AI built to connect the supply chain’s domain and language
Supply chain data has a major problem: multiple "sources of truth.” You need models that can unify and standardize disconnected data so you can automate work and cut costs.
Not ChatGPT's generic models
Our domain-driven approach is powered by logistics-AI that breaks down complex data into our atomic task system so you can automate work and control costs.
Built for the supply chain
Models created for the supply chain’s domain and language so you can finally understand your fragmented data.
Trained on your data
True logistics-AI is trained on your network’s documents and data so it’s contextualized to your supply chain. This means it becomes more accurate and efficient as your network evolves and grows.
Produces insights
Every supply chain stakeholder tracks, names, and defines things differently. Trying to reconcile this with rigid technology and manual workflows is a mess. AI-driven data management is dynamic, so it can easily handle different taxonomies and terminologies.
Powers automation
Logistics-AI constructs a comprehensive but flexible view so you can automate work and assess your business at any level (network spend, cost per accessorial per carrier, cost per product, etc).
Built for the supply chain
Ask for a reference number for a shipment and you’ll get several answers: shipment ID, PO #, carrier pro number, order number. The supply chain's mass fragmentation and low standardization across stakeholders and systems means is hard to understand, let alone reconcile.
Logistics-AI addresses these challenges by contextualizing, categorizing, extracting, standardizing, and linking all your data. To succeed today, you need a comprehensive view of your transportation spend to properly control costs and reduce risks.
Trained on your data
Our models are trained on your data so you get a comprehensive but flexible view of your business (network spend, cost per accessorial per carrier, cost per lane, cost per product, etc.). We use multiple models that have a consensus mechanism to run accuracy checks with human experts in the loop if needed.
Our platform can easily take in new data formats and sources, so you can easily activate new carriers and always get the most accurate view. The best part? Our models become faster and more accurate as your network grows.
AI-powered extraction and standardization
Trying to connect the dots across shipments and documents can make you feel like you’re looking for a needle in a haystack.
Logistics-AI’s dynamic data management extracts, contextualizes, categorizes, and standardizes all of your data. This allows the Loop platform to filter down to a tracking-ID level or pull the aggregate data to show you top accessorials across carriers.
Teams today need this flexibility so they can get a comprehensive view of their supply chain and spend data.
Unlocking the power of connected data
Loop’s comprehensive view of your transportation and financial data means our platform can automate work and uncover insights across your network. From running scenario planning to assessing how you should allocate shipment volume to identifying your highest cost buckets so you can focus on your contract negotiation wisely.
Logistics-AI’s connected data empowers you to optimize your team, carrier, and network performance.
Not a generic LLM
We get much higher accuracy fine-tuning our own models and this level is what our customers need. Generic API based models, like Chat GPT, cannot handle supply chain tasks with enough precision and speed.
Human in the loop approach
When we build a new model, we always construct and train it with three humans involved so we can validate its performance.
Multi-model decision making
To arrive at a decision, Loop utilizes multiple models, general-purpose models and fine-tuning models to ensure there is consensus.
Atomic task system
A system that breaks down goals into smaller, manageable atomic tasks so our platform can automate work.
Computer visions models
Our models that take in image inputs and convert them to text outputs.
Large language learning models
Our homegrown models that take in text inputs to understand their context and meaning to create outcomes.
Data categorization
Identify each document type to validate it and ensure compliance.
AI-driven extraction
Configure your extraction and validate with human oversight to ensure accuracy.
Data standardization
Normalize data from every carrier regardless of how they name their service-levels, detail their line-hauls, or write “fuel surcharge”... “fuel,” “FL Sur”, “FS,” etc.
Connect your supply chain’s unstructured data
Logistics-AI constructs a comprehensive but flexible view so you can automate work and uncover insights at every level (network spend, cost per accessorial per carrier, cost per product, etc).
Is your system operating in the 1980s, 2000s, or 2020s?
Today’s transportation and finance teams deal with more challenges than ever yet they are STILL managing their work and their insights the same way they did 30+ years ago.
Still not sure about AI?
See how logistics-AI has transformed our customers’ business.
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Frequently asked questions
Unlock profit trapped in your supply chain.
Chat with our team of supply chain and automation experts to get started today.