Predictive Supply Chain Intelligence, Built Around Your Data
Seaflux helps logistics, retail, manufacturing, and distribution teams replace static spreadsheets and reactive firefighting with AI models that predict delays, forecast demand, and flag risk before it hits the bottom line.
Why AI-Powered Supply Chain Solutions Matter Now
Rule-based ETAs, manual check calls, and static reorder points were built for a slower, more predictable supply chain. Rising freight volatility, thinner margins, and customer expectations for real-time updates have made reactive visibility a competitive liability, not just an operational inconvenience.
Supply chain AI solutions close that gap by turning live telematics, ERP, WMS, and historical transaction data into continuously updated predictions, risk scores, and recommended actions, giving operations, procurement, and customer service teams a head start instead of a fire to put out.
Business Challenges We Solve
Modern supply chains generate vast amounts of operational data, yet many teams still rely on static ETAs, manual carrier calls, and reactive workflows. Our AI solutions help organisations overcome:
Static ETA calculations that ignore traffic, weather, and carrier history
No early warning before shipments become delayed
Hours spent chasing carrier updates
SLA penalties and avoidable escalations
Fragmented visibility across TMS, ERP, WMS, and telematics
Limited insight into supplier and shipment risk
Choosing the Right AI for Every Supply Chain Problem
Instead of applying one AI model to every business problem, Seaflux combines traditional machine learning, forecasting models, optimisation techniques, AI agents, and LLMs where each delivers the greatest value.
ETA prediction
High accuracy, explainable predictions, low latency
Delay prediction
Learns from traffic, weather, dwell time and carrier history
Demand forecasting
Captures seasonality and demand patterns
Risk scoring
Continuous shipment risk evaluation
Operational assistant
Summaries, recommendations, escalation rationale
Large Language Models are valuable for operational assistance, but structured logistics predictions are better served by purpose-built machine learning models such as XGBoost and LightGBM. This hybrid approach delivers faster predictions, lower infrastructure costs, and greater explainability.
Our Supply Chain AI Expertise
Real-time shipment and inventory visibility using live GPS, ELD, and IoT telematics streams
AI-driven ETA prediction and dynamic risk scoring using XGBoost, LightGBM, and time-series models
SKU-level demand forecasting and inventory optimization across multi-location networks
Supplier risk and performance scoring built on lead time variance, financial signals, and delivery history
TMS, ERP, and WMS integration with platforms like MercuryGate, Blue Yonder, SAP, and NetSuite over REST APIs
Control tower dashboards giving ops, CX, and leadership one color-coded view of the network
AI agents that automate exception handling, escalations, and routine procurement workflows
How Our AI Platform Works
Signal ingestion
Real-time data pipeline
AI & ML layer
Decision agents
Operational surfaces
Dashboard view
Live maps, drill-down exceptions, confidence-aware ETA updates, and role-specific filters give teams a single operational picture.
Alert center
Context-rich alerts route into dashboard queues, email, Slack, and partner workflows with escalation rationale attached.
Real Results: Predictive Visibility in Action
For a mid-size US 3PL broker moving 30,000 loads a month, we replaced static ETAs with a real-time predictive visibility and risk alert engine in 10 weeks .
From delayed visibility to decision-ready operations
Fewer manual check calls and email follow-ups across dispatch, customer service, and procurement
Lower SLA penalties through earlier intervention on late or high-risk shipments
Better customer communication with proactive updates grounded in confidence-based predictions
More actionable executive reporting through one map-driven, filterable control-tower experience
Who Benefits Most from SupplyPulse AI
From freight brokers chasing carrier updates by phone to retailers balancing inventory across hundreds of locations, any team relying on manual status checks or static reorder rules is a strong candidate for predictive supply chain AI.
3PLs & Freight Brokers
Retail & E-commerce
Manufacturing
Distribution & Wholesale
CPG Brands
Cold Chain & Pharma Logistics
Our Business Model
Choose the engagement format that matches your rollout scope, operating cadence, and long-term ownership model.
Fixed-Scope Projects
For clearly defined visibility, forecasting, or integration initiatives.
Time & Material / Agile
For iterative model tuning, experimentation, and phased rollouts.
Managed AI-as-a-Service
Ongoing monitoring, retraining, and governance after go-live.
Why Choose Seaflux for SupplyPulse AI
We design the platform around your freight lanes, data realities, and operating workflows rather than forcing a one-size-fits-all product story.
How We Work: Our Supply Chain AI Framework
From data audit to production rollout, Seaflux delivers SupplyPulse AI as a practical operating system for predictive visibility rather than a disconnected experiment.
Map existing TMS, ERP, and WMS systems and assess data readiness before writing a line of code.Build ingestion pipelines connecting live telematics, transaction, and historical data into one unified layer.Train and tune forecasting, risk, and scoring models against your specific lanes, SKUs, and supplier mix.Ship the control tower dashboard and alerting workflows, with secure multi-user access from day one.Track model accuracy and business KPIs, retraining as lanes, suppliers, and demand patterns shift.Technology Stack
Built on modern data, ML, and application layers that can integrate with enterprise operations at scale.
Our Clients
We understand that your success is our success, and that's why we are dedicated to providing you with top-quality service and software solutions.
Frequently Asked Questions (FAQ): Get the Answers You Need
What is an AI-powered supply chain solution?
An AI-powered supply chain solution replaces static, rule-based tracking and forecasting with models that learn from live and historical data. Instead of a fixed distance-and-speed ETA or a fixed reorder point, the system continuously updates predictions using traffic, weather, carrier history, demand signals, and supplier performance, so teams see problems forming before they cause a missed delivery or a stockout.
How does Seaflux build supply chain visibility and risk alert systems?
We start by ingesting live GPS, ELD, and IoT telematics data into a unified shipment or inventory context. From there, ETA prediction and risk scoring models built on XGBoost and LightGBM evaluate dwell time, weather, and route conditions to assign a live risk level. Context-aware alerts then reach dispatch, procurement, or customer service teams through Slack, email, or a dashboard, before the delay or disruption actually happens.
Can Seaflux integrate with our existing TMS, ERP, or WMS?
Yes. We build integrations over REST APIs with platforms like MercuryGate, Blue Yonder, SAP, and NetSuite, so you keep your existing systems of record and add a predictive layer on top rather than ripping out your current stack. Integration scope is scoped during discovery based on your specific systems and data access.
How accurate is AI demand forecasting compared to traditional methods?
Traditional forecasting methods rely on historical averages and manual adjustments, which break down when demand patterns shift. AI models incorporate seasonality, promotions, lead time variance, and external signals, and are continuously retrained against real outcomes. In our logistics ETA work, this approach improved prediction accuracy from 72 percent to 91 percent, and similar gains are typical for demand forecasting when data quality is solid.
What industries does Seaflux serve for supply chain AI?
We build supply chain AI solutions for 3PLs and freight brokers, retail and e-commerce, manufacturing, distribution and wholesale, CPG brands, and cold chain or pharma logistics. Our cross-industry experience means we bring proven patterns from adjacent sectors rather than solving each problem from scratch.
How long does a typical supply chain AI project take?
A focused engagement, such as a predictive visibility and risk alert system, typically takes 8 to 10 weeks from discovery to go-live. Broader initiatives involving multiple integrations, demand forecasting across many SKUs, or a full control tower dashboard run longer, and are scoped in phases so you see working functionality early rather than waiting for a single big-bang release.
Do we need clean data before starting a supply chain AI project?
No. Data quality issues are common and expected, which is why our process starts with a discovery and data audit phase rather than assuming clean inputs. We assess what is usable, what needs pipeline work, and where governance gaps exist, then build the data foundation alongside the first working version of the system.
What is the difference between a control tower dashboard and a TMS?
A TMS manages the transactional work of booking, tendering, and executing shipments. A control tower dashboard sits on top of your TMS, WMS, and other systems to give a unified, predictive view across the entire network, including risk scores, forecasted disruptions, and recommended actions, which most TMS platforms do not provide natively.
Your Freight. Your Data. Your Predictive Edge.
Get a free assessment of where static tracking and manual processes are costing your supply chain the most, and what a predictive system would look like on your data.