Build AI Tools to Automate Your Sales Pipeline

Most teams start with their CRM's built-in automation, like HubSpot Workflows. These tools are great for simple triggers, like sending a template when a deal stage changes. But they cannot understand conversational context. They cannot read a meeting transcript to generate a unique summary or identify a customer's primary objection.

A 10-person sales team might look at conversation intelligence platforms like Gong, but the per-seat pricing is designed for larger organizations. This leaves teams trying to stitch together multiple services. A common attempt is to use Zapier to connect a call recording folder in Google Drive to an AI transcription service, then to an AI model for summarization, and finally back to the CRM. This workflow fails for two reasons.

First, Zapier has processing limits and timeouts not designed for large files like 30-minute audio recordings. The workflow often breaks without a clear error message. Second, the per-task pricing becomes extremely expensive at scale. A single call can consume 4-5 tasks. For a team handling 200 calls a week, this translates to nearly 1,000 tasks, pushing a Zapier bill into hundreds of dollars monthly for just one automated process.

Syntora would start by engaging with your team for discovery, identifying key integrations and process requirements. We would connect to your source systems using their native APIs, for example, the HubSpot API for deal data, an AWS S3 event trigger for new call recordings, and the Google Calendar API for meeting context. This process allows us to establish a clear picture of your sales interactions and analyze existing call data to understand your unique sales language and patterns.

The core of the system would be an event-driven pipeline built on AWS Lambda. When a new MP3 file indicating a call recording lands in S3, a Python function would trigger. This function would send the audio to a transcription service like AssemblyAI, which typically returns a timestamped transcript quickly. The resulting text would then be fed to the Claude API with a custom prompt engineered to extract specific elements like action items, customer objections, and budget discussions. Our work on AI product matching and document processing pipelines has given us significant experience in achieving precise structured output parsing from complex text.

The structured JSON output from Claude would then be passed to a FastAPI service. This service could update the corresponding deal record in HubSpot with a concise summary and action items, and also call the Gmail API to create a personalized follow-up email draft for the sales rep. Pydantic would be used to ensure all data moving between services is correctly formatted, a pattern we apply across our production systems.

Deployment of such a system would typically be on your AWS account, using the AWS CDK for infrastructure-as-code. Syntora can also design and build a simple dashboard, potentially using Streamlit, to provide call analytics and insights. Access management would be implemented, for example, through Supabase with role-based permissions, to ensure secure operation.