Automate Internal Admin Tasks with Multi-Agent AI

Small professional services firms often manage Statements of Work (SOWs) with Google Docs templates and an e-signature tool like DocuSign. While these tools handle the final signature, they are completely disconnected from the sales process. The deal's financial terms are in Salesforce, but the client's actual verbal commitments and specific needs are buried in a 60-minute Gong call recording.

Consider a 25-person consulting firm that produces 50-60 SOWs per year. A partner closes a deal, promising a specific performance guarantee on a sales call. The operations manager, who was not on the call, drafts the SOW using the standard template and CRM notes, completely missing the custom guarantee. The draft goes to the client, who flags the omission. This kicks off a 3-day delay while the partner re-listens to the entire call to verify the terms, slowing down the cash-to-close cycle.

This isn't a failure of the operations manager or the CRM; it's a structural data problem. Sales context exists in an unstructured, conversational state in call recordings. Contractual obligations must exist in a structured, legally sound format. Tools like PandaDoc or Qvidian are presentation layers, not data integration engines. They cannot listen to a call, extract commitments, and check for contradictions against a stored MSA.

The result is a single-person bottleneck, typically a senior partner or operations lead, who must manually review every single SOW before it goes out. This review consumes 3-4 hours of a high-value employee's time per document, introduces a high risk of human error, and delays revenue recognition.

Our process begins with mapping the complete flow of information, from the first discovery call to the final signed contract. We audit the connection points between your CRM (Salesforce, HubSpot), your call recording platform (Gong), and your document repositories. This audit produces a clear diagram showing exactly where data is being manually re-entered and where critical client commitments are being lost in translation.

For a professional services client, the technical approach would use a multi-agent system built in Python. One agent, using the Claude API, connects to Gong to extract key terms, scope items, and pricing directly from call transcripts, a process taking under 90 seconds for a 1-hour call. A second agent retrieves your MSA from a database to fetch standard legal clauses. A third agent then compares the extracted terms against the MSA rules, flagging any commitments from the call that conflict with your standard agreement. A FastAPI service orchestrates these agents to generate a structured JSON object representing the final SOW.

We built a similar system for our own internal proposals, which generates client-ready documents from Fireflies transcripts and publishes them via Supabase. We also deployed a JSON-driven SOW generator that produces print-ready HTML with dynamic clauses. Your delivered system would be a lightweight service, hosted on AWS Lambda for under $20/month, that connects to your existing tools and delivers a final SOW for review in under 30 minutes, removing the partner-level bottleneck.