Real-time AI for your Enterprise Data
Aura Intelligence gives models, agents and end users a shared foundation for asking questions, generating answers and building AI experiences grounded in the same live understanding of your business — so one agent’s action can become the next agent’s context.
Users ask questions in plain English, and Aura Analyst interprets the request, plans the query, generates SQL, validates it, executes it against live operational data and explains the results. Where it makes sense, it also returns charts and visualizations.
Business leaders can answer more of their own questions without waiting for dashboards or SQL requests, while engineering teams retain complete visibility into the generated queries and continue to govern how data is accessed.
With AI Functions, teams can call AI capabilities from SQL. That makes it possible to classify, summarize, score or enrich data without first exporting it to another system.
For example: Models give teams flexibility in how inference runs. You can call externally hosted models or run open-source models closer to your SingleStore environment when latency, control or data locality matter more.
Embeddings can be stored and searched as native vectors alongside the rows they describe. That means similarity search can run against current operational data instead of a separate vector database that only reflects the last sync.
Developers can connect tools like Cursor, Claude Code, VS Code, Claude Desktop, Windsurf, Gemini CLI and other supported clients to live SingleStore environments. Their coding assistants can inspect schema, run SQL and reason about real data without relying on pasted table definitions or stale exports.
For engineering teams, this is the practical difference between "AI that can write database code" and "AI that understands the database it is working with.
Authentication uses a browser-based OAuth flow for local development. Docker-based clients use API key configuration.
Self-service operational analytics
Let business teams ask questions against governed live data without waiting for every answer to become a dashboard.
AI agents with shared context
Developer copilots for live data
Real-time AI applications
Use fresh operational data, vectors, SQL and model calls to power personalization, fraud detection, recommendations, support automation, telemetry analysis and other workloads where stale context is not good enough.
Embedded intelligence inside products
Real-time
Live operational data, always ready for AI.
Secure + Governed
Enterprise security, governance, and access controls built in.
Open + Interoperable
Connect the models, tools, and platforms you already use.
Start building today
Your intelligent apps are about to get even better