Stop Managing Projects and Start Shipping AI Systems

Most businesses needing custom AI start by hiring a freelancer from a marketplace. The problem is that the person who can write a Python script is rarely the person who can deploy and maintain a production service on AWS. We have seen clients arrive with a GitHub link from a freelancer, but the code only runs on the freelancer's laptop. The business owner, who is not a DevOps expert, is left with a script they cannot deploy or monitor.

The alternative, an AI automation agency, introduces a different failure mode: communication overhead. You have a great discovery call with a senior partner who understands your business. That partner then hands the project to a junior project manager, who translates your needs for an offshore developer who has never used your core tools. An 11-day build for a lead routing engine can balloon into a 6-week project with 40 hours of project management fees.

The core issue is the gap between business context and code execution. When the person who understands the 'why' is not the person writing the code, the project is at risk. You spend more time managing the process than you get value from the result.

Syntora's approach prioritizes direct engagement. The senior engineer involved in initial discussions would be the one writing the production code. We would start by thoroughly mapping your existing workflow, for example, a lead routing process.

For data management, Syntora would typically use Supabase, a PostgreSQL database. This allows for storing dynamic data such as territory rules and sales rep capacity, which can be updated directly without requiring code changes.

The core routing logic would be built as a FastAPI service in Python. When an event trigger, such as a new lead webhook from a CRM like HubSpot, occurs, our service would receive the payload. We rely on Pydantic for robust data validation, ensuring that malformed requests do not destabilize the system. The territory matching and assignment logic would be designed for high efficiency, aiming for rapid processing to ensure timely notifications to sales representatives. We have experience building similar data processing pipelines using Claude API for financial documents, and apply comparable architectural principles to other document or data types.

The FastAPI service would be packaged in a Docker container and deployed on AWS Lambda. This serverless architecture offers automatic scaling, effectively handling varying traffic loads from zero to high concurrency without manual intervention. Vercel would manage the public-facing URLs for webhooks. This setup generally results in highly efficient infrastructure costs, often under $20/month for typical volumes, compared to alternative solutions.

Upon completion, Syntora delivers full source code in your company's GitHub repository, a runbook detailing the architecture, and a monitoring dashboard. This dashboard would track key metrics like processed items, error rates, and API latency, and could be configured to trigger alerts for critical issues. Typical build timelines for systems of this complexity range from 3 to 6 weeks, depending on integration points and the availability of client-provided data schemas and access.