Give your support team code-level context without needing to read code. Investigate technical issues independently. Reduce escalations. Resolve tickets in hours, not days.
Free and open source. Business and Enterprise plans available.
Get code-level context for customer issues without reading code. Understand what changed, what's affected, and what the fix should look like.
Reduce average resolution time from days to hours. Support teams can diagnose and resolve technical issues without waiting in the engineering queue.
New tickets automatically get code context, related issues, and recent changes. Support starts with full context instead of gathering it manually.
Find related Zendesk tickets, Jira issues, and code changes automatically. Surface patterns that indicate root causes before they become major incidents.
But every technical question means escalating to engineering. Context-gathering takes longer than the actual fix. Your team is flying blind, customers are waiting, and engineers are drowning in interruptions.
Customer reports payment failure. Support needs to know: Is this a bug? What changed? Which customers are affected? Without code context, every technical issue escalates to engineering. 70% of escalated tickets could be resolved by support with the right context. But that context is locked in code.
Engineering gets a ticket. They need to understand: What exactly is broken? When did it start? What changed recently? What did the customer try? Engineers spend more time gathering context than fixing the issue. Tickets bounce back and forth. Three days later, the customer is frustrated and the fix takes five minutes.
Five different customers report similar issues. Each ticket is handled independently. Nobody realizes they share a root cause until the pattern becomes obvious. By then, you've wasted 15 engineering-hours investigating the same bug five times. The root cause was a single deploy three days ago.
Real prompts from support operations and customer success teams. Questions, investigations, and automations — connecting tickets to code.
New tickets automatically get code context, related issues, and recent changes — before a human even looks at them.
When a ticket arrives, Probe immediately analyzes it. It extracts keywords and error messages, searches the codebase for relevant components, checks recent deployments, finds similar past tickets, and identifies known issues. All of this context is added as an internal comment before your team even opens the ticket.
Support teams can ask "what changed?" and get answers without reading code — or waiting for engineering.
Traditional support teams are blind to the codebase. They know what customers report, but not what the code actually does. This creates a hard dependency on engineering for any technical investigation.
Probe gives support teams code-level visibility without requiring them to read code. They can trace customer issues to specific code paths, compare releases to identify regressions, understand feature flags and configuration, and identify which customers are affected by a deployment. All in plain English.
Connect Zendesk tickets to Jira issues to code changes to deployments — automatically surface the patterns.
Most support issues aren't isolated. Five different customers report the same bug, but nobody realizes they're related until it's obvious. By then, you've wasted hours investigating the same root cause five different times.
Probe connects the dots across systems. It correlates Zendesk tickets with Jira issues, links customer reports to recent deployments, detects patterns that indicate systemic problems, and generates intelligence reports showing trends and root causes. Support becomes proactive instead of reactive.
Pre-built automation workflows you can deploy immediately. Customize per team. Version like code. Improve over time.
When a ticket is created, automatically classify it (bug, feature request, config issue), gather relevant code context, find related issues, check for known problems, and add enrichment as internal comment.
Trace customer reports to code. Find the affected components, recent changes, similar past issues, and known workarounds. Generate customer-friendly explanations and response drafts.
After each deployment, analyze user-facing changes, identify which customers might be affected, detect potential breaking changes, and proactively notify support team with talking points.
Build and maintain support knowledge base automatically. Extract common issues, workarounds, and solutions from code, tickets, and deployments. Keep documentation synchronized with code changes.
Runs entirely inside your infrastructure. Customer data and code never leave your environment. Full data sovereignty and compliance compatibility.
Use your preferred model — Claude, GPT, open-source, or self-hosted. No vendor lock-in. Switch providers without changing workflows.
OpenTelemetry instrumentation captures every query, every automation run, every decision. Complete traceability for compliance and debugging.
The core engine is open source and auditable. Inspect exactly how customer data is processed. No black boxes in your support stack.
Start with the open-source core to evaluate the technology, then scale to enterprise when you need automation and integrations.
Free forever
The core code intelligence engine. Perfect for evaluating the technology on a single project or for individual support engineers exploring a codebase.
Contact for pricing
Everything in Open Source, plus automation, integrations, and cross-system correlation for support operations at scale.
We recommend a two-phase approach: first, validate the core technology with open source, then pilot the automation workflows on a real support queue.
Pick any of these and have something running before your next meeting. No account required.
Install Probe and ask questions about your codebase in plain English. No code reading required. Get answers grounded in actual code with file references for verification.
Create a Slack bot that answers questions about your codebase. Your support team can investigate issues in Slack without leaving their workflow or waiting for engineering.
Set up basic Zendesk integration. When a ticket is created, Probe automatically searches the codebase for related components and recent changes, adding context as an internal comment.
Once you've validated the core technology, run a pilot on a real support queue to test the full automation: ticket enrichment, correlation, and investigation workflows.
Choose a support queue with technical issues (bugs, performance, integrations). Ideally where tickets currently escalate frequently to engineering.
Connect Zendesk, Jira, and your repos. Configure automated workflows: new ticket enrichment, hourly correlation, deployment monitoring, pattern detection.
Show support engineers how to use code-aware investigation. Teach them to ask "what changed?" and trust the answers. Review enrichment comments on tickets.
Track: average resolution time, escalation rate to engineering, tickets resolved by support without engineering, customer satisfaction. Compare to pre-pilot baseline.
Want to discuss how a pilot would work for your support team?
Schedule a ConversationTraditional knowledge bases are manually written and quickly become outdated. They capture what someone thought was important six months ago, not what's actually in the code today.
Probe reads the actual codebase in real-time. When code changes, the answers change. When a customer asks about a feature, Probe checks the current implementation, not a stale document. It's always accurate because it's always reading the source of truth.
No. Support engineers ask questions in plain English and get answers in plain English. Probe translates code into explanations: "The payment flow checks for valid billing address, then calls Stripe API, then updates the order status in the database."
Every answer includes file references so engineering can verify if needed, but support can understand and act on the answers without reading code.
Probe analyzes actual code, not documentation or guesses. When it says "this component changed 2 days ago in PR #1847," that's a fact pulled from Git history. When it finds related tickets, those are real Zendesk or Jira tickets with similar keywords and error patterns.
The enrichments are designed to give support context, not to make final decisions. A human still reviews the ticket, but they start with relevant context instead of starting from zero.
Most teams see: 30-50% reduction in engineering escalations (support can resolve more issues independently), 40-60% faster average resolution time (start with context instead of gathering it), 20-30% fewer duplicate investigations (pattern detection catches systemic issues early).
The exact numbers depend on your current escalation rate and the complexity of your product. Most teams break even within the first quarter.
Let's talk about how Probe can transform your support operations. We'll show you how it works on a real support queue — yours — and discuss how to structure a pilot.