MODEL OPTIMIZATION IN A BOX FOR
DATA OBSESSED TEAMS

Collect high quality training data from your product at scale, over time. Roll out new models to customers quickly, without deploying new code. Evaluate your model's performance and its impact on your business

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Model requirements defined in code

Accurate, well-structured data is a necessity for machine learning models. Get everyone on the same page with Causal’s shared data model. Define what data needs to flow in and out of your model, and keep it updated easily through your own revision control system.

Prevent breaking changes to your data pipelines

Causal’s data model provides built-in data governance by preventing accidental changes to the front-end that damage inputs to ML models. Custom, typesafe SDKs ensure that no one on the team can break your pipelines.

Gather model inputs without burdening your front end

Causal's plug-in architecture makes it simple to incorporate any data source as an input to your models. Use data from any source you need, without having to pipe it through to your front end.

Stop cobbling together error-prone ETL processes

Causal gathers all the data needed for your model, passes it into the model, and passes the model responses back into your product. As that happens, it records all the data you need and delivers it to your data warehouse in exactly the format needed for training. You get accurate data with zero production-training skew and no chance your model can “peek” into the future.

A/B test new models without writing or releasing code

Causal's web tools make it easy for anyone to swap in new models, without needing to write or deploy new code. Anyone on the team can easily roll out a model, load test a new model by gradually increasing its traffic, or A/B test new models.

Instantly reuse model inputs

Once you've set up a model input for one model, it can easily be reused in any other model. No additional data engineering effort is required.

Evaluate both model performance and business impact

Causal supports common ML performance metrics like NDCG and MRR out of the box. It also allows you to monitor your models using business metrics, like revenue per user or user retention.

See features as your users do and release with confidence

Causal's QA tools make it easy to view experimental versions of your app experience and make sure your tracking code is set up correctly before you roll it out. With Causal's Event Viewer, you can see how data will flow to your models.

We integrate with your existing tech stack

Why product dev
teams love Causal

We had four A/B testing platforms—none of which met our needs—and I’ve been trying for years to unify them and solve our problems across our stack. We’ve started eliminating those other platforms and Causal has increased the velocity of our experimentation.”

Tony Deigh
Co-Founder & Chief Data Officer, Jobcase

With Causal, I don’t have to worry about the data being off. So I can be more productive around new things to implement and not have doubts around pushing out experiments.”

Bade Adebayo
Staff Software Engineer, Summari

Build, ship and optimize consumer tech products
without stitching together half a dozen tools