Your First Steps to AI Transformation

Many businesses start by purchasing an off-the-shelf AI platform. These tools promise a total solution, but they come with high per-seat subscription fees and require a dedicated person to configure and maintain them. A 10-person marketing agency might buy a $2,000/month AI suite just to generate ad copy variants, a task a simple Claude API script could handle for pennies.

Another common mistake is hiring a large consulting firm. Their playbook is designed for Fortune 500 companies. They will spend six weeks on discovery and deliver a 100-page strategy document, charging five figures without writing a single line of code. This approach is too slow and abstract for a small business that needs a working solution, not a PowerPoint deck.

These approaches fail because they treat AI as a massive, top-down initiative. A small business does not have the budget or headcount to support a general-purpose AI platform. They need to solve a specific, painful problem with a tool that works immediately and does not create new operational overhead.

Syntora would approach an automation challenge by first working with your team to identify the most expensive manual process within your business. This initial discovery includes mapping the exact steps of the workflow, such as opening an email attachment, identifying document types, extracting key details, and updating an existing management system.

We would design a processing pipeline using a large language model API, like Claude 3 Sonnet, for its capability to extract structured data from unstructured text. An AWS Lambda function would be triggered whenever a new document or email arrives. This function would read the text, call the chosen API to extract a JSON object with relevant fields (e.g., claimant_name, policy_number, incident_type), and then validate the output against predefined rules.

We have built document processing pipelines using Claude API for financial documents, and the same pattern applies to other industry documents like claims reports or applicant resumes.

The extracted structured data would then be used to make an API call directly to your existing management system. Syntora would develop a small FastAPI service to handle authentication and data mapping, ensuring the new records are created accurately within your current infrastructure.

The proposed system would not include a complex custom dashboard. Instead, any document the AI cannot process with high confidence would be forwarded to a designated channel for human review, allowing your team to focus only on exceptions. The system would use structlog for logging, writing failures to a Supabase table for debugging and monitoring. The delivered system would be production-ready and integrated into your current operations, along with documentation and knowledge transfer for your team.