Stop Fighting Off-the-Shelf Tools. Get a Custom AI Process Built.
Yes, hiring an AI automation consultancy is worth it when off-the-shelf tools cannot handle your core business logic. A custom-build gives you a production-grade system you own completely, without per-user fees.
Syntora offers custom AI automation consulting, specializing in intelligent document processing and data extraction. We design and build tailored systems that automate complex, manual workflows, ensuring data accuracy and secure integration with existing business applications. Our approach focuses on delivering production-grade systems that clients own completely, without per-user fees.
The complexity of a custom process rebuild depends on the number of systems to integrate and the specifics of your rules. A workflow that connects two modern APIs with clear logic is a 2-week build. A project involving legacy systems or unstructured data like PDFs requires more discovery and development time.
Syntora designs and builds custom AI-powered automation solutions. We have experience building document processing pipelines using Claude API for sensitive financial documents, and the same architectural patterns apply to automating data extraction from various industry-specific forms and unstructured text. Our focus is on delivering secure, auditable, and maintainable systems tailored to your specific operational needs.
What Problem Does This Solve?
Most small businesses first try point-and-click automation platforms. These tools are great for simple A-to-B connections, but they break down when faced with complex, multi-step logic. Their conditional paths often cannot merge, forcing you to build duplicate, hard-to-maintain branches that burn through your task allowance on every run.
A regional insurance agency with 6 adjusters tried to automate their claims intake process. New claims arrived as PDFs attached to emails. Their CRM's automation module could trigger on a new email, but it could not read the PDF attachment. It could only parse the email body, which missed 90% of the required information. Every single claim still required manual review and data entry, defeating the purpose of the automation.
Their next step was hiring a freelancer to write a script. The script worked for one specific PDF layout from a single carrier. When a slightly different form arrived, the script failed silently, losing the claim entirely. There was no error handling, no logging, and no monitoring. It was a temporary fix, not a reliable business system that the company could depend on.
How Would Syntora Approach This?
Syntora would start an engagement by auditing your existing process and analyzing 15-20 examples of your source documents or data inputs. We would work with your team to define the key data fields required, such as policy number, claimant name, and date of loss, establishing a precise data map that forms the foundation of the system. This initial discovery phase is crucial for ensuring the solution accurately reflects your business logic and integrates effectively with your operations.
Next, we would design and build the core processing engine. The system would use Python libraries like PyMuPDF to extract raw text from documents. We would then develop a prompt for a large language model, such as the Claude 3 Sonnet API, to reliably extract the defined fields from the raw text and structure them as JSON. This prompt chain would be engineered to handle variations in form layout without needing extensive rule sets. We would wrap this extraction logic in a FastAPI service, providing a clear API endpoint for processing. FastAPI handles synchronous and asynchronous requests efficiently, ensuring the core service is responsive. All operations within the service would be logged using structlog for clear, machine-readable audit trails.
The FastAPI application would be deployed as a container on a serverless platform, typically AWS Lambda, fronted by an API Gateway endpoint. This architecture allows for cost-effective, usage-based billing, scaling automatically with demand. We would integrate the endpoint with your existing systems, such as an email provider to trigger processing from new attachments, or directly with your enterprise resource planning (ERP) system. Extracted data would be sent to your ERP via a direct API call, using libraries like httpx for robust communication.
For system monitoring and reliability, the system would store every processed file and its JSON output in a database like Supabase for a configurable period, typically 30 days. We would configure CloudWatch alarms to provide alerts via Slack or other communication channels if error rates exceed defined thresholds or if processing times indicate an issue. The client would receive the full source code in their private GitHub repository, along with a runbook detailing maintenance procedures and troubleshooting guides. A typical build of this complexity takes 4-8 weeks, depending on data variability and integration points.
What Are the Key Benefits?
Launch in 3 Weeks, Not 3 Quarters
Go from a manual process to a production-ready AI system in 15 business days. Your team sees the benefit immediately, not after months of development.
A Fixed Price, Not a Rising Subscription
One fixed-price build with an optional flat monthly maintenance plan. You are not paying a per-seat fee that punishes you for growing your team.
You Own the System and the Code
We deliver the full Python source code to your GitHub account. The system runs on your cloud infrastructure. There is no vendor lock-in, ever.
Reliability is Built In, Not Bolted On
With structured logging via structlog and real-time monitoring in CloudWatch, you have a production-grade system, not a fragile script.
Connects Your Existing Tools
We build direct API integrations to your CRM, ERP, and other core platforms. Your team works within their existing software, no new tabs to open.
What Does the Process Look Like?
Discovery and Scoping (Week 1)
You provide documentation of your current process and access to any relevant systems. We deliver a detailed Statement of Work with a fixed price and timeline.
Core System Build (Weeks 1-2)
We create a private GitHub repository for you to see daily code commits. We build the core logic and unit tests for your custom process.
Integration and Deployment (Week 3)
We deploy the system on your cloud infrastructure and connect it to your existing tools. You receive a runbook with API documentation and deployment instructions.
Monitoring and Handoff (Week 4)
We monitor the live system for one week to ensure stability. After a final review, full ownership is transferred. Book a discovery call at cal.com/syntora/discover
Frequently Asked Questions
- How much does a custom process rebuild cost?
- Pricing is based on a fixed project scope. The cost depends on the number of systems to integrate, the complexity of the business rules, and the quality of the source data. A single document-processing pipeline is a smaller project than an interactive AI agent that needs to query multiple APIs. We establish a fixed price after a free discovery call where we can assess these factors.
- What happens when something breaks or an API changes?
- The system is built for resilience. API calls using httpx include automatic retries with exponential backoff. If an error persists, it's logged to Supabase and a Slack alert is sent. For unavoidable failures, the process fails gracefully, for example, by forwarding an email to a human operator so no data is lost. Our optional maintenance plan covers adjustments for third-party API changes.
- How is this different from hiring a freelancer on Upwork?
- We deliver production-ready software assets, not just scripts. A freelance script might solve the immediate problem, but it typically lacks logging, monitoring, and automated deployment. We deliver clean, documented Python code in your GitHub repo, deployed with infrastructure-as-code, and monitored with CloudWatch. It's a maintainable system designed for long-term business use.
- Why use Python and AWS Lambda?
- We use Python because its libraries for AI and data integration are the best in the industry. We deploy on AWS Lambda because it is a serverless platform, which is extremely cost-effective for the transaction volumes of most small businesses. You only pay when the code runs, often totaling less than $20 per month, without ever having to manage a server.
- How do you ensure our data is secure?
- We deploy all code and infrastructure directly into your own AWS account. You retain full control over your data and access credentials. We operate on a principle of least privilege, requesting only the specific IAM permissions needed for the system to function. Syntora never stores your sensitive business data on our own systems.
- What does the optional maintenance plan cover?
- The flat-rate plan covers proactive system maintenance. This includes updating Python package dependencies, applying security patches, responding to monitoring alerts from CloudWatch, and adapting the code to minor, non-breaking changes in third-party APIs. It ensures the system remains reliable over time. Major new features are scoped as separate fixed-price projects.
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