, and more — with new connectors being added regularly.

Connections support SSH tunneling and SSL/TLS for secure access to on-premise systems. Sync schedules are fully configurable, and schema changes are detected and handled automatically.

Catalogs document data. BI tools chart it. Neither lets you build on it.

Agimus goes from raw, scattered data all the way to production applications. Connect your sources, structure them into a semantic ontology that captures how your business actually operates, and then build on that foundation with a Python SDK, REST API, and AI tools that make the LLM measurably more accurate and reliable. Your team ships real products on real data.

No. Agimus is designed for teams as small as one or two technical people. A VP of IT with a data engineer, or even a single developer, is enough to define an ontology and start building.

The Python SDK uses intuitive Django-style syntax. The AI coding tools (MCP integration with Claude Code and Cursor) make the LLM measurably more accurate and reliable, so developers write correct code faster. The ontology evolves without rebuilding from scratch.

No. Agimus sits on top of your existing systems — it connects to them. Your ERPs, CRMs, databases, and warehouses stay exactly where they are. Agimus is the layer that makes all of them work together by providing one structured, queryable view across everything.

First ontology and a working application can be up in weeks, not months. Connect your data sources, define the entities and relationships, and start building. One of our clients went from fragmented data across dozens of systems to a full production supply chain application in that timeframe.

This is not a 12-month implementation project.

The ontology makes your company's data efficiently accessible to AI. Instead of dumping raw data or static context files into an LLM, the ontology provides structured, relationship-aware data with access control. AI doesn't guess — it operates on a real data foundation.

Agimus also provides an MCP server that connects directly to AI coding tools like Claude Code and Cursor. When a developer is building on Agimus, the AI already understands the ontology: what entities exist, their properties, how they relate, and how to query them through the SDK.