AI agents for Manufacturing and Industry 4.0: CPQ quoting for MTO/ETO and OEE in natural language

Vantegrate is built for the nine typical sub-verticals of the sector: discrete manufacturing (auto parts, machinery, electronics, appliances, furniture), process manufacturing (food, beverages, chemicals, pharma, cosmetics), automotive industry (OEMs, Tier 1, Tier 2, auto parts, electric mobility), food (meat processors, dairy, beverages, baked goods), chemical (industrial, fertilizers, paints, gases, petrochemical), pharmaceutical (medicines, biotech, medical devices, supplements), metalworking and steel, industrial electronics, and industrial textile/apparel. It supports all five production models: MTS (Make to Stock), MTO (Make to Order), ATO (Assemble to Order), ETO (Engineer to Order) and continuous production.

No. Vantegrate integrates with existing systems: industrial ERP (SAP S/4HANA Manufacturing, SAP Business One, Oracle E-Business Suite, NetSuite Manufacturing, Microsoft Dynamics 365 Supply Chain Management, Infor CloudSuite Industrial, Epicor Kinetic, IFS Cloud, EGA Futura ERP), MES (Siemens Opcenter, Rockwell FactoryTalk, Ignition by Inductive Automation, AVEVA Wonderware, GE Proficy), SCADA (OSIsoft PI, Wonderware Historian), PLM (Siemens Teamcenter, PTC Windchill, Dassault ENOVIA), CPQ (Salesforce Revenue Cloud, SAP CPQ, Tacton, Configit) and quality systems (Sparta Systems TrackWise, MasterControl, Veeva Vault). The goal is to accelerate the operation without forcing the company to replace its stack.

Yes. Sellium integrates with Salesforce Revenue Cloud (formerly CPQ), SAP CPQ, Oracle CPQ, Tacton and Configit, and respects product configuration rules, dependencies between technical options and quote assembly with a realistic lead time against the master production schedule. For Make to Stock it confirms stock and immediate delivery; for Make to Order it checks the master schedule and returns a realistic manufacturing lead time; for Engineer to Order it captures the project parameters and routes to engineering with context, logging it in the CRM.

Yes. It supports catalogs with hundreds of thousands of part numbers, search by internal code, supplier code, description, technical spec, dimension, material, standard or cross-reference between brands. It is critical in auto parts (Brembo / Bosch / ATE / Donaldson cross-references), industrial hardware, chemical and industrial electrical. It automatically attaches SDS, technical data sheet and quality certificates (ISO 9001, IATF 16949) at the time of the quote.

Every project starts from an operating baseline and is measured with concrete sector indicators: quote-to-order ratio, quote response lead time, OEE by line/plant, throughput, FPY (First Pass Yield), scrap rate, rework rate, MTBF and MTTR of critical assets, OTIF to OEMs and retailers, fill rate, unit cost by SKU, industrial accounting close time, cost per document processed and audit pass rate (ANMAT, IATF, GMP, FDA).

Typical go-live for Sellium runs 6 to 12 weeks depending on the catalog, tiered commercial rules, CPQ complexity and the integration with the industrial ERP. Catalog modeling (part numbers, cross-references, BOMs, technical rules) is the most demanding phase. Arconte runs 6-12 weeks; Metrix 6-10; Revio and Trazzo have shorter timelines (3-6 weeks). For multi-plant companies with heterogeneous MES, the modeling phase for Metrix's semantic layer can extend further.

The AI agents integrate with the industrial ERP, MES, SCADA, PLM, CPQ, maintenance systems (SAP PM, Maximo, Fiix), quality systems (TrackWise, MasterControl, Veeva), industrial IoT platforms (AWS IoT, Azure IoT, GE Predix, PTC ThingWorx, Siemens MindSphere) and the data warehouse. They serve customers on WhatsApp with CPQ and credit terms validation; process technical documents (CoA, SDS, batch records) with OCR + LLM (IDP); answer business questions in natural language; and optimize B2B routes meeting dock windows at OEM plants. All without replacing current systems and respecting Industry 4.0 standards (ISA-95, ISA-88, OPC UA, MQTT).

Yes. It meets automotive traceability and documentation requirements, captures order data aligned with standard EDI (ANSI X12, EDIFACT, GS1 EDI), processes non-standard Tier 2 and aftermarket POs by converting them into ERP sales orders, and leaves an auditable trail for OEM audits. Trazzo meets strict dock windows at OEM plants to bring OTIF closer to the level those customers demand (large retailers like Walmart require 98% and Amazon 90% or more) and to reduce penalties. Metrix measures MTBF, FPY and unit cost by SKU for IATF 16949 reports.

Yes. It is one of the core use cases in chemical, pharma and food. It captures quality parameters, lot number, analysis date, methods used, and pushes them to the quality system (Sparta Systems TrackWise, MasterControl, Veeva Vault) or the ERP. GHS-compliant SDS stay indexed and available to all teams. For pharmaceutical traceability, it captures lot, expiration date, GTIN and SSCC on receipt of goods and pushes the information to the corresponding sector system (ANMAT Disposición 3683/11, ANVISA, INVIMA, COFEPRIS, DSCSA in the US). It processes batch records, master batch records and deviation reports for GMP/cGMP compliance.

Yes. Native connection with SAP S/4HANA Manufacturing and Business One, Oracle E-Business Suite, NetSuite Manufacturing, Microsoft Dynamics 365, Infor CloudSuite, Epicor Kinetic. On MES: Siemens Opcenter (formerly Camstar), Rockwell FactoryTalk ProductionCentre, Ignition by Inductive Automation, AVEVA Wonderware, GE Proficy. On SCADA: OSIsoft PI System, Wonderware Historian. On industrial IoT: AWS IoT, Azure IoT Hub, GE Predix, PTC ThingWorx, Siemens MindSphere. It cross-references vibration, temperature and consumption signals and generates predictive maintenance alerts.

Yes. Your implementation runs on Salesforce and Oracle Cloud Infrastructure, which maintain SOC 2 Type II, ISO 27001 and PCI DSS Level 1. Those certifications belong to the platforms, not to Vantegrate. We support the sector requirements: IATF 16949 (automotive), ISO 9001 (quality), ISO 14001 (environmental), ISO 45001 (occupational safety), ISO 13485 (medical devices), GMP/cGMP for pharma and food (FSSC 22000, ISO 22000), FDA 21 CFR Part 11 for data traceability in pharma, GHS-compliant SDS for chemicals, REACH and RoHS for electronics and chemical exports. We comply with Law 25.326, LGPD, LFPDPPP, GDPR, CCPA/CPRA and sector regulation (ANMAT, ANVISA, INVIMA, COFEPRIS, SENASA, FDA, DSCSA). It supports Industry 4.0 standards (ISA-95, ISA-88, OPC UA, MQTT, GS1).

Yes. It optimizes B2B routes with strict dock windows at OEM and large retailer plants (automatic penalties for low OTIF), manages loads with specific requirements (weight, volume, pallets, containers, hazardous materials with ADR/DOT/NOM/SCT, cold chain, oversized, fragile), prioritizes OEM customers with hard SLAs, and captures digital POD with pallet photo, receiver signature at the dock and geolocation. The goal is to bring OTIF to OEMs closer to the level those customers demand (large retailers like Walmart require 98% and Amazon 90% or more), sharply reducing penalties for low OTIF.

Automating industrial B2B quoting requires: integration with the industrial ERP and CPQ for stock, master schedule and technical rule validation; a catalog with part numbers, specifications and cross-references between brands; pricing rules by commercial tier (OEM, Tier 1, Tier 2, distributor, export); live credit terms validation; templates approved by Meta. Sellium quotes with a realistic lead time, attaches SDS and certificates, validates credit terms, captures the customer's PO and loads the order into the ERP. Revio analyzes each customer's historical purchase cycle and fires timely repurchase reminders with a pre-built order.

ROI is measured by combining incremental revenue (better quote-to-order ratio from answering fast and with technical accuracy, capture of quotes that used to arrive late, more industrial consumables repurchase across the segmented base, reactivation of dormant distributors, capture of after-hours industrial orders) with operating savings (industrial accounting close accelerated toward the range of days efficient operations report, sanitary audit prep dropping from days to hours, administrative FTEs freed up per plant, fewer unplanned stoppages with IoT predictive maintenance (plants with mature predictive maintenance markedly reduce unplanned downtime, with reactive maintenance associated with ~8.4% vs. ~5.4% for predictive per UpKeep 2024), penalties for low OTIF avoided, scrap and rework reduced with real-time visibility). Every implementation starts from a baseline and is measured with concrete KPIs: quote-to-order ratio, OEE, scrap, FPY, MTBF, OTIF, unit cost, audit pass rate.