AI in logistics is most useful in the unglamorous middle of the operation: the spreadsheets, inboxes and ERP screens where people copy numbers from one system to another. Flowrest Labs builds supply chain automation that removes that copying. We sync inventory across warehouses and systems, turn inbound orders and documents into clean records, and alert the team when something needs a decision. Everything is custom code connected to your existing stack, and you own it.
To be clear about scope: we build warehouse automation software, not robotics or conveyor hardware. Our work sits between your WMS, ERP, storefronts, carriers and inboxes.
Where supply chain teams lose time
- Inventory that disagrees with itself. Each warehouse, ERP and sales channel holds its own stock count. Reconciling them is manual, so it happens late, and overselling or stockouts follow.
- Retyping purchase orders. POs arrive by email, PDF, portal or EDI and get keyed into one or more systems by hand.
- Order exceptions. Wrong SKUs, missing addresses and quantity mismatches are found by whoever happens to notice.
- Paperwork. Packing lists, bills of lading, proof of delivery and supplier invoices need to be read and matched to orders.
- Status questions. Sales and customer service chase operations for "where is this order?" answers that already live in a carrier or WMS system.
Supply chain automation use cases we build
| Use case | What gets automated | Typical systems |
|---|---|---|
| Inventory sync automation | Reading stock levels from every location and system, reconciling them on a schedule and flagging mismatches | ERPs, WMS, Shopify, PostgreSQL, webhooks |
| Order processing automation | Extracting and validating orders or POs, then creating them in the ERP or WMS | Email, PDF, EDI, Shopify, ERP |
| Stockout and low-stock alerts | Watching thresholds and sales velocity, sending early warnings | ERP, WMS, Slack, email |
| Replenishment PO drafting | Creating draft purchase orders when safety stock is breached, held for approval | ERP, supplier email, Slack |
| Shipping and supplier document extraction | Pulling data from bills of lading, packing lists and invoices, matching to orders | Email inbox, scans, ERP, accounting software |
| Order status assistant | Answering internal or customer status questions from carrier and WMS data | Carrier APIs, WMS, Shopify, Zendesk, Slack |
Inventory sync and order routing are AI workflow automation builds. Reading POs and shipping paperwork is intelligent document processing. An assistant that answers status questions or works an exceptions queue is a custom AI agent, and if your team needs a dashboard or ops portal on top, we build it as a custom web app.
The systems we integrate with
Logistics stacks are rarely tidy. We connect to anything with an API, webhook, database or file export: ERPs, warehouse management systems, Shopify and other storefronts, carrier tracking APIs, accounting tools such as QuickBooks and Xero, Slack, email and PostgreSQL. For older systems without an API we work from scheduled exports or direct database reads.
Many trading partners still exchange documents as EDI. The X12 856 Ship Notice/Manifest, for example, lists the contents of a shipment and lets the receiver process it against open purchase orders. Our pipelines can read and produce that kind of structured data alongside the PDFs and emails that fill the rest of the inbox.
Compliance, traceability and data confidentiality
Supply chain data is commercially sensitive: customer lists, pricing, supplier terms and volumes. We build with zero-retention data pipelines, encrypted webhooks and private hosting on AWS or Google Cloud, with role-based permissions and an activity audit log. Your data is never used to train public models.
Some sectors add record-keeping rules. If you handle foods on the FDA's Food Traceability List, the Food Traceability Rule (FSMA 204) requires records of key data elements for critical tracking events such as shipping and receiving, and an electronic sortable spreadsheet within 24 hours of an FDA request. The FDA proposed moving the compliance date to July 20, 2028, and Congress then directed the agency not to enforce the rule before that date. Clean, synced shipping and receiving records make that kind of request much easier to answer. Whether the rule covers you is a question for your compliance team; this isn't legal advice.
Human approval where it matters
Reading, reconciling and alerting run on their own because they don't change anything outside your systems. Actions that commit money or stock don't. A replenishment PO is drafted and sent to a buyer in Slack or email with the stock levels and reason attached, and it goes to the supplier only after a one-click approval. Order changes above a value you set, unusual quantities and low-confidence document reads are routed the same way.
If an upstream system goes down or returns strange data, the pipeline stops and alerts rather than syncing bad numbers everywhere. We explain the pattern in AI workflow automation vs. traditional RPA.
Case study: multi-warehouse inventory and order automation
A global logistics operator's warehouse staff were manually copying purchase orders between 3 different ERP systems, which caused stockout errors and delayed shipments. We built an automated sync pipeline that continuously reads inbound POs, reconciles stock levels and flags low inventory across all 3 warehouses every 5 minutes. When safety thresholds are breached it drafts replenishment orders, and it sends Slack and email dispatch alerts.
- $120k annual manual labor savings
- 99.9% order accuracy
- 5-minute inventory sync interval
- Zero manual data entry between the ERP systems
- 4-week deployment, built with Next.js, Node.js, PostgreSQL, the Shopify API and webhooks
See the full summary on our portfolio.
How an engagement runs
We begin with a free 30-minute workflow audit to map where data is copied by hand, which systems are the source of truth and where errors start. You get a fixed-scope proposal with an exact price and timeline. Most builds go live in 1 to 3 weeks; multi-system projects like the one above can take longer, and that one took 4. We test against real order and stock data in staging before launch, then transfer the full code to your GitHub with a video runbook and 30 days of post-launch support. Book a free audit to start.