Find a Chicago AI Developer Who Writes Production Code

Many AI development agencies in Chicago assign projects to junior developers overseen by non-technical project managers. The founder you meet in the sales process disappears after the contract is signed. Communication gets filtered through multiple layers, leading to misunderstandings about technical requirements and slow feedback loops.

For example, a 15-person logistics company needed to automate their invoice processing. They hired an agency that promised a 6-week turnaround. The project stretched to 12 weeks because the offshore development team misunderstood the specific validation rules for their top 5 clients, causing a 30% error rate at launch. The Chicago-based project manager could only relay messages back and forth.

This model fails because the person with the most context (the client) is separated from the person writing the code by at least two layers of management. Every technical question becomes a game of telephone. The result is a system that technically meets the spec sheet but fails to solve the actual business problem.

Syntora's engagement would begin with a detailed discovery process to map your exact workflow and define requirements. For a document intake system, this would involve collaborating to define specific matter types and associated keywords. Based on these requirements, we would design a technical architecture, typically involving a Vercel-hosted frontend, a FastAPI backend on AWS Lambda, and a Supabase database for storing classifications and metadata.

The core logic would be developed in Python. A document parser using the PyMuPDF library would extract text from scanned PDFs. This extracted text would then be sent to the Claude 3 Sonnet API with a carefully crafted prompt for classification. Syntora has built document processing pipelines using the Claude API for financial documents, and the same pattern applies to legal documents. The system would then write the classification result, including matter type and confidence score, to Supabase. While specific performance varies by document complexity, typical processing times for similar systems are within seconds per document.

The system would be designed to integrate with your existing tools. For a legal practice, this would include using the Clio API to create a new matter and automatically upload the classified document. Deployment would typically use serverless functions for scalability and cost efficiency, with infrastructure costs often being minimal for moderate document volumes.

Upon completion, you would receive the full source code in your private GitHub repository. Syntora would provide a runbook with API documentation and would set up a monitoring dashboard, for instance in Grafana. This dashboard would track metrics such as API latency, Claude API costs, and classification accuracy, with configurable alerts sent to Slack if thresholds are exceeded.