Zero Zeta applies AI across materials and utilisation, process operations, quality inspection, material compliance, engineering documents, procurement, sales and cross-functional executive decisions. These solutions bring workflow context and evidence-backed decision support together while preserving human accountability.
Choose a solution based on the business challenge your team wants to solve.
Treats remnants as live inventory, matches required parts against available remnants, and supports material-saving cutting workflows.
Supports shop-floor coordination, scheduling, operational visibility, alerts, and multilingual or WhatsApp-first workflows where applicable.
Supports pre-pour casting defect-risk prediction, explainable parameter insights, and human-reviewed process adjustments.
Uses Zero Zeta's proprietary computer-vision approach for dimensional inspection, reject analysis, marking, drift detection, and batch-quality intelligence.
Extracts, interprets, and validates Material Test Certificate information, including chemical composition and mechanical-property evidence, for compliance, traceability, and supplier-quality review.
Interprets engineering drawings, identifies components and assembly relationships, and produces structured BOM recommendations for engineer review.
Brings supplier performance, pricing, quality, delivery, contract, and risk evidence together for human-led sourcing decisions.
Supports cross-location offcut visibility, shape matching, nesting, and material reuse.
Connects lead capture, ownership, follow-up, customer-conversation insights, and pipeline visibility.
Unifies cross-functional information, detects anomalies, supports conversational analysis, and explains root causes for human-led executive decisions.
Industrial organisations often suffer from data fragmentation—critical information remains locked in separate databases, operational spreadsheets, and static engineering drawings. Applied Industrial AI provides active, workflow-aware decision support across materials, quality, documents, suppliers, customer operations, and leadership decisions, rather than retrospective reports.
Connects operational context across planning, floor activity, quality compliance, and follow-up alerts.
Supports faster decisions using model-driven insights, rules, and operational logic.
Designed for practical use, including mobile widgets, conversational analytics, or low-friction update logs depending on the use case.
Works with source data such as drawings, BOMs, test certificates, transaction ledgers, quality signals, and process parameters depending on the use case.
Helps teams understand why a recommendation, prediction, or risk signal appears.
Supports pilot planning, feasibility review, data security checks, and phased rollout.
We work to target the specific bottleneck—such as material waste, scheduling delays, invoice backlog, or procurement risks.
Audit available logs, parameter history, drawing formats, and workflow files surrounding the process.
Define a narrow, measurable pilot scope around a single process, material type, or workflow category.
Deploy in mock or workflow-sync mode to measure prediction accuracy, operational value, and user adoption.
Align user training, system integrations, and data governance limits for full deployment.
For broader AI enablement across business teams, functions, and leadership workflows, explore Zero Zeta's Enterprise AI solutions.
Explore Enterprise AI