Your platform team shouldn't be a human search engine. Probe gives every engineer self-serve access to infrastructure knowledge, dependency maps, and operational context.
Free and open source. Business and Enterprise plans available.
Map dependencies across all repos and services. Know what breaks when you change something before you push.
Enforce platform standards on every PR. Catch Terraform misconfigurations, missing health checks, and security issues automatically.
Stop answering the same questions. App teams query the codebase directly instead of filing tickets against your team.
Correlate alerts with recent deploys, config changes, and dependency updates. Cut MTTR by giving on-call real context.
Platform teams build infrastructure so app teams can move fast. But "move fast" turns into "Slack the platform team for everything" the moment something isn't documented.
Your team spends 40% of its time answering questions that are already answered in code. "How does the deploy pipeline work?" "Which env vars does service X need?" "Why does this Terraform module exist?" The answers are there. Nobody can find them.
You write docs. Infrastructure changes. Docs go stale. App teams follow outdated runbooks and break things. You spend time fixing what they broke and updating docs they won't read. The cycle repeats every sprint.
Org adds 3 new teams. Each needs onboarding to your platform. Each has different tech stacks and requirements. Your 4-person platform team is now supporting 12 app teams, and the Jira board is nothing but "help me deploy" tickets.
Real prompts from platform engineers. Infrastructure questions, dependency mapping, incident investigation, and automation across the entire stack.
Ask any question about any service, any Terraform module, any Helm chart, and get answers grounded in actual code.
Probe reads infrastructure-as-code the same way it reads application code. It understands Terraform resources, Kubernetes manifests, Helm values, Docker compose files, and CI/CD pipelines. It connects the dots between application code and the infrastructure that runs it.
Every PR reviewed against your platform standards. Every new service validated against your golden path.
Generic code review tools don't understand your platform. They don't know that every service needs a /healthz endpoint, that Terraform modules must tag resources with cost center, or that Dockerfiles should use your internal base images. They can't tell you that a Helm chart is missing resource limits.
Probe learns your platform standards and enforces them on every change. It checks Terraform plans for security misconfigurations, validates Kubernetes manifests against your policies, and catches breaking changes to shared infrastructure before they hit production.
Correlate incidents with code changes, config drifts, and dependency updates across your entire stack.
When something breaks at 2 AM, the on-call engineer shouldn't need to know the entire system to debug it. They need context: what changed recently, what depends on the broken component, and what's the safest way to fix it. That context is scattered across 30 repos, 5 monitoring tools, and someone's head.
Probe assembles that context automatically. It correlates alerts with recent PRs, Terraform applies, and config changes. It maps blast radius by tracing dependencies. It surfaces relevant runbooks and past incidents. The on-call engineer gets a complete picture, not a PagerDuty alert with no context.
Pre-built automation workflows for common platform team responsibilities. Deploy them, customize per team, iterate over time.
Automated review of Terraform changes, Kubernetes manifests, and Helm charts. Validates against platform security policies, checks for resource misconfigurations, and detects breaking changes to shared modules before they merge.
When an alert fires, automatically gather context: recent deploys, config changes, dependency status, related past incidents. Give on-call everything they need to start debugging without asking anyone.
When a new service repo is created, audit it against your golden path. Check for required health checks, structured logging, graceful shutdown, Dockerfile standards, and Helm chart completeness. Open issues for gaps.
Nightly scans for CVEs in base images, stale Terraform modules, unused resources, over-provisioned services, and drift between declared and actual state. Summary posted to your platform channel.
Runs entirely inside your infrastructure. Code and infrastructure configs never leave your environment. Meets SOC 2, HIPAA, and FedRAMP requirements.
Use your org's preferred model -- Claude, GPT, open-source, or self-hosted behind your firewall. Switch providers without changing workflows or losing context.
OpenTelemetry instrumentation on every query and workflow execution. Export to your existing Datadog, Grafana, or Splunk stack. Debug AI workflows like any other system.
The core engine is open source. Your security team can audit exactly how code is processed. No vendor black boxes in your infrastructure stack.
Start with open source on a single repo. Scale to enterprise when you need multi-repo architecture and workflow automation across teams.
Free forever
The core code intelligence engine. Evaluate the technology on a single infrastructure repo or service codebase.
Contact for pricing
Everything in Open Source, plus multi-repo architecture, cross-service dependency mapping, workflow automation, and integrations.
Two-phase approach: validate core technology with open source, then pilot enterprise features on your actual infrastructure stack.
Pick any of these and have something running before your next standup. No account required.
Get infrastructure-aware code search in Claude Code, Cursor, or any MCP-compatible tool. Point it at your Terraform repo or service codebase. One command to install.
Add a GitHub Action for automated review of Terraform changes, Kubernetes manifests, and Helm charts. Every PR gets a first-pass review for security, cost impact, and compliance.
Create a Slack bot that answers questions about your infrastructure. App teams ask "how do I deploy to staging?" and get answers grounded in your actual CI/CD config and Helm charts.
Once you've validated the core technology, run a pilot across your infrastructure stack to test multi-repo understanding, automated reviews, and operational intelligence.
We work with your team to connect all service repos, infra repos, and shared libraries. Map cross-service dependencies and configure access controls.
Codify your golden path into review rules. Health checks, resource limits, logging standards, security policies -- all enforceable on every PR automatically.
Set up incident context assembly, nightly hygiene scans, and service onboarding automation. Connect to your monitoring stack via OpenTelemetry.
Track support ticket volume from app teams, mean time to resolve incidents, and infra PR review cycle time. Compare to pre-pilot baselines.
Want to discuss how a pilot would work for your infrastructure stack?
Schedule a Technical DiscussionDeveloper portals give you a catalog of services with manually maintained metadata. Probe gives you live, queryable access to the actual code, configs, and infrastructure. When someone asks "how does service X connect to the database?", Probe reads the connection config from the code. Backstage shows whatever someone wrote in a YAML file six months ago.
They're complementary -- Probe can actually keep your Backstage catalog accurate by generating metadata from real code.
Yes. Probe reads infrastructure-as-code semantically, not as flat text. It understands Terraform resource relationships, Kubernetes manifest structures, Helm value overrides, and Docker multi-stage builds. It can trace a reference from a Helm values file to the Terraform module that provisions the underlying resource.
The enterprise tier connects all your repositories -- service code, infrastructure code, shared libraries, CI/CD configs. When you ask a question, it determines which repos are relevant and pulls context from all of them. A question about "why is service X slow" might pull code from the app repo, networking config from the infra repo, and resource limits from the Helm chart.
Yes. Probe runs entirely locally -- retrieval is local and you control what context is sent to the model. You choose your LLM provider, including self-hosted models like Llama or Mistral. All workflow execution is local. OpenTelemetry traces export to your existing monitoring stack. No data leaves your network unless you explicitly configure it to.
Let's talk about how Probe can reduce your platform team's support load and give app teams self-serve access to infrastructure context. We'll show you how it works on real infrastructure code.