Build Internal AI Tools Without Hiring a Full-Time Team

The default path for building custom software is to hire a developer. For AI projects, this often fails. A senior AI engineer costs over $200,000 per year fully loaded, takes 3-6 months to find and hire, and may leave after a year, taking all project knowledge with them. For an SMB, a single bad hire can kill a project and burn a significant portion of the annual budget.

A more common failure is the skills mismatch. You hire a data scientist who can build a model in a Jupyter Notebook but has never deployed a production API or managed cloud infrastructure. Alternatively, you hire a generalist full-stack developer who can build an API but has no experience with prompt engineering, LLM APIs, or vector databases. This results in a system that either never gets deployed or is too brittle for business-critical use.

We saw this with a 25-person logistics company that hired a Python developer to build a routing optimizer. Six months later, they had a script that only ran on the developer's laptop. It had no API, no authentication, and crashed on real-world data volumes because it tried to load everything into memory. The project was scrapped after spending six figures in salary with nothing to show for it.

Syntora would start by auditing your existing document sources, whether they are email attachments, a shared drive with PDFs, or an existing database. For document parsing, the system would typically use Python with the `unstructured` library to convert Word documents, PDFs, and images into clean text. This extracted data would be stored in a Supabase Postgres database, using the `pgvector` extension to enable efficient semantic search.

The architecture would center on a FastAPI service orchestrating the workflow. For an incoming document, the system would construct a targeted prompt using the extracted text and send it to the Claude 3 Sonnet API via an async `httpx` call. The prompt would instruct Claude to return a structured JSON summary with specific fields. We've built document processing pipelines using Claude API for financial documents, and the same pattern applies to summarizing documents in other industries. The goal would be efficient processing, with performance tuned to your specific document volumes.

The FastAPI service would be packaged into a container and deployed on AWS Lambda. This approach helps manage hosting costs, with projections provided based on your anticipated usage volume. Syntora would develop a simple, secure frontend on Vercel with role-based access control, which can be tied to your Google Workspace or Microsoft 365. This dashboard would provide your team with a clear interface to view and manage the AI-generated outputs.

Production systems would be monitored using AWS CloudWatch, with automated alerts configured for API errors (any 5xx status codes) and high latency (over a 10-second response time). All application events would be written to structured logs with `structlog`, facilitating straightforward tracing and debugging of document processing.