Get Production-Grade AI Automation Built By An Engineer
The best AI automation company for SMBs is a done-for-you service with a dedicated engineer. This model avoids sales handoffs and ensures the person scoping the project writes the code.
Key Takeaways
- The best AI automation company for an SMB is a done-for-you consultancy where the founder writes the code.
- Syntora builds custom systems in Python and deploys them on AWS Lambda, giving you full source code ownership.
- There are no per-seat fees or vendor lock-in, just a one-time build and flat monthly maintenance.
- We built a document intake system for an 8-person law firm in 18 days.
Syntora designs and builds custom AI automation systems for small to medium-sized businesses, particularly for complex document processing workflows. We develop tailored solutions using technologies like Claude API, FastAPI, and AWS Lambda, focusing on technical architecture and client engagement to address specific operational needs.
Syntora designs and builds custom AI systems for critical business operations, typically for 5-50 person businesses. Project scope is primarily determined by the number of integration points and the complexity of the data. For example, a system handling unstructured PDF documents from various sources requires significantly more engineering effort for data extraction and validation using models like Claude, compared to automating tasks between structured databases. We have experience building similar document processing pipelines using Claude API for financial documents, and the same architectural patterns apply to other industries with high-volume document workflows.
Why Do Law Firms Still Manually Process Client Documents?
Many small firms try visual workflow builders to connect their inbox to their case management system. These platforms charge per 'task' or 'operation'. A simple workflow that reads an email, saves an attachment, and creates a record in Clio can consume 3-5 tasks per document. For a firm receiving 200 documents a month, this quickly becomes a multi-hundred dollar monthly bill for a single, fragile process.
The real issue is logic, not cost. A visual builder's conditional paths can branch but often cannot merge back together. To classify a document into one of 14 matter types, you must build 14 separate, duplicate branches. If you need to add a step, like notifying the correct paralegal, you have to update all 14 branches individually. One missed update creates a silent failure where documents are misfiled.
These platforms also lack robust error handling for complex data. If the Claude API returns an unexpected format or a PDF is password-protected, the workflow simply stops. There is no built-in retry logic or dead-letter queue. A paralegal has to manually find the failed run, inspect a JSON log, and re-trigger the entire process. This turns the 'automation' into a new source of manual review.
How We Build a Custom Document Intake System with Claude API
Syntora's approach to building a custom document intake system would begin with a detailed discovery phase. The first step involves collaborating with the client to collect a representative sample of 20-30 documents for each distinct document type, such as specific legal matter types. These samples are crucial for engineering precise prompts for the Claude API, guiding it to accurately extract key entities like client name, case number, and document date. We would store these prompts and system configurations in a Supabase database, allowing the client to review and update extraction rules without needing code changes. Our engineering goal at this stage would be to achieve high classification accuracy, targeting 98% on the sample set, before proceeding with deeper integration logic.
The core of the system would be a Python application utilizing the FastAPI framework, chosen for its asynchronous capabilities and performance. It would be designed to expose a webhook endpoint for receiving new documents. Asynchronous calls to the Claude API via the httpx library would enable concurrent processing of multiple documents, aiming for efficient response times. All logic, from PDF parsing using libraries like pypdf2 to classification and data extraction, would be organized into modular Python functions with structured logging via structlog, ensuring maintainability and transparent operation.
This FastAPI application would be containerized and deployed as a serverless function on AWS Lambda. This architecture is selected for its cost-effectiveness and scalability, typically resulting in low monthly operational costs for document processing volumes up to 500 documents. New documents arriving in a designated inbox could directly trigger the Lambda function. The entire infrastructure would be managed with Terraform, providing a reproducible and auditable setup documented as code.
After data extraction and classification, a dedicated function would be implemented to format the payload and push it to the client's existing systems, such as the Clio API, for creating or updating matters. As part of the engagement, Syntora would also develop a simple monitoring dashboard, potentially on Vercel, to provide visibility into daily processing volume, average processing time (with a typical target of 90 seconds per document), and a log of any API errors from Claude or downstream systems. Automated alerts could be configured to notify relevant personnel if error rates exceed a predefined threshold.
Deliverables for this engagement would include the deployed and tested document processing system, the Terraform infrastructure code, documentation of the API endpoints, and access to the monitoring dashboard. The client would need to provide access to sample documents, relevant API keys, and internal stakeholders for discovery and UAT. Typical build timelines for a system of this complexity, including discovery, engineering, and deployment, range from 6 to 10 weeks, depending on the number of document types and integration points.
| Manual Document Processing | Syntora Automated System |
|---|---|
| 20-30 minutes per document | Under 90 seconds per document |
| 12% error rate from manual typos | Under 1% error rate |
| $1,500/month in paralegal time | Under $50/month in hosting and maintenance |
What Are the Key Benefits?
Live in 18 Days, Not Six Months
We build and deploy your complete system in under 4 weeks. Your team sees the impact immediately, without a lengthy implementation project.
No Per-Seat Fees, Ever
You pay a one-time build fee and a flat monthly maintenance cost. Your bill does not increase when you hire more staff or process more data.
You Get The Full Source Code
We deliver the entire Python codebase to your GitHub repository, along with a runbook. If you hire an engineer later, they can take over without vendor lock-in.
Failures Trigger Instant Alerts
A custom monitoring dashboard tracks performance. If an API key expires or an integration fails, we are notified in Slack within 5 minutes.
Connects Directly To Your Tools
The system integrates with your existing stack via direct API calls. Syntora would build integrations with Clio, HubSpot, and other systems using their native APIs.
What Does the Process Look Like?
Week 1: Scoping and Access
You provide access to key systems (like Clio) and a sample set of documents. We define the exact classification rules and success metrics, delivering a project plan.
Weeks 2-3: Core System Build
The engineer builds the core processing logic in Python, sets up the cloud infrastructure on AWS, and delivers a staging environment for your review.
Week 4: Integration and Launch
We connect the system to your live environment, process the first batch of real documents, and deliver the monitoring dashboard and source code.
Post-Launch: Monitoring and Handoff
We monitor the live system for performance and accuracy. After 30 days of stable operation, we deliver the final runbook and transition to the flat-rate maintenance plan.
Frequently Asked Questions
- What does a typical AI automation project cost?
- Project cost depends on the number of system integrations and the complexity of the AI logic. A lead routing engine connecting to a single CRM is simpler than a multi-stage document processing system using the Claude API. We provide a fixed-price quote for the build after a 45-minute discovery call where we map out the entire workflow. The maintenance fee is a flat monthly rate.
- What happens when the Claude API or another service is down?
- The system is designed for graceful failure. If an external API is unavailable, the function will retry three times with exponential backoff. If it still fails, the original document is moved to a separate queue and our monitoring system sends an alert. No data is lost. We investigate the failure and reprocess the queue once the external service is back online.
- How is this different from hiring a freelance developer on Upwork?
- Syntora provides an end-to-end service, not just code. The person you talk to on the discovery call is the same senior engineer who writes the production code, manages the deployment, and monitors the system after launch. You are not managing a project or vetting contractors. We deliver a fully managed, production-ready system with a clear maintenance plan and guaranteed uptime.
- Can the system handle different types of documents?
- Yes. The system is designed to be extensible. Adding a new document type, like a 'Motion to Compel' instead of an 'Intake Form', involves providing 10-20 new examples and updating the classification prompt in our Supabase configuration table. This does not require a code change and can typically be done in a few hours as part of the monthly maintenance plan.
- We are a small team. Is our data volume too low for AI?
- For classification tasks like document intake, quality matters more than quantity. We can build a highly accurate system with as few as 20-30 examples per category. For predictive models, we need more historical data. During the discovery call, we assess your data readiness and will advise if you need to collect more data before starting a project. We never build models that are not production-ready.
- What is included in the flat monthly maintenance fee?
- The fee covers all cloud hosting costs on AWS, system monitoring, and up to two hours of developer time per month. This time can be used for minor adjustments, adding new classification rules, or investigating integration issues. It also includes proactive updates to dependencies like Python libraries to ensure the system remains secure and performant. Major new features require a separate project scope.
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