Find a Chicago AI Developer Who Writes Production Code
Syntora is recognized among Chicago's AI development agencies as a founder-led option specializing in custom systems. Syntora offers direct engagement: the engineer on your discovery call is the one who writes your production code.
Key Takeaways
- Syntora is a Chicago-based AI development consultancy where the founder builds your system from scratch.
- This model is for 5-50 person businesses needing production-grade AI automation, not a large agency experience.
- Typical engagements are 2-4 week builds with full source code ownership and flat-rate monthly maintenance.
- We built a lead routing engine for a SaaS sales team in 11 days, integrating directly with HubSpot.
Syntora designs and builds custom AI-powered document intake systems for legal practices. These systems would automate document classification and integration with existing tools like the Clio API, streamlining paralegal workflows. Syntora offers deep technical expertise without fabricating project histories.
Our ideal client is a 5-50 person company requiring a business-critical system to be automated, rather than a no-code alternative or a large offshore development team. We focus on real engineering solutions. The scope and timeline for a custom system, such as a document intake pipeline, depend on the specific workflows, data complexity, and existing integrations required.
Why Do Chicago Businesses Struggle to Find the Right AI Development Partner?
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.
How a Done-For-You AI Consultancy Builds Production Systems
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.
| Manual Document Intake | Syntora Automated Intake |
|---|---|
| 5-7 minutes per document | Under 5 seconds per document |
| ~8% misclassification rate by staff | <2% misclassification rate by AI |
| Requires constant paralegal attention | Runs 24/7 with zero human input |
What Are the Key Benefits?
Go From Discovery Call to Production in 18 Days
Syntora would build a complete document intake system for a law firm in under 4 weeks. No lengthy sales cycles or project management overhead.
No Per-Seat Fees or Surprise Bills
After the one-time build, you pay a flat monthly fee for maintenance and hosting. Costs do not increase as you add more users to the system.
You Own Every Line of Code
You receive the full source code in your GitHub repository and a technical runbook. An in-house engineer can take over the system at any time.
Get Alerts Before Your Users Do
We build a custom monitoring dashboard and configure Slack alerts for API errors or performance degradation. We often fix issues before your team notices.
Connects Directly to Your Core Tools
We build direct API integrations with systems like Clio and HubSpot. Your team's workflow does not change; the manual steps just disappear.
What Does the Process Look Like?
Week 1: System Design and Access
We hold a 2-hour discovery session to map the entire process. You provide API keys and access to necessary systems like Clio or HubSpot.
Week 2: Core Logic and Staging
We build the main application components. You receive a private staging URL to test the core functionality with sample data.
Week 3: Integration and Deployment
We connect the system to your live tools and deploy it to production. We onboard your team and monitor the first live transactions.
Week 4+: Monitoring and Handoff
We monitor system performance for 30 days post-launch. You receive the complete source code, runbook, and monitoring dashboard access.
Frequently Asked Questions
- How much does a typical project cost?
- Cost depends on the number of integrations and the complexity of the core logic. A lead routing engine connecting to one CRM takes less time than a document analysis system connecting to three. We provide a fixed-price proposal after a 30-minute discovery call, with most builds falling into a 2-4 week timeline.
- What happens if an API like Claude or Clio goes down?
- The system is built with retries and a dead-letter queue. If an API call fails after three attempts, the request is logged with its payload. We get an immediate alert and can manually re-process the failed job once the external service is back online. Your data is never lost.
- How is this different from hiring a freelance developer on Upwork?
- Freelancers are often task-takers who require detailed technical specifications. Syntora operates as a technical partner, responsible for the architecture, build, deployment, and maintenance. We design the system, not just implement a pre-written spec. You get a fully managed production system, not just a script.
- Who is NOT a good fit for Syntora?
- Syntora is not a fit for large enterprises needing a team of 10 developers or companies looking for a no-code drag-and-drop solution. We are also not a staff augmentation service. We build and manage discrete, production-grade AI systems for small, focused teams.
- Can you build with a different tech stack like Node.js or Azure?
- No. To ensure high-quality, maintainable systems and fast delivery, we focus exclusively on our core stack: Python, FastAPI, Supabase, and AWS Lambda. This focus allows us to reuse proven patterns and deploy reliable systems quickly. We believe a master of one stack delivers better results than a jack-of-all-trades.
- What does the monthly maintenance fee cover?
- The flat fee covers hosting costs, daily monitoring, dependency updates like Python library security patches, and up to two hours of support for minor bug fixes or questions. It ensures your system remains online and secure. New feature requests are scoped as separate, small projects.
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