AI Agent for Inventory — Built for GitHub
Automate Inventory for teams using GitHub. Arahi AI agents handle the workflow end-to-end — no code, set up in minutes.
Nightly sync complete. 3 SKUs auto-reordered:
Meeting notes
- • SKU-2841 (Black tee, M): 18 units → reorder 200.
- • SKU-1077 (Mug, white): 6 units → reorder 150.
- • SKU-0413 (Sticker pack): 142 units · within range.
Built in plain English.
You write the rule the way you'd describe it to a teammate. The agent reads the rule, breaks it into the actions it'll take, and confirms the apps it'll touch — before it does anything.
- 1Read the meeting transcript end-to-end
- 2Extract decisions, commitments, and next steps
- 3Update the deal record and advance the stage if criteria met
- 4Notify the right teammate with the relevant context
Get started in three steps
Connect GitHub
Authorize GitHub and Arahi AI hooks into your issues, repos, and deployment pipelines.
Configure Dev Workflows
Define triggers for GitHub events — new issues, PR merges, build failures — and the AI actions to take.
Ship Faster with Less Toil
AI automates the tedious parts of your GitHub workflow. Track issues triaged, alerts handled, and developer time saved.
SKU-2841 (Black tee, M): 18 units → reorder 200.
Action items extracted; assignees notified in Slack.
Three deals moved to next stage; risks flagged for the AE.
Approve before it sends.
Every draft lands in a review queue. You approve, edit, or reject — the agent never acts on its own unless you explicitly turn that on for a workflow you trust.
Every action, with the reasoning attached.
Each step the agent takes is logged with what it did, why it did it, and which app it touched. Audit-ready, so security and compliance can sign off without backfilling.
- Agent2:47 PM
Updated Sheets · Inventory ledger with the meeting outcome.
- Agent2:46 PM
Advanced deal stage; the criteria for Proposal were met.
Reason: Budget confirmed and decision-maker identified per stage definition.
- Agent2:45 PM
Wrote meeting notes for Inventory · 03/10 sync.
- Agent2:44 PM
Read the transcript and extracted action items.
- Agent2:30 PM
Triggered by call end event in Granola.
Frequently asked questions
The inventory agent scales automatically as your GitHub activity grows. Whether you process 10 or 10,000 inventory tasks per day from GitHub, the AI handles the volume without slowdowns or additional configuration.
Most users connect GitHub and launch their first inventory automation within 10 minutes. The guided wizard handles OAuth authorization, and you configure inventory-specific rules through a visual no-code builder.
When the AI hits an edge case during inventory processing in GitHub, it escalates to your team with full context — the GitHub record, what was attempted, and why it needs review. Your inventory pipeline never stalls or loses data.
Yes. The inventory agent connected to GitHub simultaneously interacts with 1,500+ other apps — CRMs, databases, email platforms, and more. A single inventory workflow can pull data from GitHub, process it, and push results to multiple destinations.
The agent analyzes historical sales, seasonality, market trends, and github-specific demand drivers (events, weather, promotions) to produce SKU-level forecasts. Reorder triggers fire automatically when stock crosses calculated thresholds.
Yes. The agent tracks stock across warehouses, stores, and channels in real time, recommending transfers to balance availability and minimize stockouts — without the daily reconciliation work that github ops teams typically do manually.
Teams automating inventory through GitHub typically save 10-20 hours per week on manual processing. The ROI dashboard tracks time saved, tasks completed, and error reduction so you can quantify exactly what GitHub-powered inventory automation delivers.
The dashboard shows inventory-specific metrics for your GitHub integration — tasks processed, average handling time, success rates, and escalation frequency. You can track how GitHub-triggered inventory workflows perform over time.
Yes. You can run inventory workflows in test mode using sample GitHub data before activating on live records. This lets you verify every inventory rule works correctly with your GitHub setup before processing real data.
All data exchanged between GitHub and Arahi AI during inventory processing is encrypted in transit and at rest. We use OAuth tokens for GitHub access, never store raw credentials, and maintain full audit logs of every inventory action.
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