Automate Your B2B Prospecting with Custom Claude Agents

A sales team often starts with a tool like Apollo.io for lead sourcing and Instantly for cold email. They export a CSV from Apollo, clean it in Google Sheets to remove bad fits, and upload it to Instantly. This is a 3-hour manual process repeated weekly, prone to human error.

Trying to connect these with a simple webhook is brittle. Apollo's API might return a lead with a generic title like "Co-founder" which needs a custom lookup to confirm their role fits the ICP. A basic webhook just passes this junk data through, resulting in outreach to unqualified prospects.

Platforms that chain API calls often fail on state management. An effective prospecting workflow needs to check if a prospect already exists in the CRM (e.g., Pipedrive), check if they have been contacted in the last 90 days, and only then add them to a sequence. These checks require conditional logic and data lookups that become a tangled mess of paths and filters, costing dozens of tasks per lead and failing silently when an API times out. These tools treat prospecting as a linear pipe, but real prospecting is a state machine. Off-the-shelf tools cannot manage this state across multiple systems, leading to duplicate outreach and missed follow-ups.

Syntora's engineering approach starts with defining the Ideal Customer Profile (ICP) as a set of rules within a Python configuration. The core of the system would be a supervisor agent built with LangGraph, which orchestrates specialized sub-agents. One agent would source leads from the Apollo.io API, another would enrich data using PeopleDataLabs, a third would qualify against the ICP using Claude, a fourth would check for duplicates in your CRM via its REST API, and the last would format the lead for an outreach tool like Instantly. Syntora's experience building similar data processing and orchestration pipelines using Claude API for sensitive financial documents informs the design of these multi-agent systems, where accuracy and robust error handling are critical.

The entire workflow would be managed as a state machine. Syntora would implement Supabase with a PostgreSQL backend to track each prospect's state. For example, when a "Sourcing" agent finds leads, it would write them to a `prospects` table with a `sourced` status. A separate process, potentially triggered by a cron job, would pick up these leads, run them through the "Qualification" agent using Claude 3 Haiku for speed, and update their status to `qualified` or `disqualified`. This design prevents data loss if one step fails.

The system would be architected as a FastAPI application, deployable on platforms like AWS Lambda. This design helps manage hosting costs and provides scalability. The system would be event-driven; a webhook from your CRM on a "Closed-Won" deal could trigger a new search for lookalike accounts. API calls to external services like Apollo would be made with httpx for efficient asynchronous performance.

Syntora would design a human-in-the-loop escalation path. If Claude cannot confidently determine if a lead fits the ICP, it would flag the lead with a `needs_review` status. These could be surfaced in a custom dashboard, such as one built with Retool, where a human can approve or reject them. Syntora would configure structured logging with structlog and establish alerts for API failures, piping them directly to a shared communication channel like Slack. For systems of this complexity, Syntora typically estimates an initial build timeline of 6-10 weeks from discovery to initial deployment, depending on the client's specific integration requirements and readiness to provide necessary API access and detailed ICP criteria. Deliverables would include the deployed system, source code, and comprehensive documentation.