Build a Custom AI Agent System for Your Business

Many businesses attempt to automate workflows using visual builders or basic scripting. These tools are effective for simple A-to-B data transfers, like creating a Salesforce contact from a Typeform submission. They fail when a process involves multiple decisions and state changes. Their linear, trigger-action model cannot handle a workflow that needs to check three different APIs, pick the best option, wait for a human to approve a quote, and then act on that approval hours later.

Consider a 15-person freight brokerage. A customer emails a request for a quote. A human broker reads the email, extracts the origin, destination, and pallet count. They log into three different carrier portals, re-enter the same data three times, and compare the rates. This process takes 15 minutes per quote. The broker then emails the best option back to the customer. This manual work is slow, error-prone, and prevents them from handling more than 30 quotes per day.

A simple automation tool might parse the initial email. But it cannot log into three separate carrier portals, especially if one requires multi-factor authentication. It cannot intelligently compare the structured data returned from each portal. It also cannot manage the state of the quote. If the customer replies 'book it' 4 hours later, the original rate may have expired. A stateless workflow tool has no memory of the original quote and cannot re-verify the rate before booking.

The structural problem is that these platforms are built for stateless, one-way data flows. They lack a persistence layer to track a job over hours or days. They also lack the procedural logic to handle branching, looping, and error recovery needed for multi-step tasks. An agent system, by contrast, is built around a state machine. The system knows a quote is 'pending_approval' and has a dedicated function to execute when the state changes to 'approved', giving it the memory and flexibility that simple automation lacks.

The first step is a workflow audit. We map every decision point, manual step, and external system in your current process. For a freight brokerage, this means documenting the exact fields needed by each of your top 5 carrier portals and how you handle exceptions. This audit produces a state diagram and a technical specification which you approve before any code is written.

Syntora's approach is based on our own multi-agent platform, built with FastAPI and Python. An orchestrator, using a state machine framework like LangGraph, routes tasks to specialized agents using a fast model like Gemini Flash, which has a sub-200ms response time. We persist state in a Supabase Postgres database, which allows workflows to pause for hours and resume correctly. This architecture, deployed on DigitalOcean App Platform, can process over 500 transactions a day for under $40/month in hosting costs. Human escalation is handled via a webhook with a response time under 1 second.

The final system is a containerized application deployed to your cloud account. You receive the complete source code in your own GitHub repository, a runbook detailing deployment and maintenance, and a simple dashboard for monitoring agent activity. The system integrates with your existing tools, for example, by watching a specific Gmail inbox for new requests or posting updates to a dedicated Slack channel using Server-Sent Events (SSE) for real-time updates.