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Manifold Labs: Building custom data & AI technology

Our software product development services accelerate building custom data and AI technology, using the modern data stack.

“Manifold achieved in 3 months and a team of 4 what could have taken 12 months and a team of 10. They have a unique ability to navigate the maze of product and architecture decisions for AI products.”
SVP Technology,
Global 500 Silicon Valley Lab
“From the beginning, the Manifold team set the tone for our work together and has consistently exceeded our expectations. This platform has given us the ability to own end-to-end model lifecycle, and empower multiple parts of the organization to consume these technologies in a scalable, reliable, and repeatable manner.”
VP Data Engineering & Analytics,
Fortune 1000 Company
“The joint discovery and development effort enabled us to realize multiple opportunities across the enterprise in the deployment of data and AI products.”
VP Data Science,
Fortune 1000 Company
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Production Machine Learning


We take machine learning models from experiments in a Jupyter notebook to production in the cloud. We follow a structured MLOps process for the entire modeling lifecycle, including Dockerized ML development, parallelized backtesting, ML API patterns, model explainability, model performance monitoring, and infrastructure as code modules for rapid deployment.

Data Science


We develop models to solve hard problems—from unsupervised anomaly detection in multi-variate time series to dynamic system identification using deep learning. We take a heterodox approach to data science: we start from first principles about the mathematical formulation of the problem and then experiment with the relevant methods—from modern hierarchical Bayesian methods, to gaussian processes, to deep learning, and sometimes to more classical techniques like Kalman filters. As with all good science, we start simple, experiment a lot, and iterate our way to the best possible solution.

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Data Engineering


We engineer modern data infrastructure, including purpose-built enterprise data platforms, complex data pipelines, batch and streaming data, sensitive data handling, serverless technologies, and more. In data science and machine learning, more and better data always beats better algorithms.