One AI-native engineering workflow
Your AI can write code, explain files and accelerate delivery. Opsphere gives it the operational context it needs to understand your infrastructure, investigate production issues and support real engineering decisions.
THE OPERATIONAL PAIN
AI can generate code. It still lacks production context.
AI coding tools are changing how teams build software, but most of them remain disconnected from the systems that actually run in production. Engineers still need to jump between Cursor, cloud consoles, logs, deployments and observability tools to understand what is happening.
"Cursor helps us move faster, but when production breaks, we still need five tools open to understand what actually happened."
— Senior Full-Stack Engineer, AI-native SaaS Team
AI does not know your infrastructure
Your assistant may understand the codebase, but it usually cannot see deployments, Kubernetes state, cloud resources or incident signals.
Engineers still switch tools
The workflow is faster until something breaks. Then engineers still move between dashboards, terminals, logs and deployment systems.
Production investigation remains manual
AI can suggest possibilities, but without live operational context it cannot reliably explain what is happening in your environment.
HOW OPSPHERE SOLVES IT
Operational intelligence inside AI-assisted engineering
Opsphere connects your infrastructure, deployments, observability and runtime signals into the AI workflow, so engineering teams can investigate and operate production systems with real context.
Native Cursor Workflow
Ask operational questions directly from Cursor and get answers grounded in your real stack, not generic assumptions.
Infrastructure-Aware AI
Opsphere gives AI access to signals from AWS, Kubernetes, Vercel, Terraform, GitHub, Datadog and more.
Production Investigation
Move from code-level reasoning to live operational analysis across services, deployments and environments.
Controlled AI Integration
Use your own OpenAI or Mistral keys, or choose a managed AI layer with controlled token allocation.
BEFORE / AFTER OPSPHERE
- Code-only AI context
- Manual production checks
- Multiple dashboards open
- Generic AI suggestions
- Disconnected operations
- Low production confidence
- Infrastructure-aware context
- AI-assisted investigation
- One operational layer
- Stack-specific answers
- Workflow-native operations
- Higher operational confidence
SCENARIO WALKTHROUGH
Ask Cursor. Get infrastructure answers.
Here's how an AI-native engineering team uses Opsphere to investigate a production issue without leaving the development workflow.
Scenario: Checkout failing in production
Thursday 17:42 UTC — checkout errors increase after a frontend release and backend deployment
- 17:42
Engineer asks from Cursor
The developer asks why checkout is failing in production directly from the development workflow.
💬 No dashboard switching
- 17:42
Opsphere gathers operational context
Signals from Vercel, Kubernetes, AWS, Datadog and GitHub are correlated into a single investigation context.
🔎 Full-stack context assembled
- 17:43
Probable root cause identified
Opsphere links the issue to a recent deployment, affected services and a downstream API latency spike.
⚡ Stack-specific answer generated
- 17:49
Engineer acts with confidence
Rollback and verification steps are provided inside the workflow, with incident context ready for follow-up.
✅ From question to action in minutes
READY?
Turn AI coding into AI-assisted operations.
Give your engineering team AI workflows that understand code, infrastructure and production reality.