Anyone encountering for the first time learns how fundamental intention is to our work in AI. We intend to create a world without waste. We’re a leading Enterprise Artificial Intelligence® companyfocused on the core of theglobal economy – how companies make things and move thingsNot only are we incredibly proud of the company we’ve built in the past three years, we have a great, nuanced story to tell about how we go to market in this crowded, noisy artificial intelligence space. When you are full of purpose and have a clear value proposition that allows customers to realize tangible business results within months of implementation, you should be proud.  

What is our brand promise? How do we deliver value? How do we communicate that promise sufficiently well to provoke curiosityengagement from prospects, partnersanalysts and loyalty and advocacy from our customers?  

Our Promise is built around our not-so-secret plan to create a world without wasteOur proven business model configures and implements AI-as-a-Service enterprise applications and a platform built for the AI use case that delivers business value and reduces industrial waste (FYI93.7% of the waste in the world is industrial).  

Why was the company founded? Back in 2016, it was clear that advances in algorithms, data, and supercomputing created the perfect storm that made enterprise AI- solutions possibleNprovider was addressing the shortcomings of legacy software head-on. What limitations are inherent in traditional software? It’s rigidly rules-based, blind to outside data that impacts outcomes (e.g. weather conditions, tire wear), it lacks the capability to learn and iterate based on actual data and is deterministic, not probabilistic. This results in extreme business inefficiencies: increased buffer stock, lost revenue, wasted working capital, wasted transportation costs and energy usage and, notably unnecessary CO2 was founded to focus where the waste is – in factories, fleet and transportation networks, warehouses, and supply chains. Enterprise AI

 Noodle’s applications act as a system of intelligence integrated with customers’ current ERP, CRM, MES, and SCADA systems– unlocking hidden business value while eliminating waste.   

Our work across our customer base has yielded reductions in things like scrap material, deadheads, excess inventory, inter-DC transfers and in-field repairs. We calculated that these customer implementations of software has an annual impact of eliminating 180,000 tons of CO2 in 2018, the equivalent of taking 37,000 cars off the road, or 10% of the cars in San Francisco!  

Our Applications and Platform Use Data Science to Deliver Real Value  

On top of all that environmental goodness,’s products offer full stack enterprise artificial intelligence to manufacturers and supply chains. We’ve developed four apps for manufacturing and five for supply chains that provide a probabilistic system of intelligence on top of customer’s current enterprise softwareCompanies we serve are in these industries: transportation & distribution (3PL and private fleet), manufacturing (process) and supply chain (fast moving consumer goods and consumer durables). Noodle ingestcustomer data – both internal and relevant external datacreates a solvable problem statement, applies the most-suitable AI/ML algorithm, and then proceeds to sense, predict and recommend actions that reduce costs, increase efficiency, and capture lost revenue Enterprise AI

Noodle’s applications can be purchased to run in a customer’s current tech stack or can be combined with purchase of our Enterprise AI® platform, comprised of an edge platform (gateway and servers) and a data platform, developed to ingest OT and IT data (L0-5)and drive actions in manufacturing settings at the edge, in real time. The Enterprise AI® Platform is the only platform built specifically for AI use cases, resulting in both increased performance and reduced cost.   

Let’s take a moment to talk about our data science approach. Noodle’s apps sensethe current state of a customer’sbusiness, predict what is likely to happen and why, then recommend decisions to the ops and planning teams of our customers. The “sense, predict and recommend” paradigm, or “Deep Probabilistic Decision Machine,” leverages data science methods that fall within the category of machine learning:  

  • Sensesthe current state of your businessSensory enginebased ondeep learning;representing the current state of the business or industrial process as the compressed features or automatically extracted key hidden features inferred from high-dimensional historical experiences (actions, conditions, observations and utilities) over time. 
  • Predicts what is likely to happen and whyPrediction enginebased on Bayesian probabilistic learning; simulating the future probabilistic paths of observations and utilities from the predicted states over timefor any given future conditions and actions, then updating the current state given the actual observation. 
  • Recommends decisions for you to takeDecision enginebased on reinforcement learning or model predictive control;taking the optimal action to maximize utilities or KPIs in a particular state and condition. 

Customers have implemented applications to realize benefits such as: 

  • Consumer Goods: Improved fill rates lead to $275M in lost sales recovery over 3 years 
  • Manufacturing: Saved $10M annually from enhanced transportation planning, network efficiencies, and production coordination 
  • Transportation: Projected savings of $3M (7%) of maintenance and purchased transportation costs over 3 years by transitioning asset maintenance from static and scheduled to predictive and dynamic  

Communicating the promise of a complex technology 

The job of marketing Noodle’s AI portfolio is multi-layered because of the need to communicate different value propositions depending on the buying center we’re speaking to, which can range from the C-Suite: CEO, CIO, Chief Data Scientist, Chief Analytics Officer, to LOB (e.g. VP of Supply Chain), to in-house data science teams, to the technical implementation teams to the software and hardware procurement teams. Enterprise AI

Our differentiators directly appeal to the pain points of decision-makers in our target industries. Our marketing must address the technical superiority of our offering (supercomputing backbone and data science), our AI-as-a Service model and the deep knowledge our team has of the industry-specific pain points our customers can solve with AI.   

The implications for the Marketing team? Each member of my team is able to bring the full value of their individual expertise to bear with a direct line from what they create to both revenue and customer care: 

  • Content marketing is critical and complex as it must address all these audiences in the voice that speaks to them 
  • Customer case studies are vital in an emerging space where the testimonies of first-movers give fast-followers confidence 
  • Third party analysts who validate our position become trusted advisors 
  • Channel mix matters, both online and offline, as finding first-mover and fast-follower enterprise buyers who are seeking a competitive advantage means we have to be smart as we prioritize our spend to reach the most likely buyers.  
  • Channel partners like Dell EMC (a investor and channel partner) are key to opening doors where trusted customer relationships already exist. 

Conclusion owns a unique space in the AI market – a brand with a purpose and intention as strong as its products. 

We are hell bent on creating a world without waste, and as we deploy our solutions, we will factor that intention into the way we approach and measure our impact. 

We’ve got a well-constructed product offering that focuses on where the waste is, where the data is quantitative, abundant, and steers clear of creepy and ethically questionable personal data issues. 

The data science behind our AI is well constructed, patentpending, and executed by our tremendous team of in-house data scientists.  

Our marketing is more complex than that required when selling straightforward technology like CRM. Our ideal customer has a growth mindset and is willing to learn and iterate with us. Finding them, engaging them and making them customers for life in this exciting AI space is the thing that gets me hopping out of bed every day saying, “Let’s go!” 


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