Smarter Data Entry for GitLab Teams
Turn Data Entry into a background job. Arahi AI agents use GitLab to execute on your behalf, 24/7.
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 GitLab
Authorize GitLab and Arahi AI hooks into your issues, repos, and deployment pipelines.
Configure Dev Workflows
Define triggers for GitLab 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 GitLab 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 GitLab to handle the full data entry workflow. The AI agent monitors GitLab events, processes data entry tasks automatically, and writes results back to GitLab — no copy-pasting or tab-switching required.
Yes. You can run data entry workflows in test mode using sample GitLab data before activating on live records. This lets you verify every data entry rule works correctly with your GitLab setup before processing real data.
All data exchanged between GitLab and Arahi AI during data entry processing is encrypted in transit and at rest. We use OAuth tokens for GitLab access, never store raw credentials, and maintain full audit logs of every data entry action.
Yes. You define exactly which GitLab 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 GitLab.
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 gitlab (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 gitlab environments. Edge cases below the confidence threshold are flagged for human review instead of guessed.
Yes. The data entry agent connected to GitLab simultaneously interacts with 1,500+ other apps — CRMs, databases, email platforms, and more. A single data entry workflow can pull data from GitLab, process it, and push results to multiple destinations.
Yes. You can create parallel data entry workflows that respond to different GitLab events or conditions. For example, one data entry flow for new GitLab records and another for updated ones — each with independent rules and actions.
The data entry agent scales automatically as your GitLab activity grows. Whether you process 10 or 10,000 data entry tasks per day from GitLab, the AI handles the volume without slowdowns or additional configuration.
The dashboard shows data entry-specific metrics for your GitLab integration — tasks processed, average handling time, success rates, and escalation frequency. You can track how GitLab-triggered data entry workflows perform over time.
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