AI-Driven ERP Suite (White-Label)
A modular platform that infuses advanced AI into every core of enterprise resource planning—sales, supply chain, finance, HR, and maintenance. Built by Hanar AI for rapid re-branding and seamless cloud or on-prem deployment, the suite turns your ERP from a record system into an autonomous decision-making engine.
| Module | What It Delivers |
|---|---|
| 1. Demand Forecast & Inventory Optimizer | ML models predict sales at SKU / location level and auto-recommend reorder points, slashing stockouts and overstock. |
| 2. Smart Procurement & Supplier Recommender | AI scores vendor offers on price, lead time, and quality, then suggests the optimal supplier for each PO. |
| 3. Dynamic Pricing & Revenue Engine | Real-time price recommendations driven by demand, costs, and competitor moves—boosting margin without manual tweaks. |
| 4. Predictive Maintenance & Asset Health | Sensor analytics foresee equipment failures and schedule just-in-time service, reducing unplanned downtime by up to 30 %. |
| 5. Cash-Flow & Financial Forecasting | Recurrent neural networks project receivables, payables, and liquidity weeks ahead for smarter treasury moves. |
| 6. Fraud & Anomaly Detection | Models flag suspicious invoices, duplicate payments, and abnormal transactions in milliseconds. |
| 7. HR Talent & Attrition Analytics | NLP + classification predict turnover risk, shortlist best candidates, and recommend retention actions. |
| 8. Document AI & Conversational Assistant | OCR + NLP auto-ingests invoices and POs, while a chat agent answers questions (“Inventory for SKU 123?”) and generates reports on demand. |
Deployment Highlights
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White-label ready: custom logo, colors, and domain in days.
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API-first & headless: hooks into SAP, Oracle, Microsoft Dynamics, or bespoke ERPs.
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Cloud / on-prem / hybrid: AWS, Azure, GCP, or local datacenter for compliance.
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MLOps baked in: CI/CD pipelines, drift monitoring, and automated retraining.
Business Impact: early adopters cut working-capital tied in inventory by 18 %, lowered maintenance costs 25 %, and achieved invoice-processing accuracy of 98 % within the first quarter.