Build a Custom AI Tool, Not Another SaaS Subscription
You build an internal AI tool by connecting a large language model API to your data sources. This system is deployed on your own cloud infrastructure, avoiding per-seat SaaS fees.
Syntora designs and engineers custom internal AI tools that connect large language models to an organization's proprietary data, enabling secure, per-request access without per-seat SaaS fees. Our approach involves architecting robust, serverless Python applications with FastAPI and deploying them on secure cloud infrastructure like AWS Lambda, tailored to specific data integration needs.
The final system is a private, custom-built dashboard that lets your team query internal documents, analyze structured data, or summarize reports using AI. The scope of such an engagement depends significantly on your data's complexity. Connecting to a single PostgreSQL database for querying is a relatively fast build. Integrating with multiple third-party APIs and processing a diverse collection of scanned PDFs requires more extensive engineering and discovery. Syntora's real-world experience building document processing pipelines using Claude API (for financial documents) shows how the same patterns apply to internal document challenges.
What Problem Does This Solve?
Teams often start with ChatGPT Teams or Microsoft Copilot. These tools are useful for one-off questions but cannot be integrated into a repeatable workflow. You cannot connect them to your production database or trigger them from a new entry in your CRM. The chat interface also raises data privacy concerns, as employees might paste sensitive customer information into a third-party service.
Next, they look at specialized AI SaaS products for tasks like document analysis or data visualization. These tools fail on two fronts: cost and rigidity. A price of $49 per user per month seems reasonable for one person, but it becomes a $588 monthly bill for a 12-person team. The workflow is also fixed. If your process requires summarizing a document AND checking a value in a separate system, the tool cannot do it. You are stuck with a generic interface that does not match how your team actually works.
This forces teams into a broken process. For example, a 6-person insurance claims team might pay for a SaaS summarizer. They manually download a 50-page claim file from their primary system, upload it to the AI tool, wait for the summary, then copy-paste the results back into their claims system. The extra steps make the process slower than their old manual review, so adoption fails within a month.
How Would Syntora Approach This?
Syntora would approach building your internal AI tool by first conducting a discovery phase to audit your existing data sources and understand your team's specific needs. We would then design an architecture for securely connecting to your data. This often involves using the Boto3 library to access documents in an AWS S3 bucket or psycopg2 to query a PostgreSQL database in AWS RDS. For handling diverse documents like PDFs, we leverage tools such as Pymupdf to extract not just text, but also tables and layout information. This technical approach preserves the original context, allowing the Claude API to provide more accurate and relevant answers.
The core of the system would be a robust Python application, typically built with the FastAPI framework. This application would contain the custom business logic necessary for interacting with the Claude API, managing prompt templating with Jinja2 to ensure consistent and effective queries against your data. All system operations, performance characteristics, and costs would be logged using structlog for transparent monitoring and future optimization.
For deployment, Syntora would containerize the FastAPI application using Docker. It could then be deployed as an AWS Lambda function, exposed securely via Amazon API Gateway. This serverless architecture is a cost-efficient choice for internal tools, as you only pay for active execution time, making it highly scalable for intermittent usage patterns. Access to the system would be secured using AWS IAM, allowing restriction of usage to specific roles within your organization based on your existing security policies.
To provide a user-friendly interface for your team, Syntora would develop a simple web front-end, often utilizing Streamlit due to its rapid development capabilities. This front-end could be deployed on platforms like Vercel. For authentication and role-based access control, we would integrate a service such as Supabase. This enables fine-grained permissions, for example, allowing an analyst to query their own documents while a manager could view aggregate usage statistics across the team.
A typical engagement for a system of this complexity often ranges from 6 to 12 weeks from discovery to initial deployment, depending heavily on data integration requirements and the scope of features. Key deliverables would include the deployed system code, comprehensive documentation, and knowledge transfer to your internal teams. Your organization would need to provide access to relevant data sources and stakeholder input throughout the project.
What Are the Key Benefits?
Live in 4 Weeks, Not 4 Months
We deploy a functional system your team can use in 20 business days. No long implementation cycles or training delays.
One Build Cost, Not a Monthly Subscription
A single project engagement covers the build. Your only recurring cost is cloud hosting, typically under $50 per month.
You Get the Keys and the Code
We deliver the complete source code in your private GitHub repository, along with deployment scripts and a runbook.
Built-in Monitoring, Not On-Call Headaches
We configure AWS CloudWatch alerts for API errors and high latency. You get a Slack notification if something breaks, before your team even notices.
Connects to Your Data, Not Ours
The system integrates directly with your existing S3 buckets, PostgreSQL databases, or Google Drive folders. No data migration required.
What Does the Process Look Like?
Week 1: Scoping and Access
You provide read-only access to data sources and walk us through the target workflow. We deliver a technical spec outlining the architecture and data flow.
Weeks 2-3: Core System Build
We build the back-end service, connect to the Claude API, and containerize the application. You receive a link to a staging environment for early testing.
Week 4: UI and Deployment
We build the custom dashboard, integrate role-based access, and deploy to your AWS account. You receive login credentials for your team to begin production use.
Post-Launch: Monitoring and Handoff
We monitor the system for 30 days to resolve any issues. You receive the final runbook, source code repository, and documentation for long-term maintenance.
Frequently Asked Questions
- What does a typical project cost and how long does it take?
- Most projects are completed in 4-6 weeks. The cost depends on the complexity of your data sources and the features required in the user interface. A system that reads clean data from a single API will be simpler than one that parses unstructured text from scanned PDFs. We provide a fixed-price proposal after our initial discovery call, so you know the full cost upfront. Book a call at cal.com/syntora/discover to discuss your specific needs.
- What happens if the Claude API is down or a document fails to process?
- The system is built for resilience. API calls to Claude use exponential backoff, automatically retrying a few times if the service is temporarily unavailable. If a document is corrupt or fails processing for another reason, it is moved to a separate 'dead-letter queue' in S3, and a notification is sent to a designated Slack channel with the file name. This prevents one bad file from halting the entire process for your team.
- How is this different from hiring a freelance developer on Upwork?
- A freelancer delivers code. Syntora delivers a production-ready system. This includes architecture design, infrastructure-as-code deployment scripts, monitoring, alerting, role-based access control, and a runbook for long-term maintenance. The person you talk to on the discovery call is the engineer who writes every line of code, eliminating communication gaps. We build and deploy systems designed for business-critical workflows, not just one-off scripts.
- How do you ensure our sensitive data remains secure?
- The entire system is deployed within your own AWS cloud account. Your data never leaves your infrastructure. We work under the principle of least privilege, using temporary IAM credentials that grant us only the read-only access needed for the build. Once the project is complete, you can revoke our access entirely. You maintain full control over your data and the infrastructure it runs on.
- Why do you use Python and AWS Lambda for the tech stack?
- Python has the most mature ecosystem for AI and data engineering, with libraries for every possible task. AWS Lambda allows for a serverless architecture, which is perfect for internal tools with intermittent usage patterns. Instead of paying for a server to run 24/7, you pay only for the milliseconds of compute time your application actually uses. This makes the system extremely cost-effective to operate long-term.
- What kind of support is available after the 30-day monitoring period?
- After the initial 30 days of included support, we offer an optional monthly maintenance retainer. This provides a guaranteed service-level agreement (SLA) for critical issues, covers ongoing dependency updates, and includes a set number of hours for minor feature requests or adjustments. This ensures the tool continues to meet your team's needs as your business evolves. Many clients choose this for peace of mind.
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