Solutions · AI for Databases
Autonomous Database Operations
Tuning, capacity, patching, backup and incident response run as a continuous agentic practice inside change control, so scarce database expertise goes to the hard problems.
The problem
What changes when this goes wrong?
Scarce, expensive expertise on routine work
Performance degradation found by users
Patch and capacity debt accumulating
The Agents
How They Act in Real-Time Environments?
AGENT 01
Performance agent
Diagnoses degradation and proposes tuning with a simulated effect.
AGENT 02
Capacity agent
Forecasts growth and flags headroom before it becomes an incident.
AGENT 03
Maintenance agent
Executes patching and backup verification inside change windows.
Outcomes
We Measure
Manage complex, high volumes of data efficiently with the advanced indexing methods and query techniques we bring to your databases.
- Query performance improvement
- Incident volume
- Patch currency
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Bring AI Prowess to Your Database
AI-powered databases mark a pivotal shift in running mission-critical AI/ML workloads at enterprise level. Talk with our engineers to make your database intelligent and powerful.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT