Improve Supply Chain Visibility with Custom AI Automation

Most SMBs struggle with a patchwork of tools that create blind spots. A standard TMS plugin tracks major carriers like UPS and FedEx but fails with regional LTL carriers who only provide updates through a portal or email. This forces your team into a cycle of manual checks, copying and pasting tracking numbers from portals into a central spreadsheet.

This manual process is slow and error-prone. For a distributor managing 300 active orders, it can take a full-time employee half their day just to update statuses. When a customer calls asking for an ETA, the answer is hours old. Inventory management tools often rely on simple reorder points, failing to account for supplier lead time volatility or seasonal demand spikes, leading to costly stockouts or excess inventory.

The core problem is data fragmentation. Off-the-shelf tools operate in silos and cannot enforce the custom business logic needed to unify data from a dozen different sources. The cost of an enterprise platform that can solve this is prohibitive, so SMBs are left with manual processes that cannot scale.

Syntora would approach improving supply chain visibility by first conducting a detailed data audit to identify and integrate your primary data sources. This involves extracting shipment history from your TMS and inventory data from your WMS using their documented APIs. For carriers or partners without APIs, Syntora would develop Python scripts, leveraging tools like Playwright, to scrape tracking data directly from web portals. Unstructured data, such as updates from emailed PDFs, would be parsed using the Claude API to extract key fields like shipment IDs, statuses, and locations. Syntora has built similar document processing pipelines using the Claude API for financial documents, and these effective patterns apply here. All extracted and structured data would then be loaded into a Supabase Postgres database.

Next, Syntora would design and build a central data normalization service using FastAPI. This service would poll all integrated sources at a defined frequency, standardizing disparate carrier statuses into a unified event timeline for each shipment. For predictive ETAs, we would develop a lightweight XGBoost model, trained on your historical transit data, to provide more accurate estimates. The system would also be configured to send alerts to designated channels, like Slack, if a shipment shows no progress for a configurable period, such as more than 8 hours.

To address inventory optimization, Syntora would develop a demand forecasting model utilizing your historical sales data, often employing libraries like Prophet, to predict sales for the upcoming 90 days. This forecast would then feed an inventory optimization module designed to calculate dynamic reorder points, factoring in supplier lead times and desired stock levels. This aims to reduce the need for emergency orders and associated expedited freight costs.

The entire data pipeline and associated services would be deployed using serverless technologies like AWS Lambda functions, typically resulting in efficient resource utilization and low monthly hosting costs. Syntora would also develop a focused dashboard on a platform like Vercel, providing at-a-glance visibility into critical shipment statuses and inventory health. A typical engagement, from initial discovery and architectural design through to development and initial deployment, often spans 8 to 12 weeks for a system of this complexity, depending on client data readiness and feedback cycles. The client would provide access to relevant systems, historical data, and dedicate internal stakeholders for collaboration and feedback. Deliverables would include the deployed system, source code, and comprehensive documentation.