For Engineering Ops & Platform Teams

Automate the Engineering ToilThat Eats Your Sprint

Stop burning engineering hours on manual health checks, ticket triage, release notes, and compliance audits. Visor automates the recurring operational tasks that should have been automated years ago.

Code-awareScheduled jobsEvent-drivenAny LLM

Built on Probe + Visor. Enterprise plans available.

What changes for your team
Before
Manual morning health checks every day
Copy-paste release notes from PRs
CI failures investigated manually
After
Automated health dashboard posted daily
Release notes auto-generated from commits
Root cause analysis posted to Slack instantly
Code-aware automation
Cron-like Event-driven Code context

Why engineering teams automate with Visor

Scheduled Automations

Cron-like jobs powered by code intelligence. Morning health checks, nightly audits, weekly compliance reports — automated and code-aware.

Event-Driven Workflows

Trigger on PR merge, deploy complete, ticket created, or CI failure. Automations that respond to what's happening in your system.

Code-Aware Actions

Automations that understand your codebase. Not just text processing — real semantic understanding of code, dependencies, and architecture.

Reduce Toil by 70%

Eliminate repetitive operational tasks that consume engineering time. Focus on building, not babysitting recurring work.

The Real Problem

Your team ships features. Then spends 30% of each sprint on toil.

Manual health checks. Copy-paste release notes. Ticket triage. CI failure analysis. Compliance audits. These aren't engineering problems. They're autopilot problems.

01

The Toil Tax

Engineers spend 30%+ of their time on repetitive operational tasks. Morning health checks that could be a cron job. Ticket enrichment that's the same pattern every time. Release notes copy-pasted from PRs. Every sprint, the same toil eats the same hours. The work gets done, but it shouldn't require a human.

02

The Stale Report

You manually compile a status report. Two hours later, it's outdated. A deploy shipped. Tickets changed. The infrastructure shifted. The report you just finished is already wrong, and next week you'll do it again. Manually compiled reports are always stale by the time someone reads them.

03

The Missed Signal

Critical alerts buried in noise because nobody has time to correlate. The CI fails nightly, but which failure matters? Three new tickets look similar — are they related? A deploy went out 6 hours ago — is that why latency spiked? Engineers miss signals because there are too many and not enough time to connect the dots.

Engineering workflows teams actually automate

Real automation examples from engineering teams. Scheduled jobs, event-driven workflows, and intelligence that runs on autopilot.

Automation
@Probe every morning: check Datadog for error rates above 1% in the last 12 hours
Morning health check scheduled for 8:00 AM daily. I'll query Datadog for services exceeding thresholds, correlate any anomalies with recent deploys from GitHub, and post a summary to #on-call. If everything is healthy, I'll post a green status. If issues are found, I'll include: affected service, metric values, likely cause, and link to the relevant deploy or config change.
Automation
@Probe every hour: check newly created Jira tickets, find related issues, classify
Hourly ticket enrichment active. Every hour I'll scan Jira for tickets created in the last 60 minutes, search for related customer cases by keyword and account, classify each issue (bug, feature request, regression, config error), and post a comment with: related tickets, affected customers, preliminary root cause from the codebase, and suggested priority. Already processed 4 new tickets — PROJ-4210 linked to 3 cases from the same customer, classified as regression in the billing module.
Automation
Nightly CI failed — analyze failure, identify root cause, post to #engineering
Build #4721 failed at 03:12 UTC. Root cause: integration test test_api_gateway_timeout depends on external sandbox endpoint that returned 503. Not a code regression — infrastructure issue. Posted to #engineering: "Nightly build failed — external sandbox was down. Tests are green when sandbox is healthy. Recommend: add retry logic to integration suite or mock the external dependency. Created INFRA-892 for the sandbox team."
Digest
Build release notes from merged PRs this sprint
Release Notes for v5.10.1 — Changed: Upgraded Go version to enhance security. Fixed: Addressed high-impact CVEs. Fixed critical issue where Hybrid mode would hang. Patched "Zip Slip" vulnerability in bundle decompression. Added: New webhook retry logic with exponential backoff. Performance: Reduced memory consumption in request pipeline by 15%...
Automation
Read ticket, find spec, generate test cases, update ticket
Done. Read PROJ-8410 (webhook retry logic). Traced back to PROJ-7201 (original spec) and RFC-031 (design doc). Generated 14 test cases: 5 positive (successful retry, backoff timing, max retries), 4 negative (malformed payload, timeout, auth failure), 5 edge (concurrent retries, partial failure, clock skew). Updated PROJ-8410 with all cases. Gap analysis: tests/webhook_test.go covers 6/14 — missing timeout, concurrent, and all edge cases.
Automation
Scan merged PRs for missing changelog entries
Scanned last 20 merged PRs. 3 PRs missing changelog entries: PR #672 (webhook retry changes), PR #681 (API rate limit headers), PR #689 (auth token expiry). All 3 PRs have "breaking-change" or "feature" labels but no CHANGELOG.md update. Created PROJ-6105, PROJ-6106, PROJ-6107 with PR links and suggested changelog text. Posted summary to #engineering.
Automation
@Probe every morning: check Datadog for error rates above 1% in the last 12 hours
Morning health check scheduled for 8:00 AM daily. I'll query Datadog for services exceeding thresholds, correlate any anomalies with recent deploys from GitHub, and post a summary to #on-call. If everything is healthy, I'll post a green status. If issues are found, I'll include: affected service, metric values, likely cause, and link to the relevant deploy or config change.
Automation
@Probe every hour: check newly created Jira tickets, find related issues, classify
Hourly ticket enrichment active. Every hour I'll scan Jira for tickets created in the last 60 minutes, search for related customer cases by keyword and account, classify each issue (bug, feature request, regression, config error), and post a comment with: related tickets, affected customers, preliminary root cause from the codebase, and suggested priority. Already processed 4 new tickets — PROJ-4210 linked to 3 cases from the same customer, classified as regression in the billing module.
Automation
Nightly CI failed — analyze failure, identify root cause, post to #engineering
Build #4721 failed at 03:12 UTC. Root cause: integration test test_api_gateway_timeout depends on external sandbox endpoint that returned 503. Not a code regression — infrastructure issue. Posted to #engineering: "Nightly build failed — external sandbox was down. Tests are green when sandbox is healthy. Recommend: add retry logic to integration suite or mock the external dependency. Created INFRA-892 for the sandbox team."
Digest
Build release notes from merged PRs this sprint
Release Notes for v5.10.1 — Changed: Upgraded Go version to enhance security. Fixed: Addressed high-impact CVEs. Fixed critical issue where Hybrid mode would hang. Patched "Zip Slip" vulnerability in bundle decompression. Added: New webhook retry logic with exponential backoff. Performance: Reduced memory consumption in request pipeline by 15%...
Automation
Read ticket, find spec, generate test cases, update ticket
Done. Read PROJ-8410 (webhook retry logic). Traced back to PROJ-7201 (original spec) and RFC-031 (design doc). Generated 14 test cases: 5 positive (successful retry, backoff timing, max retries), 4 negative (malformed payload, timeout, auth failure), 5 edge (concurrent retries, partial failure, clock skew). Updated PROJ-8410 with all cases. Gap analysis: tests/webhook_test.go covers 6/14 — missing timeout, concurrent, and all edge cases.
Automation
Scan merged PRs for missing changelog entries
Scanned last 20 merged PRs. 3 PRs missing changelog entries: PR #672 (webhook retry changes), PR #681 (API rate limit headers), PR #689 (auth token expiry). All 3 PRs have "breaking-change" or "feature" labels but no CHANGELOG.md update. Created PROJ-6105, PROJ-6106, PROJ-6107 with PR links and suggested changelog text. Posted summary to #engineering.
Automation
Weekly: audit open PRs older than 7 days, ping reviewers
Weekly PR audit complete. 8 PRs open longer than 7 days. 3 PRs blocked on review (requested reviewers haven't responded). 2 PRs have failing tests (authors haven't pushed fixes). 3 PRs are WIP but not marked as draft. Posted summary to #engineering with @mentions for reviewers and authors. Created reminder task for next week.
Automation
Nightly: check for dependencies with known CVEs
Scanned dependencies across 14 services. 2 critical CVEs found: user-service depends on express@4.17.1 (CVE-2024-29041), payment-service depends on lodash@4.17.19 (CVE-2020-8203). 5 high-severity CVEs in dev dependencies (non-production). Opened PRs #720, #721 to bump vulnerable packages. All tests passing. Posted security summary to #platform-security.
Automation
On deploy: compare staging vs production configs
Deploy triggered for payment-service v3.2.0 to production. Config comparison: staging has STRIPE_API_VERSION=2023-10-16, production has STRIPE_API_VERSION=2023-08-16 (2 versions behind). 3 environment variables present in staging but missing in production: FRAUD_CHECK_ENABLED, RETRY_MAX_ATTEMPTS, CACHE_TTL. Recommend: sync configs before deploying or document intentional differences. Posted to #deploys.
Digest
Daily: generate standup summary from yesterday's commits
Standup summary for 2024-01-15. Team shipped: 12 PRs merged across 5 services. Major changes: payment-service added 3DS authentication (PR #672), user-service refactored session handling (PR #681), notification-worker added retry logic (PR #689). In progress: 6 open PRs awaiting review. Blockers: analytics-ingest waiting on schema migration approval from data team. Posted to #daily-standup.
Automation
On new ticket: gather code context and suggest related issues
New ticket created: PROJ-8821 "API rate limit not working for /api/v2/orders endpoint". Gathered context from codebase: rate limiting implemented in middleware/rate_limit.go, endpoint defined in handlers/orders.go. Found 3 related tickets: PROJ-8103 (same endpoint, different issue), PROJ-7892 (rate limit bypass in v1 API), PROJ-8445 (Redis connection pool issue affecting rate limiter). Posted analysis to ticket with code references and links.
Automation
Weekly: compliance report on code changes to regulated modules
Weekly compliance audit for regulated modules (payment processing, user data, PHI access). 4 PRs merged this week touching regulated code. All 4 PRs have required security review approval. 1 PR (payment-service #672) added new PII field — verified encryption at rest and logging exclusion are in place. 0 policy violations detected. Compliance report generated and posted to #security-compliance.
Automation
Weekly: audit open PRs older than 7 days, ping reviewers
Weekly PR audit complete. 8 PRs open longer than 7 days. 3 PRs blocked on review (requested reviewers haven't responded). 2 PRs have failing tests (authors haven't pushed fixes). 3 PRs are WIP but not marked as draft. Posted summary to #engineering with @mentions for reviewers and authors. Created reminder task for next week.
Automation
Nightly: check for dependencies with known CVEs
Scanned dependencies across 14 services. 2 critical CVEs found: user-service depends on express@4.17.1 (CVE-2024-29041), payment-service depends on lodash@4.17.19 (CVE-2020-8203). 5 high-severity CVEs in dev dependencies (non-production). Opened PRs #720, #721 to bump vulnerable packages. All tests passing. Posted security summary to #platform-security.
Automation
On deploy: compare staging vs production configs
Deploy triggered for payment-service v3.2.0 to production. Config comparison: staging has STRIPE_API_VERSION=2023-10-16, production has STRIPE_API_VERSION=2023-08-16 (2 versions behind). 3 environment variables present in staging but missing in production: FRAUD_CHECK_ENABLED, RETRY_MAX_ATTEMPTS, CACHE_TTL. Recommend: sync configs before deploying or document intentional differences. Posted to #deploys.
Digest
Daily: generate standup summary from yesterday's commits
Standup summary for 2024-01-15. Team shipped: 12 PRs merged across 5 services. Major changes: payment-service added 3DS authentication (PR #672), user-service refactored session handling (PR #681), notification-worker added retry logic (PR #689). In progress: 6 open PRs awaiting review. Blockers: analytics-ingest waiting on schema migration approval from data team. Posted to #daily-standup.
Automation
On new ticket: gather code context and suggest related issues
New ticket created: PROJ-8821 "API rate limit not working for /api/v2/orders endpoint". Gathered context from codebase: rate limiting implemented in middleware/rate_limit.go, endpoint defined in handlers/orders.go. Found 3 related tickets: PROJ-8103 (same endpoint, different issue), PROJ-7892 (rate limit bypass in v1 API), PROJ-8445 (Redis connection pool issue affecting rate limiter). Posted analysis to ticket with code references and links.
Automation
Weekly: compliance report on code changes to regulated modules
Weekly compliance audit for regulated modules (payment processing, user data, PHI access). 4 PRs merged this week touching regulated code. All 4 PRs have required security review approval. 1 PR (payment-service #672) added new PII field — verified encryption at rest and logging exclusion are in place. 0 policy violations detected. Compliance report generated and posted to #security-compliance.

Three automation primitives that power everything

01

Scheduled Automations

Cron-like jobs powered by code intelligence. Run on schedule, understand your codebase, take action.

  • "Every morning: check Datadog for error spikes and correlate with recent deploys"
  • "Every hour: scan new Jira tickets and auto-classify by affected service"
  • "Nightly: audit services for missing health checks and open remediation tickets"
  • "Weekly: generate compliance report on changes to regulated code modules"

Traditional cron jobs can't understand code. They execute scripts that grep logs or parse JSON. Visor's scheduled automations read code semantically, correlate across systems (GitHub, Jira, Datadog), and take intelligent action based on what they find.

Code-aware schedulingJobs understand codebase context, not just text patterns
Cross-system correlationQuery GitHub, Jira, Datadog, Slack in a single workflow
Actionable outputsPost to Slack, open tickets, create PRs, update docs automatically
02

Event-Driven Workflows

Trigger automations on PR merge, deploy completion, ticket creation, CI failure, or any event in your system.

The best automations run when they're needed, not on a fixed schedule. When a CI build fails, analyze it immediately. When a ticket is created, enrich it with code context right away. When a deploy completes, validate configs before traffic shifts.

Visor workflows trigger on real events from GitHub, Jira, CI systems, and deployment pipelines. Each workflow has full code context and can take action across your entire toolchain.

GitHub webhooksTrigger on PR open, merge, comment, issue creation, or release
CI/CD eventsRun workflows on build failure, deploy start, deploy complete, test failure
Ticket system eventsRespond to new Jira tickets, status changes, or customer escalations
03

Code-Aware Actions

Automations that understand code semantically, not just as text. Know the difference between a real issue and noise.

Generic automation tools treat code as text. They can't tell you if a failing test is flaky or a real regression. They can't trace a customer bug report back to the code module responsible. They can't determine if two tickets share a root cause by analyzing code paths.

Visor actions are powered by Probe's code intelligence. They understand functions, classes, dependencies, and call graphs. They correlate alerts with actual code changes. They trace bugs to source. They know what breaking changes look like, not just what git diff shows.

Semantic code understandingParse and understand code structure, not just text patterns
Dependency awarenessKnow which services depend on what, which changes break what
Historical contextLearn from past PRs, tickets, and incidents to make smarter decisions

Workflow packs for common engineering operations

Pre-built automation workflows you can deploy immediately. Customize to your environment. Version like code.

Daily

Morning Ops

Start every day with automated health checks. Query metrics from Datadog, correlate with recent deploys, scan for error spikes, and post a morning status update to your on-call channel. Green checkmark or investigation required.

  • Health check dashboard posted daily
  • Error rate correlation with deploys
  • Performance regression detection
  • Auto-triage critical alerts
On Release

Release Management

Automate the busywork around releases. Generate release notes from merged PRs, check for missing changelog entries, validate staging vs production configs, and post deploy summaries. Let machines handle the checklist.

  • Auto-generated release notes
  • Changelog completeness check
  • Config drift validation
  • Deploy success summary
On Ticket

Ticket Intelligence

When a ticket is created, auto-enrich it with context. Find the relevant code modules, link related past tickets, classify by type and severity, and suggest which team should own it. Save engineers from doing this detective work manually.

  • Auto-classify ticket type and priority
  • Link to relevant code modules
  • Find related historical tickets
  • Suggest owning team based on code
Weekly

Compliance & Audit

Scheduled scans for security, compliance, and code hygiene. Check for CVEs in dependencies, audit code changes to regulated modules, scan for missing tests or docs, and generate compliance reports. All automated, all code-aware.

  • CVE scan with auto-bump PRs
  • Compliance audit for regulated code
  • Missing test coverage report
  • Stale documentation detection

Built for production workflows

On-Premises Deployment

Workflows run entirely in your infrastructure. Code, secrets, and operational data never leave your environment. Full compliance compatibility.

Any LLM Provider

Use Claude, GPT, open-source models, or self-hosted LLMs. Workflows adapt to your model choice. No vendor lock-in.

Full Audit Trail

OpenTelemetry instrumentation on every workflow run. Track what was queried, what decisions were made, what actions were taken. Debug automations like code.

Open Source Core

Built on Probe's open-source engine. Workflows are versioned like code. Inspect, audit, and customize everything.

Open Source vs Enterprise

Start with Probe open source for code intelligence, then add Visor enterprise for scheduled and event-driven automation workflows.

Probe Open Source

Free forever

The core code intelligence engine. Ask questions about your codebase, understand dependencies, trace code paths. Perfect for individual engineers or small teams.

  • Single-project code understanding — Ask questions about one repository at a time
  • Semantic code search — Understands code as code (functions, classes, dependencies), not just text
  • No indexing required — Works instantly on any codebase, runs locally
  • MCP integration — Use with Claude Code, Cursor, or any MCP-compatible tool
  • Any LLM provider — Claude, GPT, open-source models — your choice
  • Privacy-first — Everything runs locally, no data sent to external servers

Visor Enterprise

Contact for pricing

Everything in Probe Open Source, plus workflow automation, multi-repo intelligence, scheduled jobs, event-driven actions, and team integrations.

  • Scheduled automations — Cron-like jobs powered by code intelligence (daily, hourly, weekly)
  • Event-driven workflows — Trigger on PR merge, deploy, ticket creation, CI failure, or custom events
  • Multi-repository architecture — Query and automate across your entire system of services
  • Code-aware actions — Automations that understand code semantically, not just as text
  • GitHub integration — Auto-comment on PRs, open issues, create commits based on workflow results
  • Jira integration — Auto-classify tickets, enrich with code context, update based on code changes
  • Datadog / Grafana integration — Correlate alerts with deploys, code changes, and dependency updates
  • Slack/Teams integration — Post workflow results to channels, trigger workflows from chat
  • Workflow library — Pre-built workflows for ops, release management, compliance, and hygiene
  • On-premises deployment — Runs entirely in your infrastructure for maximum security

How to evaluate workflow automation

Start with Probe open source to validate code intelligence, then pilot Visor automations on real operational workflows.

Phase 1

Technical Validation

~10 minutes

Install Probe and test code intelligence on your codebase. No automation yet — just validate that it understands your code.

~2 min

Add Probe to AI Coding Tools

Install Probe as an MCP server for Claude Code, Cursor, or any MCP-compatible tool. Ask it questions about your codebase. See if it understands dependencies, architecture, and code patterns.

You get: A specialized AI agent for code search and analysis. Test prompts: "How does auth work?", "Which services depend on X?", "What changed between v1 and v2?"
AI code editor setup →
~5 min

Test Automated PR Review

Add Probe as a GitHub Action to review PRs. See how it flags security issues, performance problems, and breaking changes with code-aware analysis.

You get: First-pass PR review on every commit. Test on a few recent PRs to see what it catches.
Phase 2

Pilot Automation Workflows

2-4 weeks

Once code intelligence is validated, pilot 2-3 real automation workflows. Morning health checks, ticket enrichment, or release notes generation are good starting points.

1
Pick high-toil workflows

Choose 2-3 workflows your team does manually every day or week. Morning health checks, ticket triage, changelog generation, or compliance audits are common choices.

2
Setup and configuration

We'll help you connect Visor to your GitHub, Jira, Datadog, and Slack. Configure the first workflow with your team's actual context and policies.

3
Run workflows in parallel

Let the automation run alongside the manual process for 1-2 weeks. Compare the automation's output to what your team does manually. Tune the workflow based on feedback.

4
Measure time saved

Track how many hours per week the workflows save. If morning health checks took 30 min/day and are now automated, that's 2.5 hours/week per engineer saved.

Success criteria: Workflows produce output quality equal to or better than manual process. Measurable time savings (target: 5-10 hours/week for a 5-person team). Team trusts the automation enough to stop doing the work manually.

Want to discuss which workflows to automate first?

Schedule a Workflow Discussion

What teams ask us about automation

How is this different from Zapier or GitHub Actions?

Zapier and GitHub Actions automate tasks, but they treat code as text. They can't understand dependencies, trace code paths, or correlate a customer bug with the module responsible. They execute scripts. They don't understand what the scripts are operating on.

Visor automations are powered by Probe's code intelligence. They understand your codebase semantically — functions, classes, dependencies, architecture. They can answer "which services would break if I change this?" Zapier cannot.

Do workflows run on a schedule or in real-time?

Both. Scheduled workflows run on cron-like schedules (every morning, nightly, weekly). Event-driven workflows trigger on real events from GitHub, Jira, CI systems, or deployments. You choose which model fits each automation.

What if a workflow makes a mistake?

Workflows can be configured to run in "dry-run" mode where they post results but don't take action (no PRs, no ticket updates, no deploys). You can review outputs, tune the workflow, and enable actions when you trust it. All workflow runs are logged and auditable via OpenTelemetry.

Can we customize workflows for our specific processes?

Yes. Workflows are configured like code and versioned in your repo. You can customize prompts, add integration steps, change schedules, and define custom actions. We provide workflow templates as starting points, then you adapt them to your team's actual processes.

Ready to automate the toil?

Let's talk about which workflows are eating your team's time and how to automate them. We'll show you real workflow examples and discuss how to run a pilot with your team.