AI — intelligent oversight
AI integration for databases
AI is not a marketing sticker here — it is a layer that continuously reads your database's metrics, logs and audit trail and turns them into early warnings and readable answers.
Database health monitoring with AI
Classic monitoring watches thresholds: "if the value exceeds X, send an alert." The AI layer learns your database's normal behaviour — and reports deviations from it even when no threshold was crossed. That is exactly where trouble starts.
- Anomaly detection on performance metrics (AWR/ASH)
- Event correlation — what happened together and what caused it
- Predictive capacity planning (data growth, archive logs)
- Continuous alert-log analysis instead of manual reading
- Fewer false alarms — alerts with context and a recommendation
Security oversight
A production database's audit trail is far too large for anyone to read manually — and attackers count on exactly that. AI reads it for us. Every day. All of it.
- Unified Audit trail analysis — who, when, from where, to what
- Detection of unusual access (time, origin, data volume)
- Tracking privilege changes and new accounts
- Warnings about suspicious patterns before they become incidents
- A regular security report in plain language
Reports built around your questions
Database health should not be a secret only a DBA can decode. Our reports are written for management to understand — and one-off questions don't have to wait until month-end.
Scheduled reports
Weekly or monthly summaries of health, performance, capacity and security — automatic, in an agreed structure.
Answers on demand
"How long will our storage last?" "Why was invoicing slow yesterday?" — answers from real data, not impressions.
Plain language
Technical findings translated into decisions: what is happening, what it means, what we recommend.
Your metrics
Reports built around what matters to you — SLAs, application KPIs, costs.
AI features inside Oracle 26ai
The latest Oracle Database generation (26ai) ships with AI built into the core. We help you deploy and use these features — safely and deliberately, not as an experiment on production.
- AI Vector Search — semantic search over your own data
- Select AI — querying the database in natural language
- In-database machine learning on production data
- RAG architectures: your data as a knowledge base for AI applications
- An honest assessment of which AI features make sense for you — and which don't
The AI we work with
We don't build on anonymous "artificial intelligence" — we work with specific models and agents whose behaviour and limits we know from daily practice.
Anthropic Claude
We use Claude models to analyse operational data — AWR, logs, audit trails — and for natural-language reports and recommendations. Precise, careful, built for environments where correctness matters.
NOUS RESEARCHHermes Agent
Hermes agents run autonomous routine checks: they walk the metrics, gather evidence and prepare a summary — the DBA steps in only where a human decision is needed.
Curious what AI oversight would say about your database?
We start with a pilot on a single database — you see results within weeks.