API Health Monitor
Given an API endpoint + expected behavior, returns a structured health read: latency, status, expected vs actual response shape.
The infra hygiene nobody schedules and everybody needs — the backup, the SSL check, the log scan, the one-off cron script that rots until it silently stops firing. Describe the check in plain English. InTouch runs it, watches it, and when it breaks InTouch AI reads the failure, tells you why, and fixes what InTouch AI can.
Backups, monitors, SSL/DNS watchers, log scanners, GitHub digests, postmortem drafts. Runs on your own machine, alongside your existing stack — nothing leaves your network.
InTouch AI lives on your own machine, alongside your existing monitoring and CI. InTouch AI doesn't replace Datadog or Prometheus — InTouch AI fills the gaps where a one-off script plus a cron job normally rots until it silently stops firing. No per-run bill, and nothing leaves your network.
You tell InTouch AI what to do, when, what to do when it works, and what to do when it doesn't — and the "when it doesn't" clause isn't a dumb rule anymore. It's not "retry 3 times, email a log." InTouch AI reads the failure, smart-retries, refreshes the expired token, and surfaces the one sentence that matters. It broke. Here's why. I fixed it. No plain cron job can say that.
Everything below installs with a click and points at your own infrastructure — usually a spreadsheet, an endpoint, or a config value. Read it and adjust before you rely on it.
SSL certs expire Saturday morning. DNS gets changed by someone you don't remember authorizing. Disk fills up the day before quarter-end. Backups fail silently and you find out the day you need them. InTouch catches every one of these before they catch you.
Your team pushed 47 commits this week, opened 12 PRs, merged 9. Weekly digest of activity beats opening GitHub. Release notes drafted from commit messages. Bad commit message? Catch it on push.
Your prod logs have 3,200 ERROR lines yesterday. Half are duplicates, half are real. Scan, dedupe, surface the actually-new ones. Postmortem draft from an incident thread.
Your AWS bill jumped 40% last month and nobody noticed until the invoice. A daily Cost Explorer pull and a threshold alert catch the runaway instance while it's still cheap to kill.
You don't start from a blank page. Find it in the Hub, install it, point it at your setup, run it — in your own language. These are starting points that already work.
Given an API endpoint + expected behavior, returns a structured health read: latency, status, expected vs actual response shape.
Paste a log slice; get a deduped summary of the actual unique errors, frequencies, and likely root causes. Better than `grep ERROR | sort | uniq`.
Given an incident timeline + Slack thread, drafts a postmortem skeleton: what happened, contributing factors, timeline, mitigations, action items.
Summarize a repo's week: commits, PRs opened/merged, issues filed/closed, notable changes. Useful for weekly engineering reports.
Take a vague commit message ("fix stuff", "WIP"); return a structured conventional-commit style message based on the actual diff.
Draft a release notes section from commits between two tags. Categorize by type (features, fixes, breaking changes).
Daily check of a list of hosts; alert at 30/14/7 days before any cert expires. Sheet-backed list of hosts to watch.
Periodic check of DNS records for a list of domains. Alert on any change (A, CNAME, MX, NS). Catches unauthorized changes early.
Periodic HTTP HEAD against a list of URLs. Alert on non-200 or slow responses. Cheap external monitor.
Trust but verify: scheduled check that your backups (S3, local NAS) actually contain recent files, not just an empty success status from yesterday's cron.
Per-host disk fill alerts. SSH out, run df, alert if any mount is over threshold. Headless servers especially.
When a PR is opened, post a Claude-drafted code review comment with suggested improvements. Augments human review, doesn't replace it.
Scheduled cleanup of S3 prefixes older than N days. Prevents your dev/staging buckets from drifting to thousands of dollars/month.
Daily AWS Cost Explorer pull. Alert if today's spend trajectory exceeds a threshold (catches stuck-on EC2 instances and S3 explosion early).
Daily summary of your DB: row counts per table, growth, key metrics. Spot anomalies before customers report them.
InTouch AI runs as a single download on any machine with Java — your laptop, a server, or a container. Self-hosted from laptop to enterprise, so your passwords and logs never leave your network. Two ways in: describe what you want in plain English, or wire it up as code for the builders. Free Personal edition to evaluate.
Each workflow reads from a spreadsheet, an endpoint, or a config value. Read the short guide, set the placeholders, and point it at your infrastructure.
InTouch runs the workflow on cron-style schedules, logs every run, and alerts on your existing channels (email, Slack, Discord, Teams, PagerDuty webhook). When something breaks, InTouch AI reads the failure, fixes what InTouch AI can, and surfaces the one sentence that matters — not a stack trace. You didn't write it line-by-line, so you can't debug it line-by-line. It debugs it for you.
Personal edition is free. One small download on your dev box or homelab server. Take one example, point it at your infrastructure, and watch it save you an hour this week. And you hold the determinism dial: run a check AI-native while it earns trust, then graduate it to fully deterministic — zero AI cost per run, identical every time, fully audited. Stop configuring. Start describing.