A multi-supplier industrial distributor was bleeding 15–20% of orders to supplier failures and manual chaos. We built a Django platform that runs the entire order-to-delivery flow across 200+ vendor portals and 100+ warehouses with zero manual bottlenecks. Five integrated systems. 16 weeks from kickoff to production. Five-person manual ops team displaced.
When you're routing 8–10K orders/month across 200+ supplier portals, every step that touches a human becomes a leak. The client's pre-build state was a five-person team manually reconciling orders, chasing suppliers, and reconstructing payment reports — and still bleeding revenue to the failures they couldn't catch in time.
Five-person ops team typing orders from spreadsheets, email threads, and phone calls into the system. Capped throughput at ~100 orders/day. Beyond that, queues built up and SLAs slipped.
Each of 200+ suppliers had its own pricing portal, its own stock format, its own quirks. Ops would tab between dozens of vendor sites just to confirm whether something was in stock today.
A failed payment or out-of-stock supplier wouldn't surface until end-of-day reconciliation — sometimes days later. By then the customer had already churned, opened a ticket, or both.
We didn't replace the team's tools — we replaced the manual workflow itself. Every order now flows through a single Django platform that handles intake, supplier negotiation, failure recovery, customer communication, and payment reconciliation without human intervention. Humans get involved only when the system explicitly escalates.
Every order auto-generates a confirmation number on submission, syncs in real-time with the POS system, and is tracked end-to-end from placement to delivery. No spreadsheets. No status emails. No "let me check on that."
Web scrapers pull live pricing and inventory directly from 200+ vendor portals. Supplier-specific parsers handle structural differences automatically — when a vendor changes their HTML, the parser logs the drift and rolls forward. Stock availability is always current, no manual lookups.
Stock issues and payment failures are flagged within seconds of detection. The system automatically re-routes the order to alternate suppliers ranked by price, lead time, and historical fulfillment rate — before the customer is ever aware. Cases that can't auto-recover escalate via auto-generated tickets with full context attached.
Customer notifications fire automatically at every milestone: order confirmation, dispatch, delay, delivery. Supplier follow-ups run through an integrated calling system. Everything is logged and traceable — no "I called them last Tuesday, I think" guesswork.
Payments are matched against bank entries automatically. Mismatches surface in real-time instead of at month-end close. Daily reconciliation reports generate with zero human input — finance reviews exceptions, not raw transactions. What used to take a person two days a month now takes 20 minutes of review.
A boring stack on purpose. Django + PostgreSQL gave us the reliability and ORM ergonomics needed for a transactional system this critical. Celery handles the async work — scraping, retries, escalations, notifications — without blocking the request path. We picked the tools that wouldn't surprise the client's eventual in-house team.
Every Build engagement runs the same playbook: foundation, core, polish, handoff. This one was no exception — though the per-supplier scraper work made phase 2 especially dense.
Mapped the existing manual workflow with the ops team, locked the Django + Celery + Postgres architecture, and de-risked the highest-uncertainty piece first: a working scraper for the 5 most-used supplier portals. By Friday demo of week 2, we could pull live stock from real vendors. The riskiest unknown was no longer unknown.
Built order management, the remaining 195+ supplier scrapers (with shared base class + per-vendor overrides), failure detection + auto-rerouting, customer notifications, and the payment reconciliation engine. Weekly Friday demos showed each system going live behind a feature flag. By week 8, real orders were flowing through the system in shadow mode alongside the manual process.
Built per-supplier health checks (so a scraper breaking doesn't break the whole pipeline), tuned escalation rules, added monitoring dashboards, and stress-tested at 5× expected daily load. Caught and fixed three categories of failure modes the manual process had been quietly absorbing for years.
Cutover from manual to automated in stages — first 25%, then 50%, then 100% of orders flowing through the new pipeline. Trained the (now-redeployed) ops team on the exception-handling dashboard. Wrote the runbook. By the end of week 16, the manual process was off and the platform was the system of record.
90 days post-launch, the numbers were undeniable. The same client team is now shipping more, recovering more, and freed from the work that was eating their week.
From a manual cap of ~100 orders/day to 250+ orders/day automated — without adding headcount.
Five-person order-chasing team redeployed to higher-leverage work. No layoffs — better problems to solve.
Failed orders that used to leak off the P&L now self-heal via alternate-supplier rerouting before customers see them.
Every vendor scraped live for pricing and inventory. Stock availability is always current, no human lookup needed.
Multi-warehouse routing logic picks the best fulfillment path per order based on stock, distance, and lead time.
From end-of-day reconciliation (hours-to-days lag) to within-seconds detection on stock issues, payment mismatches, and supplier failures.
If your business runs on multi-vendor coordination, manual reconciliation, or any process where ops people are doing what software should — we'd like to talk. We respond within 24 hours with a clear scope, timeline, and price.