Build Internal AI Tools Your Team Will Actually Use
Small businesses start building internal AI by connecting APIs like Claude to their data via custom software. This creates secure, internal systems for tasks like data analysis, document summarization, and decision support.
Syntora helps small businesses build custom internal AI solutions for operational efficiency. We design and implement secure systems, integrating APIs like Claude to connect with your data for tasks such as document summarization and data extraction.
Syntora helps businesses identify and solve high-leverage, repetitive process bottlenecks using bespoke AI solutions. The initial steps involve an audit of your existing workflows and data infrastructure to define the most impactful use cases. Factors determining scope include the complexity and volume of the documents or data to be processed, the existing data storage solutions, and the required level of integration with your current business applications.
What Problem Does This Solve?
Many teams start by using public tools like ChatGPT Plus for summarization. Copying and pasting sensitive client data (names, policy numbers, accident details) into a third-party LLM is a major security risk and violates data privacy agreements. There is no audit trail, no access control, and the data can be used for OpenAI's model training.
Trying to connect an internal system to the OpenAI API via a no-code platform introduces a different problem: cost. These platforms charge per task or API call. A workflow that parses a PDF, sends text chunks to an LLM, and combines the summary can burn 10 tasks per document. For an agency processing 200 claims per week, that is over 8,000 tasks per month, pushing them into a high-cost plan for a single process.
These approaches treat AI as a disconnected step. Real efficiency comes from integrating it directly into the primary workflow. An adjuster needs the summary inside their claims management system, not in a separate browser tab. Off-the-shelf connectors cannot provide this level of embedded, secure integration.
How Would Syntora Approach This?
Syntora's approach begins with a comprehensive discovery phase, auditing your current data sources and workflows. We would start by mapping your existing data, typically residing in a PostgreSQL database or document stores like AWS S3. Using Python and libraries such as boto3 for AWS, we would define strict schemas with tools like pydantic to ensure accurate parsing of critical entities like claim numbers, policy IDs, or dates. This initial data mapping and architecture design phase typically takes about 4 days, resulting in a clear blueprint for development.
The core of the system would be built around a FastAPI service. For document processing, when a new file is uploaded, a Python function would extract its text. We've built document processing pipelines using Claude API for sensitive financial documents, and the same robust pattern applies to other industry documents, where the Claude API is carefully prompted to extract specific entities such as claimant names, incident dates, and policy numbers. This ensures high accuracy and consistency in data extraction. The system would be engineered for rapid processing, aiming for significant time reductions compared to manual methods.
We would design and build a secure web interface, potentially using Vercel, to display processed documents alongside their structured summaries. User authentication would integrate with your existing company credentials through role-based access control, which we'd manage with tools like Supabase. The FastAPI backend would be deployed as a serverless function on AWS Lambda, providing cost-effective operation that automatically scales with demand, ensuring high availability and efficient resource use without over-provisioning.
The delivered system would integrate directly with your primary claims management or operational software via its API, allowing users to leverage the AI insights without leaving their main tools. Syntora would implement structured logging with solutions like structlog and CloudWatch to ensure comprehensive monitoring. We would configure alerts for critical performance metrics, such as Claude API latency or error rates, enabling proactive identification and resolution of any issues. This ensures the system remains reliable and performs as expected.
What Are the Key Benefits?
Live in 4 Weeks, Not 4 Quarters
We deploy a production-ready system in under 20 business days. Your team gets value immediately, not after a long, drawn-out IT project.
Your Data Never Leaves Your Control
The entire system runs on your cloud infrastructure. Sensitive customer information is never sent to a third-party SaaS or used for model training.
You Get the Keys and the Blueprints
At handoff, you receive the full source code in your private GitHub repository and a technical runbook explaining the architecture. No vendor lock-in.
Built-in Monitoring, Not Afterthought Alerts
We configure CloudWatch dashboards and latency alerts from day one. You know how the system is performing and get notified if an API dependency has issues.
Flat Hosting Costs, Not Per-Seat Fees
A serverless deployment on AWS Lambda means you pay for what you use, often under $50/month. No SaaS bill that grows with your team size.
What Does the Process Look Like?
Week 1: Scoping & Access
You provide access to data sources and walk me through the target workflow. I deliver a technical spec outlining the exact inputs, outputs, and API integrations.
Weeks 2-3: Core System Build
I build the core API and data processing logic. You receive a link to a staging environment where you can test the tool with sample data.
Week 4: Deployment & Integration
I deploy the system to your cloud infrastructure and connect it to your live systems. You get a training session and user documentation.
Post-Launch: Monitoring & Handoff
I monitor the system for 30 days to ensure stability and accuracy. You receive the complete source code, deployment scripts, and a maintenance runbook.
Frequently Asked Questions
- How much does a custom internal tool cost?
- Pricing depends on the number of data sources and the complexity of the required output. A simple document summarizer is different from a multi-step decision support tool. After a 30-minute discovery call where we map the workflow, I provide a fixed-price proposal. Engagements are a one-time build cost, not a recurring subscription.
- What happens when the Claude API is updated or breaks?
- The system is built with API versioning and error handling. For breaking changes or outages, built-in retry logic logs an error to CloudWatch and triggers an alert. I handle incident response directly. Non-breaking API updates can be tested and deployed as part of an optional monthly support plan.
- How is this different from hiring a freelance developer on Upwork?
- I specialize exclusively in building these types of internal AI systems using a proven tech stack. You are not hiring a generalist to learn on your time. The person you talk to on the discovery call is the same person who writes the code, deploys the system, and answers your support questions. There is no project manager.
- Can this integrate with our proprietary, in-house software?
- Yes, as long as your software has a REST API for reading and writing data. During the discovery phase, we will review your API documentation to confirm the necessary endpoints are available. I have experience building integrations with custom internal systems that lack public documentation.
- Our team isn't very technical. How do they use it?
- The goal is to require zero technical skill. The AI tool is embedded directly into your team's existing workflow, often appearing as a new button or data field in the software they already use every day. I also provide a short, non-technical user guide and a recorded video walkthrough for your team to reference.
- What kind of ongoing maintenance is required?
- For most systems, none. The serverless architecture on AWS Lambda handles scaling automatically. The only potential maintenance is occasional prompt tuning if the underlying LLM has a major version update. This is covered by an optional, flat-rate monthly support plan after the initial 30-day monitoring period.
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