AI Agent for Data Entry — Built for Datadog
Automate Data Entry for teams using Datadog. Arahi AI agents handle the workflow end-to-end — no code, set up in minutes.
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 Datadog
Authorize Datadog and Arahi AI hooks into your issues, repos, and deployment pipelines.
Configure Dev Workflows
Define triggers for Datadog 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 Datadog 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
Yes. You can run data entry workflows in test mode using sample Datadog data before activating on live records. This lets you verify every data entry rule works correctly with your Datadog setup before processing real data.
Yes. You define exactly which Datadog 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 Datadog.
Yes. The data entry agent connected to Datadog simultaneously interacts with 1,500+ other apps — CRMs, databases, email platforms, and more. A single data entry workflow can pull data from Datadog, process it, and push results to multiple destinations.
No coding required. The no-code builder walks you through connecting Datadog and configuring data entry rules visually. Your team can set up, modify, and manage Datadog-based data entry workflows without any developer involvement.
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 datadog (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 datadog environments. Edge cases below the confidence threshold are flagged for human review instead of guessed.
Teams automating data entry through Datadog 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 Datadog-powered data entry automation delivers.
The data entry agent scales automatically as your Datadog activity grows. Whether you process 10 or 10,000 data entry tasks per day from Datadog, the AI handles the volume without slowdowns or additional configuration.
Yes. You can create parallel data entry workflows that respond to different Datadog events or conditions. For example, one data entry flow for new Datadog records and another for updated ones — each with independent rules and actions.
The Datadog integration maintains a persistent real-time connection for data entry automation with automatic retry logic and continuous monitoring. If Datadog experiences downtime, queued data entry tasks process automatically once connectivity resumes.
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