AI Demand Forecasting
Prophet + LSTM ensemble predicts 6-12 weeks ahead per SKU/location — 8-15% MAPE vs. 35-45% for manual planning.
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Explore our services and discover how we can help you achieve your goals
Explore our services and discover how we can help you achieve your goals
NextMFG replaces spreadsheets and gut-feel planning with AI that forecasts demand, predicts production output and machine failure risk, restocks inventory with ML-driven reorder points, and keeps every sales channel in sync — returns included.
/05 Powered by innovation · trusted by businesses worldwide
/01 What we build
Forecasting, stocking, scheduling, and maintenance each run on a purpose-built ML model — not one generic dashboard.
Prophet + LSTM ensemble predicts 6-12 weeks ahead per SKU/location — 8-15% MAPE vs. 35-45% for manual planning.
Learn moreA reinforcement-learning reorder-point model recomputes safety stock and order quantities per SKU every day, factoring demand volatility and supplier lead-time variance.
Start a projectGradient-boosted models predict cycle time, yield, and delay risk for every job before it's scheduled, then auto-sequence the queue.
Learn moreOne inventory source of truth across Shopify, Magento, Amazon, Flipkart, WooCommerce and POS — auto-disables sales at zero stock.
Learn moreFull RMA workflows — authorization, inspection, refund/exchange, and restock automation by disposition.
Learn moreIsolation Forest models watch machine telemetry and quality batches in real time, flagging failure risk before it escalates, and auto-opening work orders with root-cause context.
Learn more/02 How it works
NextMFG deploys in days via Docker — connect your channels, and the models start learning from your first sync.
Sales channels, POS, supplier records, and production history sync into one hub — no rip-and-replace.
Forecasting, stocking, and scheduling models train on your SKUs, seasonality, and lead times.
Reorders trigger, jobs sequence, and at-risk batches get flagged — automatically, with confidence scores.
Every decision is reviewable on the dashboard; approve, override, or let it run hands-free.
/03 AI inventory
Three purpose-built models run continuously against your live data — each decision traceable, each model retrained as your factory changes.
Manual planning guesses once a quarter. NextMFG’s ML engine re-evaluates every SKU, every day: what you’ll sell, what to hold, and when to reorder — balancing service-level targets against holding costs and supplier lead-time variance.
The result: 18-28% less excess inventory, fewer stockouts, and purchase orders that generate themselves from the forecast.
See it on your dataProphet + LSTM ensemble on historical sales, seasonality, and external signals — 12-week predictions per SKU/location.
Daily recalculation from demand variance, service-level target, lead-time variance, and holding cost — no fixed buffers.
Isolation Forest on historical patterns catches demand spikes, supplier delays, and unusual batches — alerting ops in real time.
/04 Omni-channel
Multi-store setups usually mean five stock counts and five versions of the truth. NextMFG makes the hub the single source — stocking, sales, and customers optimized together.
/05 Why NextMFG
/06 Integrations & extensions
Channels, couriers, accounting, and payments connect out of the box — and REST/GraphQL APIs, webhooks, and CRM sync extend NextMFG to anything custom.
/07 Roadmap
/03 Tech stack
Node.jsNENestJS/12 Client testimonials
Vyrazu built an AI agent that handles our entire supplier negotiation workflow. What took a team of 6 analysts now runs overnight, autonomously, with better outcomes than we achieved manually.
Their LLM-assisted migration tool cut our 18-month COBOL replatform to 11 weeks. The AI-generated tests caught edge cases our engineers hadn't even thought of. Genuinely impressive engineering.
The RAG pipeline they built pulls from 40,000 internal documents in milliseconds. Our support team now resolves tickets 3× faster and customer satisfaction scores have jumped 28 points.
We came in sceptical about AI agents in production. Vyrazu's reliability engineering approach — guardrails, observability, fallbacks — gave us the confidence to go live. Zero incidents in 8 months.
Their fraud detection model now catches 99.7% of fraudulent transactions at 2M+ per day. It paid back its entire development cost within the first 6 weeks of going live.
Vyrazu treats AI ROI as a first-class requirement. Every model ships with a business metric attached — cost saved, decisions automated, revenue influenced. That discipline is rare and exactly what we needed.
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