The Challenge
Manual processes and data silos limit operational performance
Exception-driven workflows, fragmented data, and reactive decision-making compound into systemic operational inefficiency.
Process Bottlenecks
High-volume operational processes, purchase order processing, invoice matching, work order routing, create backlogs that slow the business and frustrate customers.
Exception Management
Operators spend most of their time on exceptions, the 20% of cases that don't fit standard process flows. AI should handle the 80% automatically, freeing humans for the complexity that requires judgment.
Reactive Supply Chain Management
Supply chain disruptions are identified after they impact operations. Weeks of downstream impact could be mitigated with earlier supplier risk signals and automated mitigation playbooks.
Operational Data Silos
Operational data sits across ERP, WMS, TMS, MES, and CRM systems with no unified intelligence layer. Leaders make decisions from incomplete or stale information.
Education AI Solutions
AI systems for operations, supply chain, and process excellence
Intelligent Process Automation
AI-orchestrated workflows that process purchase orders, work orders, and operational requests end-to-end, with exception handling and human escalation built in.
Supply Chain Risk Intelligence
AI monitoring of supplier health, logistics disruptions, geopolitical events, and commodity signals, surfacing risks 3–6 weeks before they impact operations.
Demand-Driven Inventory Optimization
ML models that set optimal safety stock levels, reorder points, and replenishment quantities by SKU-location, balancing service levels against working capital.
Operations Command Center AI
Unified AI dashboards that aggregate ERP, WMS, TMS, and sensor data into real-time operational intelligence, alerting operations leaders to issues that require action.
Vendor Performance Management
AI that tracks supplier on-time delivery, quality metrics, and communication patterns, generating automated scorecards and surfacing underperforming vendors for corrective action.
SLA Breach Prediction
ML models that predict which service requests and deliveries are at risk of SLA breach 48–72 hours in advance, enabling proactive intervention before penalties or escalations.
End-to-End Implementation
End-to-end AI implementation for operations
Solnix delivers the full lifecycle, from opportunity mapping through production deployment and continuous improvement. The same proven methodology powers every operations engagement, tailored to your systems, data, and workflows.
Discovery & AI Opportunity Mapping
We start by understanding your operations, data landscape, and goals, then map where AI delivers measurable value and where it does not. Every engagement begins with a prioritized opportunity backlog, not a technology pitch.
Data Foundation & Readiness
AI is only as good as the data behind it. We assess data quality, connect fragmented sources, and build the secure, governed pipelines that production AI depends on, with privacy and compliance designed in from the start.
Model & Agent Development
We build the models, retrieval systems, and AI agents tailored to your use cases, selecting the right approach (fine-tuning, RAG, multi-agent orchestration) for accuracy, cost, and latency, and validating against your real-world edge cases.
Integration & Workflow Embedding
AI only creates value when it lives inside the tools your teams already use. We embed models and agents into existing systems, surfaces, and workflows, so adoption is natural and human-in-the-loop controls stay in place.
Deployment, Security & Compliance
We deploy to production with the security, monitoring, and compliance controls enterprises require, including bias and fairness testing, audit logging, and the observability needed to operate AI responsibly at scale.
Optimization & Continuous Improvement
AI systems improve with use. We measure outcomes against the goals set in Phase 01, retrain and tune from live feedback, and expand to the next set of use cases, turning a single deployment into a compounding capability.
Methodology
How Solnix Builds Operations AI
01, Process Mining & Discovery
01, Process Mining & Discovery
We analyze your ERP event logs to map actual process flows, identifying deviations, bottlenecks, and automation opportunities that aren't visible in process documentation.
02, Data Integration Architecture
02, Data Integration Architecture
Operations AI requires unified data from ERP, WMS, TMS, and IoT systems. We design the integration architecture that creates a real-time operational data layer for AI.
03, Exception Handling Design
03, Exception Handling Design
Every automated process needs clear exception escalation logic. We design the decision trees and escalation workflows that ensure AI handles the routine and humans handle the judgment calls.
04, Operator UX & Change Management
04, Operator UX & Change Management
Operations AI must fit into shift-based, high-pressure environments. We design operator-facing UX for the factory floor, warehouse, and logistics hub, not the corporate office.
05, SLA Monitoring & Continuous Improvement
05, SLA Monitoring & Continuous Improvement
Post-deployment, we track automation rates, exception volumes, and SLA compliance as primary success metrics, driving continuous improvement of process automation coverage.
FAQ
Questions from COOs, VPs of Operations, and supply chain leaders
Get Started
Automate the Routine. Master the Exceptions.
Solnix builds operations AI that eliminates manual bottlenecks, makes supply chains resilient, and gives operational leaders real-time intelligence to act faster.
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