Build Internal AI Tools Your Tech Team Will Actually Use
A custom AI solution delivers ROI by automating repetitive tasks that require complex judgment. The primary return comes from staff reclaiming hours per week from manual data entry and analysis.
Syntora specializes in designing and building custom internal AI solutions, focusing on automating complex judgment tasks for tech businesses. By leveraging advanced architectures with tools like Claude API and FastAPI, Syntora delivers tailored engineering engagements to transform data processing workflows. We provide the expertise to solve your specific challenges.
The scope of a project depends on the number of data sources and the complexity of the business logic. A system that summarizes inbound support tickets from a single email inbox is a faster build than one that analyzes financial documents from three different sources. The key is clean, consistent data.
What Problem Does This Solve?
Small businesses often try off-the-shelf AI products first, but find they are too generic. A SaaS tool trained on general web data cannot understand a company's unique invoices, legal contracts, or customer support tickets. The result is generic summaries and analysis that miss the specific details that matter. These tools also charge per-seat fees that become expensive as the team grows, for a feature that only partly solves the problem.
Then they try point-and-click automation platforms to connect different apps. This works for simple A-to-B notifications, but fails for business-critical workflows. For example, a 6-adjuster insurance agency processing 200 claims per week tried to build a workflow to extract data from claim PDFs. The platform's per-action pricing meant each document cost them multiple tasks, leading to a bill of hundreds of dollars per month just to read files. The workflow was also brittle, failing silently when a PDF had a slightly different format, with no logs to show what went wrong.
These approaches fail because they are not engineered for specific, high-stakes business processes. They lack the custom logic, robust error handling, and direct data integration needed for a core operational task. They are designed for simple connections, not for becoming a reliable part of the company's infrastructure.
How Would Syntora Approach This?
Syntora's engagement would start with a detailed discovery phase to understand your existing data ecosystem and identify the specific documents and processes ripe for AI automation. We would audit your data sources, such as cloud storage buckets or internal databases, to determine the optimal connection strategy.
The core of the solution would involve processing your documents using advanced large language models like the Claude API. Leveraging its extensive context window, the system would ingest full document packages, such as support tickets, contracts, or technical specifications, ensuring all relevant information is considered for analysis. We have experience building similar document processing pipelines for financial documents using the Claude API, and this pattern applies to a wide range of business documents.
The solution's logic would be engineered in Python. We would use robust libraries, for example, boto3 for fetching documents from AWS S3, and httpx for efficient asynchronous calls to the Claude API. Through iterative prompt engineering and testing, we would refine the AI model's instructions to accurately extract specific data points and identify inconsistencies according to your defined business rules. A FastAPI endpoint would wrap this core logic, providing a scalable and responsive interface for document processing.
This FastAPI service would be deployed on serverless infrastructure, such as AWS Lambda, which dynamically scales with demand and scales to zero when idle, optimizing hosting costs. Custom, intuitive dashboards would be built using frameworks like Streamlit and hosted on platforms such as Vercel, allowing your team to review and approve the extracted data. Access control would be implemented using systems like Supabase to ensure data security and user permissions.
For operational transparency, every API request and AI response would be logged to a structured stream in AWS CloudWatch. Automated alarms would be configured to provide notifications, for instance via Slack, if any operational thresholds are exceeded. As part of the engagement, Syntora would deliver the full Python codebase in your private GitHub repository, along with comprehensive documentation and a runbook detailing how to manage and extend the system. Typical build timelines for systems of this complexity range from 4 to 8 weeks, depending on data availability and business rule complexity. The client would need to provide access to relevant data sources and actively participate in refining business logic during the development process.
What Are the Key Benefits?
A Working System in 20 Business Days
From our first call to a production-ready tool your team is using. We build the core system in 3 weeks, not three months.
No Per-Seat Fees, Ever
You pay for the one-time build. After that, you only pay for the low-cost cloud infrastructure it runs on, not for each user you add.
You Own The Code and Infrastructure
We deliver the full source code to your GitHub repository and deploy it on your cloud account. You are never locked into our service.
Alerts When It Breaks, Not After
We build monitoring and alerting into the system using AWS CloudWatch. You get a Slack message the moment an issue is detected.
Connects Directly to Your Data
We integrate with your systems where they are, from an AWS S3 bucket or Google Drive to a PostgreSQL database using psycopg2.
What Does the Process Look Like?
Week 1: Discovery and Access
You provide read-only access to the relevant data sources and walk us through the existing manual process. We deliver a technical specification document outlining the exact logic to be built.
Week 2: Core Engine Build
We write the Python code for data processing and AI integration. We provide a staging URL where you can upload test files and see the raw data output from the API.
Week 3: Dashboard and Deployment
We build the user interface and deploy the full application to your cloud infrastructure. We deliver login credentials for your team to begin testing with live data.
Week 4+: Monitoring and Handoff
We monitor the live system for performance and accuracy for 30 days. We then deliver a final runbook and transfer ownership of the code repository.
Frequently Asked Questions
- How much does a custom AI tool cost?
- The cost is scoped based on complexity. Factors include the number of data sources, the cleanliness of the data, and the intricacy of the business rules the AI must follow. A system to summarize a single document type is straightforward. A system that must cross-reference three different data sources to make a decision is more involved. We provide a fixed-price quote after our initial discovery call.
- What happens when the Claude API is down or returns an error?
- The system is built for resilience. The Python code includes automatic retries with exponential backoff for transient API errors. If a request fails three consecutive times, the task is moved to a 'dead-letter queue' and an alert is sent. The dashboard will show the source document as 'Processing Failed', allowing a team member to manually review it while we investigate the root cause.
- How is this different from hiring a freelance developer on Upwork?
- We deliver a production system, not just a script. A freelance developer might write the core Python code, but you are responsible for deployment, hosting, monitoring, error handling, and creating a user interface. Syntora handles the entire lifecycle from architecture to post-launch monitoring, delivering a reliable tool that is fully integrated into your operations. The person on the discovery call is the engineer who writes every line of code.
- How do you handle our company's sensitive data?
- Your data never leaves your control. We build and deploy the entire system within your own cloud infrastructure (like an AWS or GCP account). We operate under a strict NDA, and our access is temporary and revoked after the engagement ends. Unlike a SaaS tool, your proprietary data is never sent to a third-party vendor or used to train their models.
- Will this system scale if our business volume doubles?
- Yes. We build on serverless infrastructure like AWS Lambda, which scales automatically with demand. If your weekly document volume grew from 200 to 2,000, the system would handle it without code changes. Your cloud hosting costs would increase proportionally (e.g., from under $20/month to under $200/month), but the architecture is designed to handle at least 10x the initial volume.
- What if our business rules or document formats change in the future?
- Since you own the code, you have full control. The prompts and Python logic can be updated to handle new formats or rules. We document the code clearly so that any competent Python developer can make modifications. We also offer ongoing retainer agreements for clients who prefer us to handle maintenance and future updates.
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