Build Internal AI Tools Your Team Will Actually Use
Small businesses benefit by turning manual, multi-step tasks into single-click internal AI tools. This reduces error-prone work and cuts operational software costs.
Syntora helps small businesses automate manual, multi-step tasks using custom AI solutions. We design and build end-to-end process automation systems, leveraging technologies like FastAPI, Claude API, and AWS Lambda to deliver robust and cost-effective operational efficiencies.
The complexity of end-to-end process automation depends on the variety and consistency of your data sources, the number of decisions the system must make, and the required integration points. A system designed to automate a single-source data extraction task will have a significantly different scope than one that orchestrates decisions across multiple internal and external systems.
Syntora specializes in designing and building custom AI-driven automation solutions. We have extensive experience developing document processing pipelines using the Claude API for complex financial and legal documents, and we apply similar architectural patterns to other industry-specific automation challenges. Typical build timelines for an initial automation system of this complexity range from 4 to 8 weeks, depending on data availability and the clarity of process rules. Clients would need to provide detailed process maps, access to relevant data sources, and internal subject matter expert time for discovery and validation. Deliverables would include a deployed, custom automation system, full source code, and comprehensive documentation.
What Problem Does This Solve?
Teams often start with task automators to connect their apps. You can connect a Google Form to a Slack channel in five clicks, which feels like a win. The problem starts when the workflow needs logic. A tool like Zapier charges per task, and a single lead follow-up can burn through 5-7 tasks: trigger, format data, check for duplicates in the CRM, enrich the lead, then send the notification. At 50 leads per day, that is a 7,500 task/month bill for one workflow.
A regional insurance agency with 6 adjusters tried to automate claim intake. They used a platform to watch an email inbox, extract PDF attachments, and create a record in their claims system. But the platform's text extraction failed on 30% of scanned documents, had no logic to handle multi-file claims, and couldn't merge data from two different PDFs into one record. Adjusters had to manually review every single automated entry, defeating the purpose of the system.
These platforms are fundamentally integration layers, not logic engines. They are designed to move data from point A to point B. They cannot perform the multi-step analysis, summarization, or decision-making that AI models provide. They fail when a process requires context, not just triggers.
How Would Syntora Approach This?
Syntora's approach to end-to-end process automation begins with a comprehensive discovery phase. We would start by deeply auditing your existing manual processes, treating your team's knowledge as the primary source of truth. This involves mapping out every step, decision point, and data dependency within the workflow you aim to automate. For complex document processing tasks, this would include understanding how your team currently identifies, categorizes, and extracts information from various document types.
Based on the discovery, we would propose a custom technical architecture. A common pattern for document-centric automation involves a pipeline that ingests documents from various sources, such as AWS S3 buckets or internal systems, possibly using Python libraries like PyPDF2 for PDF manipulation or boto3 for cloud storage integration.
The core of the intelligent processing would be a FastAPI service. This service would orchestrate calls to large language models, such as the Claude API, for tasks requiring sophisticated natural language understanding and extraction. We would design and fine-tune prompts to instruct the model to perform specific roles, acting as an expert analyst to extract required fields, summarize content, or make rule-based decisions based on the document text. Our experience with Claude API in adjacent financial document processing pipelines informs this prompt engineering process, ensuring accuracy and reliability.
For deployment, the FastAPI application would typically be hosted on AWS Lambda behind an API Gateway. This serverless architecture provides auto-scaling capabilities, ensuring the system handles varying workloads efficiently, and operates on a pay-per-request model, optimizing operational costs. For monitoring system health and performance, AWS CloudWatch would be integrated. Structured logging, possibly via structlog, would provide real-time alerts to dedicated channels, allowing for prompt identification and resolution of any processing failures or API errors. Failed items would be routed to a dead-letter queue for manual review, preventing data loss and ensuring system resilience.
Should a user interface be required for managing queues, reviewing outputs, or administrative tasks, options like Streamlit hosted on Vercel could provide a lightweight and effective dashboard. User authentication and role-based access control, if needed, could be managed through services like Supabase, ensuring data security and proper workflow management. The delivered system would be a fully custom, production-ready solution, complete with all necessary infrastructure as code and operational documentation.
What Are the Key Benefits?
Your Custom Tool Goes Live in 4 Weeks
From our first call to a deployed production system in 20 business days. We skip the sales demos and project managers to build your system directly.
One-Time Build Cost, Near-Zero Upkeep
You pay for the engineering engagement, not a recurring per-user license. Monthly AWS and API costs are typically under $100 for most SMB workflows.
You Get the Keys and the Source Code
We deliver the complete Python codebase in your private GitHub repository and hand over full control of the AWS account. You own the asset you paid for.
Alerts Trigger on a 1% Error Rate
We configure CloudWatch alarms to send a Slack message if anything breaks. You know about problems before your team does, not after.
Connects Directly to Your Real Tools
The system pulls data from where it already lives, whether that's a Postgres database, a Salesforce instance, or a folder of PDFs in Google Drive.
What Does the Process Look Like?
Week 1: Workflow Discovery
You provide read-only access to relevant systems and walk me through the current manual process. I deliver a technical design document mapping out the entire automated workflow.
Weeks 2-3: Core System Build
I write the production code for the AI logic, data connectors, and API. You receive access to a staging environment to test the core functionality with real data.
Week 4: Deployment and Dashboard
I deploy the system to your cloud infrastructure and build the user-facing dashboard. Your team gets login credentials and begins using the live production system.
Post-Launch: Monitoring and Handoff
I monitor system performance and error rates for 30 days post-launch. You receive a final runbook with architectural diagrams and instructions for maintenance.
Frequently Asked Questions
- What does a typical project cost?
- Pricing is based on the number of data sources and the complexity of the AI task. A single-source document summarizer is a smaller project than a multi-source predictive model. I provide a fixed-price quote after our initial discovery call, where we map out the specific requirements. There are no hourly rates or surprise fees. Book a discovery call at cal.com/syntora/discover for a detailed quote.
- What happens if the AI makes a mistake?
- The system is designed for human oversight. Instead of replacing a person, it acts as an assistant. We build a review dashboard where your team can see the AI's output and make corrections. For critical failures, like an API outage, the process pauses and sends an alert. Unprocessable items are sent to a failure queue in AWS SQS for manual inspection.
- How is this different from hiring a freelance developer?
- I specialize exclusively in building these internal AI systems and have a reusable architecture for deployment, authentication, and monitoring. This means I can build and deploy in weeks, not months. A freelancer would need to start from scratch on the infrastructure, whereas I can focus immediately on the core business logic that delivers value to your team.
- How do you ensure our data remains private and secure?
- The entire system is deployed on your own private cloud infrastructure, under your control. Your data is never sent to Syntora's servers and is only processed by the specified sub-processors like AWS and the Claude API. We use role-based access controls in Supabase to ensure team members can only view the data they are authorized to see.
- What happens after the 30-day monitoring period ends?
- You have the full source code and runbook, so any competent Python developer can maintain the system. For teams without technical staff, I offer an optional, flat-rate monthly support plan. This covers bug fixes, dependency updates, and minor feature requests. The plan is month-to-month and can be cancelled at any time.
- What if our process changes after the system is built?
- Small changes, like adding a new field to extract, are typically covered by the support plan. A major process change, like adding a new data source or changing the fundamental business logic, would be scoped as a new, smaller project. Since you own the original codebase, we are building upon the existing asset, not starting over from scratch.
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