Build AI Agents to Automate Your Business-Critical Workflows

Many businesses try to automate workflows with visual, point-to-point connectors. These tools are great for simple tasks like 'when a form is submitted, create a CRM record.' They fail when the workflow requires logic, memory, or multiple steps that depend on each other. Their error handling is often just 'stop and send an email,' which leaves you with a broken process and a full inbox.

A typical scenario is lead processing for a B2B service firm. A lead fills out a form. A human then manually checks the CRM for duplicates, searches LinkedIn for company size, cross-references a Google Sheet of target accounts, and routes the lead to the right person in Slack. This takes 15 minutes of skilled work for every single lead, which at 20 leads per day is over 3 hours of manual effort.

A visual workflow builder can't replicate this. It can trigger on the form fill, but it cannot dynamically decide to search LinkedIn only if the email address is not a free provider. It cannot merge a new lead with an existing CRM contact based on custom rules. The result is a brittle chain of tasks that breaks silently and cannot handle the complexity of real business logic.

Syntora begins each engagement by understanding your specific workflow and business rules. We would then design a custom system, mapping your workflow into a state machine using Python and LangGraph. This architecture frames each distinct step, such as enriching a lead, checking a CRM, or routing to a sales representative, as an independently testable node. This design allows the agent to manage loops, retry failed steps, and orchestrate specialized sub-agents. All process state would be persisted in a Supabase Postgres database, ensuring that even multi-step processes can pause and resume without losing context.

To handle distinct tasks, Syntora would build specialized sub-agents using APIs like the Claude API. For example, one sub-agent might take a company domain to find firmographic data, while another queries HubSpot to identify and merge duplicate contacts. A supervisor agent would then orchestrate these sub-agents, passing information and deciding the path based on the results. This modular approach ensures the system is maintainable and adaptable as your needs evolve.

Deployment for such a system would typically involve a FastAPI application on AWS Lambda, triggered by a webhook from your website form or another event source. When a new lead arrives, the webhook would initiate the supervisor agent's workflow. This serverless architecture is designed for scalability and cost efficiency, adapting to your operational volume.

Syntora's design includes robust error handling and a human-in-the-loop escalation path. If an agent encounters an unhandled situation, it would not simply fail. Instead, it would package its current state, a summary of its actions, and its recommended next step into a detailed notification. A human operator could then review, approve, or redirect the agent, ensuring business continuity while also providing data to refine the system for future edge cases.