Causa - Optimise Anything with Causal ML

What is Causa?

Causa provides a platform engineered for simple, easy, and fast integration of causal machine learning. The platform allows businesses to build, deploy, and manage causal ML models with minimal code.

Causa's cloud-native architecture eliminates infrastructure overhead. Integration is streamlined through a flexible SDK and API, compatible with popular environments like Python and Node.

Features

Simple: Designed for quick and easy integration without requiring specialized expertise.
Cloud-native: Infrastructure is handled on Causa's cloud, no server configuration or management needed.
SDK: Integration available with popular environments like Python, Node, and with a REST API.
Best Actions: Identifies optimal actions to achieve goals, considering complex constraints.
Simulate Actions: Allows simulation of potential outcomes before taking action.
Experiments: Conducts adaptive experiments that conclude upon sufficient data collection.
Custom Plans: Offers customizable plans suitable for varying organizational needs.

Use Cases

Optimizing yield and minimizing waste on production lines.
Reducing cloud costs while improving user experience.
Safeguarding supply chains against disruption and improving efficiency.
Managing energy usage efficiently and with minimal waste.
Minimizing the impact of disruptions on project management workflows.
Reducing delays, preventing fines, and optimizing efficiency in transport networks.
Allocating assets for optimal returns while minimizing downside in portfolio management.
Optimizing experimental design to minimize time-to-result in clinical trials.
Maximizing crop yield and minimizing resource use in precision farming.

FAQs

How is causal ML different from standard AI/ML?

In short: causal ML is the *only* AI that is appropriate for decision-making, and knowing what actions to take to shape outcomes.All standard (non-causal) AI, including LLMs and deep learning, is based on correlations in the data, rather than real-world cause and effect. The result is that standard AI can only forecast, it is not appropriate to for understanding what actions to take in the real world. Causal ML understands how the different parts of your problem interplay, and will give you the actual optimal actions that you should take to improve your business outcomes. Causal ML is also great as forecasting, and is far more robust than standard AI to changing situations.See our blog for more information.
Do I need masses of training data?

No. Unlike LLMs and deep learning approaches, our causal Bayesian engine is extremely efficient and does well with both small and big data. Our approach also allows human knowledge to be baked in to the AI before it has even seen the data.
What support do you provide?

Our platform makes it easy for anyone to develop causal models by dragging and dropping variables, along with GUI and LLM support, but our ML Applied Scientists also work closely with you to partner in building your initial model, and provide technical and user ongoing support. Our ML Engineers will then support with integration into your software. You'll be surprised at how easy it is to build and integrate powerful causal ML into your product.
What is your pricing?

We offer a flexible pricing model based on your specific needs. This ensures fair and customised pricing that aligns with what's best for your business.Rest assured, it's cheaper than hiring a team of data scientists specialised in causal ML, let alone also needing to hire cloud engineers to host it.
Why not use open source?

We built our proprietary causal ML platform from the ground-up because of severe limitations businesses were experiencing with open source offerings. Better functionality: CausaDB has core most-used functions that are not available in open source. Cloud-native: Forget employing expensive cloud engineers. CausaDB takes care of cloud infrastructure for you (or we can help you deploy locally or on your cloud). Support: We provide onboarding and continuous support, alongside our comprehensive and easy to use documentation. Our ML Applied Scientists work closely with you to partner in building your initial model, and provide technical and user ongoing support Service level agreements: We can provide service level agreements to guarantee performance. Developer experience: CausaDB requires much less code in a simpler format than open-source alternatives. Causa platform: We provide a no-code graphical interface to build causal models, manage your models and data, and make deployments. Uncertainty quantification: Our Bayesian approach natively incorporates level of certainty into the recommended actions. This is poorly supported in open source, but is essential for e.g. making decisions in the face of uncertainty, and safety-critical scenarios where you want to minimize the worst-case scenario. Features that you need: We prioritize your businesses specific needs and make our causaDB highly functional for your use case. Our platform already incorporates exclusive features developed from valuable customer feedback, offering capabilities beyond those found in open-source solutions.