Build Internal AI That Automates Your Critical Processes
The cost to hire an AI automation agency is based on project scope, not per-seat SaaS fees. You pay a one-time build fee for a system that you own completely.
Syntora specializes in designing and building custom AI automation systems for businesses facing complex document processing challenges. We develop secure, scalable, and tailored solutions using modern AI tools and cloud infrastructure, focusing on deep technical understanding and clear project deliverables.
Scope depends on the number of systems to integrate, the quality of source data, and the complexity of the business logic. A system that summarizes one type of document from a single source is a straightforward build. A system that analyzes data from three separate APIs with inconsistent formatting requires more engineering.
Syntora designs and engineers custom AI systems for businesses with complex document processing needs. While we have not built a deployed system for a specific insurance agency, we have extensive experience building sophisticated document analysis pipelines using Claude API for other highly regulated industries, such as financial services. These projects involve the same technical challenges: secure data handling, accurate information extraction, and robust system deployment.
What Problem Does This Solve?
Teams often start with workflow automation platforms because they connect common apps quickly. But their task-based pricing models become expensive for high-volume processes. A workflow that triggers on a new file, calls an AI model to analyze it, and then updates a database can burn 3 tasks per file. At 500 files a day, that is a 1,500 task-per-day bill for a single workflow.
A common failure scenario involves conditional logic. Imagine an approval process that needs to check inventory in one system and customer credit in another. Most workflow tools cannot merge branching paths. This forces you to build duplicate downstream actions for each branch, doubling your task count and making the workflow impossible to maintain.
Off-the-shelf AI tools present a different problem: data privacy and a lack of specialization. Sending sensitive customer data to a third-party SaaS vendor is a non-starter for many businesses. These tools also fail on domain-specific tasks, unable to understand the difference between an 'initial claim report' and a 'final settlement offer' in insurance documents, creating more manual cleanup work.
How Would Syntora Approach This?
Syntora's approach to building custom AI automation systems for document processing begins with a deep dive into your existing data and workflows. The first step in an engagement would involve auditing your source data and implementing secure ingestion mechanisms. We would use AWS Secrets Manager for credential management and develop Python scripts with the boto3 library to pull documents from your existing storage, such as S3 buckets. This discovery phase helps define a representative dataset to validate the system's accuracy and performance.
The core of the system would be an automated analysis pipeline, typically leveraging the Claude API for sophisticated natural language processing. This pipeline would be designed to perform multi-step analysis: initially classifying document types, then applying tailored prompts to extract specific key fields into structured JSON objects. We engineer these pipelines for asynchronous processing, which supports high throughput and efficient handling of large document volumes.
The backend logic for the system would be a Python application built with FastAPI. We would containerize this application using Docker and deploy it to a serverless environment like AWS Lambda, configured to trigger based on new file uploads or scheduled events. The extracted structured data would be written to a Supabase Postgres database, providing a durable, queryable, and long-term record of all processed information.
For user interaction, we would design and build a custom dashboard interface using a framework like Vercel, providing role-based access control as required by your organization. The entire system would incorporate structured logging via structlog and integrate with cloud monitoring services such as AWS CloudWatch. We would configure automated alarms to proactively notify stakeholders of any operational issues, ensuring system stability and maintainability.
What Are the Key Benefits?
A Production System in 4 Weeks
From discovery to a fully deployed system in 20 business days. Your team starts getting value next month, not next quarter.
Escape the Per-User Tax
A single project fee and minimal monthly hosting costs on AWS. No recurring SaaS license that punishes you for growing your team.
You Own the Code and Infrastructure
We transfer the GitHub repo and AWS account ownership to you. The system is yours forever, with full documentation and a runbook for your team.
Monitoring is Built In, Not an Add-On
We configure CloudWatch alerts for latency and processing errors. If something breaks, we know in under 5 minutes, often before your team notices.
Connects Directly To Your Systems
We pull data directly from your S3 buckets and write structured output to Supabase. The system fits into your existing stack, it doesn't replace it.
What Does the Process Look Like?
Week 1: Discovery and Access
You provide read-only access to source data and systems. We deliver a Technical Scoping Document outlining the build plan, architecture, and success metrics.
Weeks 2-3: Core System Build
We build the data processing pipeline and AI logic. You receive a link to a staging environment and weekly video updates showing progress.
Week 4: Deployment and Training
We deploy the system on your infrastructure and connect it to your live data. We deliver a recorded training session for your team and full system documentation.
Weeks 5-8: Post-Launch Support
We monitor system performance and handle any issues for 30 days. At the end of this period, we deliver a final runbook and hand off all accounts.
Frequently Asked Questions
- What factors most influence the final cost and timeline?
- The primary factors are data complexity and the number of integrations. Processing structured JSON from a single API is faster than extracting data from three different scanned PDF formats. Each unique business rule or system connection adds to the engineering timeline. Most builds for teams under 50 people are completed in 4 to 6 weeks.
- What happens when the Claude API is down or a file is unreadable?
- The system is built for failure. Unreadable files are automatically moved to an error queue in S3 for manual review. If the Claude API is temporarily unavailable, the AWS Lambda function uses an exponential backoff retry strategy for 15 minutes. If it still fails, the job is requeued. This design ensures no data is ever lost due to temporary outages.
- How is this different from hiring a freelancer on Upwork?
- We deliver production-grade, maintainable systems, not just scripts. A freelancer might write code that runs on their laptop. We deliver a containerized, deployed, and monitored application on your own infrastructure with version control, structured logging, and automated alerts. You receive a durable business asset, not just a temporary fix.
- You say no data leaves our org. How is that possible if you use the Claude API?
- We use AWS PrivateLink to connect to Anthropic's Claude API endpoints. This means the API calls never traverse the public internet, staying securely within the AWS network. For clients with specific data residency requirements, we use their EU or US-based endpoints. We do not store your data; we process it in-memory and return results directly to your systems.
- What if we need changes after the 30-day support period ends?
- After the handoff, the system is fully yours to manage. The provided runbook and documentation cover common maintenance tasks. For new features or significant changes, you can engage us for a small, scoped follow-on project. We also offer optional monthly support retainers for clients who want guaranteed response times for ongoing needs.
- You are a one-person consultancy. What is your 'bus factor'?
- This is a critical question. It's why every project includes complete handoff of all assets. The code is in your GitHub, the infrastructure is in your AWS account, and the documentation details how to operate and update the system. The goal is for any competent engineer to be able to take over management of the system without needing me.
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