# SixDegree

> Live system intelligence for your entire stack. SixDegree maps your code, infrastructure, teams, and deployments in real-time so engineers can ask any question, understand any system, and take action-without hunting through Slack, wikis, and dashboards.

## Pages

- [Home](https://sixdegree.ai): Overview of SixDegree, the live system intelligence platform for engineering teams
- [Platform](https://sixdegree.ai/platform): How SixDegree works-connecting your stack, enabling natural language queries, and taking action from a single interface
- [Developers](https://sixdegree.ai/developers): How developers use SixDegree to ship faster, understand any codebase, find owners, and know what breaks before deploying
- [Operators](https://sixdegree.ai/operators): How operators use SixDegree to resolve incidents 70% faster with instant context, blast radius analysis, and on-call routing
- [Product Teams](https://sixdegree.ai/product): How product teams use SixDegree to ship with confidence-blast radius visibility, feature delivery tracking, and data-driven prioritization
- [Business](https://sixdegree.ai/business): Business impact and ROI of SixDegree for engineering organizations
- [Blog](https://sixdegree.ai/blog): Insights on AI, infrastructure, engineering systems, and knowledge management
- [Manifesto](https://sixdegree.ai/manifesto): The SixDegree manifesto-why we're building live system intelligence

## Key Capabilities

- **Unified Search**: Natural language queries across GitHub, Jira, Slack, AWS, Kubernetes, PagerDuty, and more-from a single interface
- **Dependency Intelligence**: Understand service dependencies, blast radius, and team ownership before deploying changes
- **Incident Response**: Surface affected services, recent changes, and on-call owners during outages. Resolve incidents 70% faster
- **Blast Radius Analysis**: See every service, team, and customer affected by a change before you ship
- **Feature Delivery Tracking**: Track features from customer request → code → deployment → adoption in real-time
- **Accelerated Onboarding**: Reduce ramp time for new engineers from weeks to days with instant system context
- **Living Documentation**: Auto-generated, always-current docs that reflect your actual infrastructure state
- **Institutional Knowledge Capture**: Surface Slack discussions, design decisions, and tribal knowledge linked directly to code
- **LLM Agnostic**: Works with OpenAI, Anthropic, xAI, Ollama, or self-hosted models. Your data stays in your infrastructure
- **Molecules**: Modular integration plugins that combine discovery, tools, rich visualizations, and security. Built on open standards like MCP

## Key Metrics

- 70% reduction in mean time to resolve (MTTR) incidents
- Complete organizational visibility across code, infra, teams, and deployments
- Zero surprise production breaks-know what changes before you ship

## Integrations

- GitHub
- GitLab
- Jira
- Vercel
- Kubernetes
- Slack
- Grafana
- ArgoCD
- PagerDuty
- FOSSA

## AI Models Supported

- OpenAI
- Anthropic
- xAI
- Ollama

## Deployment Options

- Self-hosted (runs in your own infrastructure)
- SaaS (hosted by SixDegree)
- Open source integrations
- Full SDK & APIs available

## Target Audience

- Engineering teams struggling with system complexity and knowledge silos
- Platform and operations teams looking to reduce incident response time
- Product teams that need visibility into blast radius and delivery tracking
- Companies scaling engineering organizations and onboarding new engineers
- Teams adopting AI tools for development and operations

## Latest Blog Posts

### [Operating MCP Servers in Production](https://sixdegree.ai/blog/operating-mcp-servers-production)
Getting an MCP server to work is not the hard part. The hard part is operating one: knowing who can access what, catching failures before users do, and building enough visibility to improve over time.

### [Building MCP Servers That Models Can Actually Use](https://sixdegree.ai/blog/building-mcp-servers-tools-and-context)
Model Context Protocol has moved fast, but implementation quality lags behind adoption. This post covers tool design and context quality: the two areas where most MCP servers fail.

### [Your RAG Passed Every Test and Failed Every User](https://sixdegree.ai/blog/rag-passed-every-test-failed-every-user)
Eval overfitting is real and underdiagnosed. Most RAG systems are built on a flat, static, insider-authored approximation of organizational knowledge. They don't have a model of the organization. They have documents about it.

### [Building AI Agents: The Fundamentals](https://sixdegree.ai/blog/building-agents-fundamentals)
Twelve rules for building AI agents that actually work. What agents are, how the agentic loop works, and the mental models that matter.

### [We Gave LLMs 150 Tools: Here's What Broke.](https://sixdegree.ai/blog/mcp-tool-overload)
MCP tool overload is real. We benchmarked six LLMs with 25 to 150 MCP tools and measured accuracy degradation, latency spikes, and hard API limits. The cheapest model won.

### [MCP vs CLI: You're Asking the Wrong Question](https://sixdegree.ai/blog/mcp-vs-cli)
The debate between MCP and CLI for agent tooling misses the point. The real question is which mode you're building for, and where the actual token costs hide.

### [Internal Developer Portals vs Context Layers](https://sixdegree.ai/blog/idp-vs-context-layer)
IDPs were built for humans browsing catalogs. AI agents need something different: queryable relationships, real-time state, and cross-system reasoning. Here's why IDPs can't close the gap.

### [a16z Just Described What We've Been Building](https://sixdegree.ai/blog/a16z-context-layer-thesis)
Andreessen Horowitz published their thesis on why data agents need a context layer. Canonical entities, identity resolution, tribal knowledge, governance. We've been building exactly this.

### [Service Catalog vs Live Ontology: Why Static Catalogs Fail](https://sixdegree.ai/blog/service-catalog-vs-live-ontology)
Service catalogs promised to be the single source of truth for your infrastructure. Here's why they go stale, what a live ontology provides instead, and when each approach actually makes sense.

### [Progressive Disclosure for Agents](https://sixdegree.ai/blog/progressive-disclosure)
Giving an LLM 40 tools and hoping it picks the right one is the same mistake dashboards made for humans. Progressive disclosure fixes it, but for agents the mechanism is different.


## Contact

- Website: https://sixdegree.ai
- Email: hello@sixdegree.ai
- GitHub: https://github.com/sixdegree-ai
- Blog: https://sixdegree.ai/blog
- Book a Demo: https://sixdegree.ai (Calendly)

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Last Updated: 2026-04-01