Rebuild Your Core Business Processes with AI Automation
Rebuilding a business process with AI automation provides direct data ownership and eliminates recurring per-task software fees. It replaces brittle, multi-app workflows with a single, maintainable codebase.
Syntora offers expertise in designing and engineering end-to-end business process rebuilds with AI automation. Our approach focuses on developing custom solutions that replace fragmented workflows with unified, maintainable systems, leveraging advanced technologies like FastAPI and Claude API.
The scope of such a rebuild depends on the number of systems to integrate and the complexity of the business logic. A simple lead routing workflow connecting a web form to a CRM might be a two-week engagement. A multi-stage process that pulls from several external systems and requires AI-based document analysis could take over a month. Syntora delivers the expertise and engineering engagement to design and implement these custom solutions, tailored to your specific operational needs.
What Problem Does This Solve?
Teams often start by stitching together apps with visual workflow builders. These tools are great for simple triggers, but they fail when a core business process depends on them. Their pricing model, which charges per task or step, becomes expensive. A single new client onboarding can trigger 15 tasks, and at 100 clients per month, that is 1,500 tasks and a surprise bill.
A regional insurance agency tried to automate new claim intake this way. The workflow parsed an email, created a record in their claims system, uploaded files to Google Drive, and sent a Slack alert. The email parser misread policy numbers 10% of the time, creating junk data. The workflow had no validation logic. If the Google Drive step failed because a folder name already existed, the entire process would halt silently, requiring a manual check of every single claim.
This approach is fundamentally brittle. It spreads business logic across multiple third-party systems you do not control. When a step fails, you get a generic error message, not a specific HTTP status code or a payload you can debug. There is no central place to manage error handling, retries, or logging, making the process impossible to depend on.
How Would Syntora Approach This?
Syntora's initial engagement would involve mapping your existing workflow into a detailed technical specification, often using Python for clarity. We would leverage libraries like httpx to interact directly with relevant service APIs, defining precise data schemas with Pydantic. This discovery and mapping phase typically takes 3-5 business days, establishing a robust blueprint for the new system.
The core logic would be developed as a single, unified FastAPI service. For AI-powered document processing, such as extracting specific data points from emails or other unstructured text, we would integrate with large language models like the Claude API. We've built similar document processing pipelines using Claude API for financial documents, and the same pattern applies to other complex document types. All custom business logic, including validation and routing rules, would be implemented as explicit Python code and maintained in a private GitHub repository. This approach consolidates fragmented processes into a high-performance, maintainable system.
The FastAPI service would be containerized with Docker for consistent deployment and then deployed to a serverless platform like AWS Lambda. This architecture ensures scalability and cost-efficiency, as you would only pay for active execution time. A robust CI/CD pipeline would be established using GitHub Actions to facilitate tested, automated deployments.
We would implement structured logging with tools like structlog, directing operational data to a dedicated database such as Supabase for comprehensive monitoring and querying. Robust error handling would be built in, including exponential backoff for API calls to manage transient network issues. Alerts for critical failures, such as repeated external API call failures, would be configured via AWS CloudWatch and delivered to team communication platforms like Slack, aiming for high system availability.
What Are the Key Benefits?
From Fragile Workflow to Production Code in 4 Weeks
We map, build, and deploy a production-grade replacement for your core process in 20 business days. No lengthy rollouts or complex implementation phases.
End Per-Task Pricing and Subscription Fees
Your process runs on serverless infrastructure. A workflow that cost $500 per month in a visual builder typically costs under $30 per month on AWS Lambda.
You Own The Code and The Infrastructure
We deliver the full Python codebase in your GitHub repository and deploy it to your AWS account. You have zero vendor lock-in and full control.
Get Alerts on Failure, Not After Failure
We build in monitoring that reports specific API errors and latency spikes directly to Slack. You know about problems in milliseconds, not hours later.
Connect Any API, Not Just Pre-Built Apps
We write custom integrations to any system with an endpoint, including internal databases and legacy SOAP APIs, using Python's httpx library.
What Does the Process Look Like?
Process Mapping (Week 1)
You provide credentials for your current systems and walk us through the workflow. We deliver a technical specification detailing every API call, data transformation, and failure point.
Core Logic Build (Week 2)
We write the Python code for the entire process as a single FastAPI service. You receive access to the private GitHub repository to review the code as it's developed.
Deployment & Testing (Week 3)
We deploy the service to your AWS account and run end-to-end tests using sandboxed data. You receive a staging URL to verify all functionality yourself.
Monitoring & Handoff (Week 4)
We configure logging and alerting, then monitor the live system for 5 business days. You receive a complete runbook covering deployment, monitoring, and troubleshooting.
Frequently Asked Questions
- What does a process rebuild typically cost?
- The cost is determined by the number of system integrations and the complexity of the business logic, not hours or headcounts. A simple two-system workflow is a small project, while a five-system process with AI-based decision points is more involved. We provide a fixed-price quote after the initial discovery call, which you can book at cal.com/syntora/discover. The project is typically scoped for a 3-6 week timeline.
- What happens when an external API like Salesforce is down?
- The system is built for resilience. API calls automatically retry with exponential backoff, which resolves most temporary outages. If an outage persists, the failed task and its data are sent to a dead-letter queue in AWS SQS. You get a Slack alert about the persistent failure, and once the external service is back online, you can re-process the queued tasks with a single command instead of losing data.
- How is this different from hiring a freelance developer?
- A freelancer often delivers a script. Syntora delivers a production-ready system. This includes CI/CD with GitHub Actions for automated testing and deployment, structured logging to Supabase for observability, infrastructure-as-code templates for repeatable setups, and a detailed runbook for long-term maintenance. We build for handoff and operational independence, not dependency.
- Can you incorporate AI decision-making into the process?
- Yes. This is a common requirement. We frequently integrate calls to large language models like Claude or Gemini for tasks that require judgment. Examples include summarizing support tickets, classifying inbound sales leads based on email content, or extracting structured data from unstructured documents like invoices or resumes. This adds an intelligence layer that rule-based systems lack.
- What maintenance is required after handoff?
- For most processes, no active maintenance is needed; the system runs independently. Intervention is only required if an external service you rely on introduces a breaking change to its API. The monitoring we set up will immediately alert you to this. We offer optional, post-project support plans to handle these external changes, or your team can manage them using the provided runbook.
- Do we need an in-house engineer to manage this system?
- No. The system is designed for operational autonomy and the runbook is written for a non-technical person. It explains how to interpret the few alerts the system might generate and what they mean for the business process. The goal is for your operations team to own the process, not your engineering team. You will not need to hire anyone to manage the system we build.
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