Fully Automate Core Business Processes with Custom AI
AI can fully automate document processing, lead qualification, and customer support triage for SMBs. These systems handle tasks like invoice data entry, website chat routing, and CRM updates without staff.
Syntora designs and engineers custom AI solutions for business process automation, including document processing and intelligent routing. For organizations seeking to automate manual data entry or initial qualification tasks, Syntora provides expertise to build tailored systems based on client-specific needs.
The complexity of a build depends on the number of source systems and the required response time. Automating invoice processing from PDFs is different from a real-time support agent that needs to query three internal APIs. The key is defining the exact inputs and the expected outputs for a specific business workflow.
Syntora has experience building document processing pipelines using Claude API for financial documents, and the same architectural patterns apply directly to other types of business documents requiring structured data extraction.
What Problem Does This Solve?
Many businesses first try an off-the-shelf SaaS tool for document parsing. These tools work well on standard, high-quality PDFs but fail on the exceptions that create most of the manual work. Low-resolution scans, handwritten notes, or non-standard invoice formats cause the parser to fail, forcing employees to review every single document and manually correct the errors.
A regional insurance agency with 6 adjusters processing 200 claims per week faced this issue. A SaaS tool handled their standard forms but failed on 30% of their documents, which included photos and adjuster notes. They were paying over $700 per month for a system that still required full manual oversight, defeating the purpose of automation.
Similarly, generic chatbot builders can answer basic FAQs but cannot access private business data. When a customer asks a valuable question like "What is the status of my order?", the bot can only respond with "Please call our support line." This creates a frustrating customer experience and turns the bot into a glorified search bar, not a functional automated agent.
How Would Syntora Approach This?
The engagement would typically start with a discovery phase to audit existing source systems and define precise data extraction requirements. Syntora would connect to source systems using their native APIs, such as a shared email inbox via IMAP or a cloud storage folder using the AWS S3 API. For document pipelines, a collection of sample documents, representative of the full range of quality and formats, would be analyzed to define the exact data fields to be extracted, like 'Policy Number' and 'Date of Loss', often using Python and the Pillow library for initial image analysis.
A FastAPI service would orchestrate the workflow, acting as the core logic for the system. The Claude 3 Sonnet API would be used for data extraction, provided with a structured JSON schema to guide output. This approach allows for consistent extraction of specific fields from unstructured text. With this architecture, the full OCR and data extraction process for a typical two-page document can be completed quickly, often in under 8 seconds. httpx would be used for asynchronous calls to the Claude API, and structlog for machine-readable logging.
This FastAPI application would be containerized with Docker and deployed to AWS Lambda for event-driven processing. New documents arriving in an S3 bucket could automatically trigger the function. The extracted data would be written directly into the client's core system, such as a claims management platform, via its REST API. The infrastructure for such a system can be cost-optimized, often running for under $50 per month for several thousand documents, depending on exact volume and services used.
Syntora would build a simple internal dashboard using Streamlit to show processing history, flag documents with confidence scores below 95%, and allow staff to manually correct exceptions. This feedback loop helps in fine-tuning prompts for the Claude API. The system could also send daily summaries to a Slack channel with processing volume and the number of manual reviews required.
What Are the Key Benefits?
Process Documents in 8 Seconds, Not 6 Minutes
Our document pipeline extracts and validates data from a typical invoice or claim form in under 8 seconds, eliminating over 98% of manual data entry time.
Fixed Build Price, Not Per-Seat SaaS Fees
You pay a one-time project fee. The system runs on your cloud infrastructure for minimal monthly costs, not a subscription that grows with your team.
You Get the Full Source Code
The complete Python codebase is delivered to your private GitHub repository. You are never locked into a proprietary platform and can have any developer maintain it.
Alerts for Failed Jobs, Not Silent Errors
We configure CloudWatch alarms that trigger Slack notifications if processing fails or error rates exceed 2%. You know instantly when a job needs attention.
Connects Directly to Your Core Systems
We build custom API integrations to write data directly into your CRM, ERP, or industry-specific platform. No more copy-pasting between browser tabs.
What Does the Process Look Like?
Scoping and System Access (Week 1)
You provide a set of sample documents and read-only access to source systems. We deliver a detailed technical specification outlining every field to be extracted.
Core System Build (Week 2)
We write the core processing logic in Python and test it against your sample data. You receive access to a staging environment to see the first results.
Integration and Deployment (Week 3)
We deploy the system on your cloud infrastructure and connect it to your live data sources and target systems. We deliver a complete deployment runbook.
Monitoring and Handoff (Week 4+)
For 30 days post-launch, we monitor performance and tune the model. You receive final documentation and full control of the GitHub repository.
Frequently Asked Questions
- How much does a custom automation project cost?
- Pricing is a fixed fee based on project scope. The primary factors are the number of unique document types to process, the complexity of the target system's API for integration, and the cleanliness of the source data. We provide a firm quote after the initial discovery call and technical scoping session. There are no hourly rates or surprise fees. Book a call at cal.com/syntora/discover to discuss your specific project.
- What happens when the AI misunderstands a document?
- The system assigns a confidence score to every extraction. If the score is below a set threshold (typically 95%), the document is flagged and routed to a simple review queue. A team member can then view the original document and the extracted data side-by-side to make corrections in a few clicks. This ensures 100% accuracy for critical data while still automating the vast majority of the workload.
- How is this different from a SaaS tool like Rossum?
- SaaS tools provide a user interface on their platform. We build a system that integrates directly with yours. Instead of logging into a third-party tool, your data appears in your existing CRM or ERP. We also handle non-standard documents and complex business rules that platforms cannot. Most importantly, you own the code and the infrastructure, avoiding vendor lock-in and monthly per-user fees.
- How is our sensitive data handled?
- All systems are deployed on your own private cloud infrastructure (AWS, GCP, or Azure). Syntora never stores or has access to your production data after the initial handoff is complete. The AI model APIs we use, such as Anthropic's Claude, have zero-retention data policies, meaning they do not train on your data. Your information remains within your security perimeter at all times.
- What kind of support is available after the project is finished?
- The initial build includes a 30-day period of monitoring and support. After that, we offer an optional flat monthly maintenance plan. This covers proactive monitoring of the system, bug fixes, dependency updates, and minor adjustments to the AI prompts. It does not cover new feature development, which would be scoped as a new fixed-price project. You are also free to have your own developers maintain the system using the provided documentation.
- Why do you use the Claude API instead of OpenAI's GPT models?
- We use the best tool for the job. For structured data extraction from long or complex documents, we have found that Claude 3 Sonnet consistently provides higher accuracy and lower error rates in production. Its larger context window is also beneficial for processing multi-page documents. For other tasks, like simple classification or summarization, we may use a different model if it proves more effective during testing.
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