Building a Custom AI Agent for Your Internal Operations

Many 5-50 person businesses run on a mix of spreadsheets, email, and disconnected SaaS tools. An operations manager might use a Google Sheet to track new client onboarding. When a signed contract arrives as a PDF in their inbox, they manually extract the client's name, start date, and service level. Then, they copy this information into three separate systems: Asana for project setup, QuickBooks for invoicing, and HubSpot for the customer record.

This manual process is slow and brittle. The Google Sheet offers no real data validation, so a typo in a client name can cause reporting errors downstream. If Asana changes its interface, the manual workflow must be re-learned. There is no central audit trail; if an invoice is wrong, it takes 30 minutes of digging through emails and spreadsheets to find the source of the error. Off-the-shelf tools that promise to connect these apps often fail at the most critical step: interpreting the unstructured data from the initial PDF.

The core issue is architectural. These point-to-point connections and manual processes lack a central layer of intelligence. They can move structured data from one known field to another, but they cannot read a 10-page contract, identify the three key terms, and then apply conditional logic based on those terms. The business logic lives in the operations manager's head, not in a system that can execute it reliably 24/7.

This reliance on manual data entry creates a ceiling on growth. The business can only onboard as many clients as one person can manually process. Hiring another person doubles the cost but also doubles the potential for human error, creating more cleanup work. The process itself is the bottleneck.

The engagement begins with a process audit. Syntora maps the entire manual workflow, from the moment a document arrives to its final destination in all target systems. This step identifies every field to be extracted, every business rule to be applied, and every exception that needs to be handled. The audit produces a detailed scoping document that confirms the technical plan before any code is written.

The technical solution is a lightweight, serverless system. For document processing, the Claude API is used to extract structured data from unstructured text like PDFs or emails. A Python service running on AWS Lambda contains the core business logic, transforming the extracted data to fit the requirements of each destination system. This service uses the official APIs for tools like QuickBooks and Asana, ensuring reliable connections that do not break when user interfaces change. A FastAPI wrapper can provide an endpoint for real-time processing, often with a response time under 2 seconds.

The delivered system is an automated workflow triggered by a simple action, like forwarding an email to a dedicated address. The agent reads the attachment, creates the necessary records in your other systems, and sends a confirmation email back to the operations manager with a summary of actions taken. Hosting costs for such a serverless function on AWS Lambda are typically under $30 per month for processing hundreds of documents. The entire build, from discovery to deployment, is often completed in under 4 weeks.