Manifold Labs: Building custom data & AI technology

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

Public Medical Device Company

We worked with a public medical device company to build a machine learning platform that enabled data scientists to put models into production—the forecasts of which are now leveraged by downstream applications.

Public Pharmaceutical Company

A public pharmaceutical company wanted to better understand their prescribers, so they could have more productive conversations and create better solutions. We helped them use the data they already had to set everyone up for success.

Wearable Device Maker

In 2020, we partnered with a wearable device maker to use wearable medical data to predict illness—even before symptoms showed up.

Payer Data Platform Company

We partnered with a payer data platform company to aggregate and process disparate health insurer data, enabling payers to run analytics on all of their data in one place.

Real Estate Investment Company

We worked with a real estate investment company to discover and build an internal product that puts all their data layers in one app with a snappy interface, allowing them to make better capital outlay decisions.

“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
“We trust Manifold and their experience not only to deliver on promised work but also to provide solid guidance on technology, product, and implementation process.”
Chief Technology Officer,
High-Growth 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.

Our Insights

We bring a unique perspective and expertise to our work. In this collection of essays, papers, and conference talks, we lay out our core philosophies and approaches. Explore the fundamental DNA of what makes us Manifold, learn about some of the tools we use every day, and understand how we build successful data products. Some of our most popular pieces include: