Build Internal AI Tools Without Sending Data to Third Parties
Yes, a fully local alternative is a custom AI dashboard deployed on your own infrastructure. It connects directly to your internal tools and data sources without sending data externally.
Syntora offers custom AI dashboard solutions for organizations handling sensitive information, such as those in recruiting or financial services. These bespoke systems are designed to operate entirely within a client's own cloud infrastructure, integrating with internal tools and leveraging AI for tasks like document summarization, without external data exposure. Syntora helps clients build secure, auditable systems that streamline workflows.
Such a system is a private web application running in your own cloud account, designed for workflows involving sensitive information like customer PII, financial records, or internal strategy documents. The scope of an engagement depends on the number of systems to integrate and your specific security and compliance policies. For instance, integrating with two modern APIs would typically be a faster build than interfacing with a legacy database system. Syntora specializes in designing and implementing bespoke solutions like these, leveraging our experience with secure document processing pipelines and API integrations in regulated industries.
What Problem Does This Solve?
Many teams explore remote AI agents to automate tasks inside web applications. These tools are impressive, but they operate by streaming your screen and actions to a third-party service for processing. For any business handling regulated or sensitive data, sending customer information, employee records, or financial data to an external AI model is a significant compliance and security risk.
A 12-person recruiting firm tried using a remote AI agent to source candidates from LinkedIn and log them in their Applicant Tracking System (ATS). Every candidate profile, including names and contact details, was sent to the agent's cloud. This created a data privacy liability, and the agent would frequently break when LinkedIn changed its UI, halting their pipeline for days while they waited for the vendor to ship a fix.
These remote control tools are fundamentally brittle because they rely on screen scraping. They create a dependency on a third party for your core business operations. When the tool is down, your process is down. This approach also fails security audits because you cannot prove where your data is stored or who has access to it.
How Would Syntora Approach This?
Syntora would start by conducting a discovery phase to map your current manual processes to potential API calls, prioritizing automation opportunities over UI-based interactions. For an engagement with a firm needing to process sensitive profiles, this would involve identifying how to securely connect to relevant APIs, such as an Applicant Tracking System (ATS). We would utilize secure, managed credentials in AWS Secrets Manager to ensure that personal user logins are never directly handled by the system.
The core of the proposed solution would be a lightweight backend service developed with FastAPI in Python, designed to encapsulate the business logic. For example, an endpoint within this service could receive a profile via an integrated API, use Pydantic for data validation to confirm all required fields are present, and then interface with the Claude API through Amazon Bedrock to generate a summary of the profile's experience against a job description. We have built robust document processing pipelines using Claude API for financial documents, and the same secure, auditable pattern applies effectively to sensitive candidate profiles or other industry-specific documents.
This service would be deployed as a serverless function using AWS Lambda, offering cost efficiency by only incurring compute charges when actively processing data. The user interface would typically be a Vercel-hosted dashboard, which could be IP-restricted for enhanced security, granting access only from approved networks.
For user management, we would implement Supabase to handle accounts and role-based access, ensuring that only authorized personnel can interact with the dashboard. The entire system would be architected for auditability, with application logs streamed to AWS CloudWatch, providing clients with full visibility and control over their data and operations. Typical build timelines for an initial system of this complexity range from 8 to 12 weeks, contingent on client responsiveness and API availability. Clients would need to provide access to necessary APIs and collaborate during the discovery and testing phases. Deliverables would include the deployed cloud infrastructure, source code, and comprehensive documentation.
What Are the Key Benefits?
Your Private Tool is Live in 4 Weeks
From our first call to a deployed system your team can use takes 20 business days. We focus on a single, high-impact workflow to deliver value quickly.
Pay Once for the Build, Not Per User
This is a one-time project cost, not a recurring SaaS subscription that grows with your team. Your only ongoing cost is low-volume cloud hosting on your own account.
You Get the Full GitHub Repo and AWS Access
We deliver the complete Python source code in your private GitHub repository. You have full ownership and can extend the system yourself or with another developer.
Alerts Fire if an API Key Breaks
We set up monitoring in AWS CloudWatch that checks system health. If an external API key expires or a service goes down, you get a Slack alert immediately.
Connects to Any System with an API
The system is built to talk to modern REST or GraphQL APIs. We've integrated with tools like Salesforce, Greenhouse, Stripe, and internal company databases.
What Does the Process Look Like?
Workflow Mapping (Week 1)
You provide read-only access to the relevant systems. We have a 90-minute call where you screenshare the exact manual workflow. We deliver a technical diagram mapping each step to a proposed API call.
Backend and API Build (Week 2)
We build the core FastAPI service that connects to your tools and handles the data processing. You receive access to a private GitHub repository to see the code as it is written.
Dashboard and Deployment (Week 3)
We build the user interface and deploy the full application into your AWS account. You receive a secure URL for your team to begin testing with real data.
Monitoring and Handoff (Week 4)
We set up logging and alerts, then provide a 1-hour training session for your team. You receive a runbook detailing how the system works and how to handle common issues, plus a 30-day support window.
Frequently Asked Questions
- How much does a custom internal tool cost?
- Pricing is based on the number of systems we need to integrate and the complexity of the workflow logic. A tool that connects to two well-documented REST APIs is straightforward. A project that requires interfacing with a legacy system or cleaning unstructured data requires more engineering time. We provide a fixed-price quote after our initial discovery call.
- What happens if an external API the tool relies on goes down?
- The Python backend is built with retry logic using the `tenacity` library. If an API is temporarily unavailable, the system will try again a few times before marking the task as failed. The failure is logged, and the user sees a clear error message in the UI. The system itself does not crash; it isolates the single failed task for manual review.
- How is this different from buying an off-the-shelf RPA tool?
- RPA tools are often complex, charge high annual license fees per user or bot, and rely on fragile screen scraping. We build a durable, API-first system that you own completely. It's a capital expense, not an operating expense. You get a purpose-built tool designed for your exact workflow instead of a general-purpose platform.
- How do you ensure our data stays secure?
- The entire system is deployed within your own AWS account and Virtual Private Cloud (VPC). We use AWS Secrets Manager for all API keys and credentials, never hardcoding them. Data is encrypted in transit and at rest using AWS KMS. No Syntora employee has ongoing access to your infrastructure after the 30-day support window ends.
- Why use the Claude API instead of another model?
- We use Claude via Amazon Bedrock, which ensures your data is not used for training and remains within the AWS ecosystem. Claude's large context window is ideal for summarizing long documents or conversations, and it excels at following complex instructions to generate structured output like JSON. This makes it reliable for production data processing tasks.
- What kind of support is included after the project is finished?
- Every project includes a 30-day support window after launch to fix any bugs or address minor issues. After that period, you have the runbook and full source code to manage the system yourself. We also offer an optional monthly support retainer for ongoing maintenance, feature requests, or on-call support if you need it.
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