Data Entry on Autopilot for GitHub Users
Arahi AI automates Data Entry across GitHub, cutting repetitive work so your team can focus on higher-value tasks.
47 PDFs processed today. Latest entry:
Meeting notes
- • Vendor: Riverline Co. · Term: 12 months from Apr 1.
- • Total contract value: $42,000 net 30.
- • Signed by: Theo Park (CEO, Riverline) + Daniel R. (Arahi).
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.
Vendor: Riverline Co. · Term: 12 months from Apr 1.
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 · Vendor agreements 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 PDF · Vendor agreement · Riverline.pdf.
- Agent2:44 PM
Read the transcript and extracted action items.
- Agent2:30 PM
Triggered by call end event in Granola.
Frequently asked questions
Arahi AI connects natively with GitHub to handle the full data entry workflow. The AI agent monitors GitHub events, processes data entry tasks automatically, and writes results back to GitHub — no copy-pasting or tab-switching required.
The GitHub integration automates end-to-end data entry — including data capture from GitHub, validation, routing, follow-up actions, and status updates. Every data entry step that touches GitHub can be handled by the AI agent.
Manual data entry in GitHub requires constant tab-switching, copy-pasting, and follow-up tracking. Arahi AI eliminates this by handling data entry tasks in real-time as GitHub events occur — running 24/7 with consistent accuracy and zero fatigue.
Yes. You define exactly which GitHub events start data entry workflows — new records, status changes, messages, or custom triggers. Each trigger can have conditions so data entry actions only fire when your specific criteria are met in GitHub.
The agent reads PDFs, scanned images, emails, spreadsheets, and structured forms — extracting data fields and writing them to your systems. Even handwritten forms common in github (intake, work orders, inspection reports) are processed accurately.
Validated extraction accuracy typically exceeds 98% on standardized documents — significantly better than the 4-5% error rates common with manual data entry in github environments. Edge cases below the confidence threshold are flagged for human review instead of guessed.
The data entry agent scales automatically as your GitHub activity grows. Whether you process 10 or 10,000 data entry tasks per day from GitHub, the AI handles the volume without slowdowns or additional configuration.
Yes. You can create parallel data entry workflows that respond to different GitHub events or conditions. For example, one data entry flow for new GitHub records and another for updated ones — each with independent rules and actions.
Yes. You can run data entry workflows in test mode using sample GitHub data before activating on live records. This lets you verify every data entry rule works correctly with your GitHub setup before processing real data.
No coding required. The no-code builder walks you through connecting GitHub and configuring data entry rules visually. Your team can set up, modify, and manage GitHub-based data entry workflows without any developer involvement.
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