Synthesize interviews, draft client deliverables, and chase the right loose ends.
Reads call transcripts and research, clusters insights by theme, drafts the next deliverable in your firm's voice, and reminds the team of the open questions before the next client meeting.
Interview synth done. Theme summary written:
Meeting notes
- • Top pain: revenue forecasting accuracy (~30% error mid-quarter).
- • Root cause: 7 disconnected data sources · no daily refresh.
- • Quick win: dashboard prototype before next QBR.
How does OpenAI (ChatGPT) work for consulting teams?
OpenAI (ChatGPT) works for consulting teams as the engine behind an Arahi AI agent built around the workflows that actually consume your week. The agent reads context from OpenAI (ChatGPT) and the other systems your consulting operation depends on, runs the routine work in the background, and surfaces only the cases that need a human decision. Automate repetitive tasks and free up your consulting team to focus on high-value strategic work. Teams typically see higher consultant hours captured once the agent is in production. Setup is no-code, every action is auditable, and the agent is scoped to the rules your consulting team defines — not a generic template applied to your business.
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 OpenAI (ChatGPT)
Authorize OpenAI (ChatGPT) in your Arahi AI dashboard. The secure connection takes less than 60 seconds.
Configure Your AI Agent
Set up triggers, actions, and conditions specific to how your team uses OpenAI (ChatGPT). For consulting teams, this typically means routing workflows from tools like Salesforce alongside OpenAI (ChatGPT).
Deploy & Monitor Results
Your AI agent goes live immediately. Track tasks automated, time saved, and accuracy metrics in real-time.
Top pain: revenue forecasting accuracy (~30% error mid-quarter).
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 Notion · Insights tracker 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 Client interview · COO, Beacongrid.
- Agent2:44 PM
Read the transcript and extracted action items.
- Agent2:30 PM
Triggered by call end event in Granola.
Frequently asked questions
Client interviews → theme synthesis → deliverable drafts. The agent handles the synthesis layer between raw notes and the structured client output your team produces.
It learns from 30–50 of your past deliverables. New drafts match your structure, terminology, and level of analytical depth.
Each client's data is isolated. The agent never references one client's information when drafting for another. This is enforced at the data layer, not just policy.
Yes — Notion, Asana, Monday, ClickUp, and direct file storage (Google Drive, SharePoint, Box). Drafts land where your team already works.
It drafts them in your engagement lead's voice, with the project status pulled from your latest call and document. Sent only after human review.
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