Monolith is the only AI platform built by engineers for engineers and trusted by the world’s top engineering teams including BMW, Honeywell and BAE. Its advanced capabilities analyse complex engineering data sets quickly and accurately to help you find relationships, new insights and anomalies so you can trust your data fast. With its user-friendly, no-code interface, domain experts can quickly get up to speed and leverage institutional knowledge for future generations.

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There are five ways Monolith delivers superior performance to statistical and simulation methods. 

1. Increased accuracy: advanced machine learning algorithms identify complex patterns and relationships in engineering datasets for more accurate predictions and insights
2. Faster analysis: algorithms are optimised for speed, allowing it to analyse large datasets much more quickly
3. More comprehensive analysis: analyse both structured and unstructured data
4. Adaptive learning: continually refine and improve the accuracy of predictions
5. Ease of use: no code environment built by engineers for engineers

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Most often, you need less data than you think! 100s of rows and a handful of columns for a tabular data approach can already get you going on your journey to adopt AI. If you don't have enough data for an AI project, we recommend the following: 

1. Data augmentation: Create new training examples from existing data by applying transformations or scaling to existing data (this is how our customer Kautex Textron did it).
2. Transfer learning: Use and reuse data gathered from an old product to improve a new product.
3. Consider a simpler model: A simpler model requires less data to train. The best approach will depend on the specific task and constraints of your project.

Our mantra: Start small - but start! Find a small, incremental quick win, and let our AI team of experts guide you on your way forward to intelligently acquire more data and build even more impactful models.

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Monolith reduces design cycles by offering rapid analysis, accurate predictions, custom models, collaboration, and optimization capabilities. Its algorithms are optimized for speed, enabling quick iteration and testing of multiple scenarios. Custom models capture domain-specific knowledge, while collaboration and optimization capabilities improve effectiveness and identify the best design options. By leveraging Monolith's capabilities, teams can deliver better designs more efficiently.

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Monolith's self-learning models automate repetitive or time-consuming tasks, allowing teams to focus on more complex work. However, data quality, model drift, and interpretation are critical considerations, all supported by Monolith. Teams may need to update or retrain models to account for changes and understand their decision-making processes. By monitoring and managing these factors, teams can ensure ongoing value from Monolith's AI models.

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