Stop Manual Data Entry. Start Your AI Transformation.
We use AI to read documents, qualify sales leads, and triage support tickets automatically. Custom AI agents replace manual data entry and repetitive rule-based checks.
We built a document processing pipeline for a regional insurance agency with 6 adjusters. They were manually entering data from 200 claims forms a week. The new system, built in 3 weeks, uses OCR and the Claude API to process each form in 8 seconds, cutting a 6-minute task to a quick review.
These are not off-the-shelf tools, but custom systems built for a specific workflow in your business. A typical build connects your existing CRM or ERP to an AI model like Claude to handle tasks that require judgment, like extracting terms from a contract or deciding if a support ticket is urgent.
What Problem Does This Solve?
Many businesses try connecting apps using visual workflow builders. These tools are great for simple triggers, like sending a Slack message when a HubSpot form is filled. They fail when a process requires multiple decisions or data transformations. Their 'if/then' logic is brittle and cannot handle nuanced human judgment.
Consider a 15-person sales team that wants to qualify leads from a contact form. They use a workflow tool to check if the email is from a corporate domain. But what if the lead's message says 'we're a 100-person company evaluating new software' but they used a Gmail address? The simple rule-based system marks it as low quality. It cannot understand intent, so high-value leads are missed daily.
The problem is these tools are stateless and have no memory. Each run is independent. They cannot learn from past outcomes or adapt. An internal Python script might seem like the next step, but it quickly becomes a maintenance burden for a company without a dedicated engineering team. When an API changes or a new data field is added, the script breaks.
How Does It Work?
The first step is mapping the exact workflow. We connect directly to your systems, like a CRM or a shared inbox, using their APIs. For a document processing pipeline, we pull the last 100-200 examples of the target document. We use this sample set to fine-tune a prompt for the Claude 3 Sonnet API, focusing on consistent JSON output for structured data extraction.
We write the core logic in Python using FastAPI to create a private API endpoint for your process. For instance, an AI agent for lead qualification uses httpx to make asynchronous calls to your CRM and enrichment services. We implement structured logging with structlog, so every decision the agent makes is recorded. The system can process over 500 leads per hour on a single instance.
The FastAPI application is deployed as a container to AWS Lambda, ensuring it only runs when needed. This keeps hosting costs under $50 per month for most workflows. We use Supabase for a PostgreSQL database to store job statuses, results, and API keys. The final system is connected to your tools via webhooks, triggering the AI process in under 200ms.
We set up health checks that monitor the system's uptime and API response times. If the error rate on API calls exceeds 2% in a 5-minute window, an alert is sent. Upon completion, we deliver the full source code to your company's GitHub repository, including a runbook for common operational tasks. You have full ownership with no vendor lock-in.
What Are the Key Benefits?
Your First AI System Live in Under a Month
A typical scoped build takes 2-4 weeks from kickoff to production. Start seeing results from AI automation this quarter, not next year.
Fixed Price Build, No Per-Seat Surprise
We agree on a single, fixed price for the entire build. Optional flat monthly maintenance means no unpredictable costs that grow with your team.
You Get the Keys and the Source Code
We deliver the complete Python codebase to your GitHub account. You own the intellectual property and can modify it with any engineer in the future.
Built-in Monitoring, Not an Afterthought
Every system includes health checks and structured logging. Get alerts for API failures or high error rates before they impact your business.
Connects to Your Core Business Systems
We build custom integrations for your CRM, ERP, and industry platforms. The AI workflow feels like a native part of your existing tools.
What Does the Process Look Like?
Discovery and Scoping (Week 1)
You provide access to the relevant systems and walk me through the current manual process. I deliver a technical plan outlining the build, timeline, and a fixed price.
Core System Build (Weeks 2-3)
I write the production code and provide weekly updates. You receive a link to a staging environment to test the system with real data.
Deployment and Handoff (Week 4)
I deploy the system on your infrastructure and connect it to your production tools. You receive the complete source code in your GitHub repo and a detailed runbook.
Post-Launch Support (Optional)
After a 2-week warranty period, you can opt into a flat monthly maintenance plan. This covers bug fixes, monitoring, and minor updates to adapt to API changes.
Frequently Asked Questions
- What does a typical custom AI build cost?
- Scoped builds are fixed-price engagements. The cost depends on the number of systems to integrate and the complexity of the AI logic. A document processor for a single PDF template is simpler than a lead qualifier that needs five data sources. We define the exact scope and price in a technical plan after our discovery call.
- What happens when an AI process fails on a specific task?
- The system is designed for graceful failure. If the Claude API cannot extract data from a document with 95% confidence, it flags it for human review in a specific folder or Slack channel. The process does not halt. It isolates the exception so your team can handle it manually, ensuring no data is lost.
- How is this different from hiring a freelance developer on Upwork?
- I am not a generalist freelancer. I specialize exclusively in building these types of AI automation systems with a specific tech stack. You are not hiring a person to learn on your project. You are hiring an expert who has built and deployed this exact architecture multiple times before. This reduces risk and shortens the timeline from months to weeks.
- How do you handle our sensitive data?
- The system is deployed on your own cloud infrastructure, not Syntora's. Your data never leaves your control. We use tools like Supabase for secure credential management and can operate within your existing security protocols. I can sign an NDA before any sensitive data is shared during the discovery phase.
- Why do you use Python and not another language?
- Python has the most mature ecosystem for AI and data processing. Libraries like httpx for async API calls and frameworks like FastAPI are production-grade and efficient. This focus allows me to build reliable systems quickly. I write every line of code, ensuring a consistent, high-quality standard.
- What does the optional maintenance plan cover?
- The flat monthly plan covers up to five hours of work. This is for fixing bugs, updating API client libraries when a connected service changes, and responding to monitoring alerts. It does not cover building new features, which would be a new scoped project. It is an insurance policy to keep your critical business system running smoothly.
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