Manifold AI

Manifold AI engineers custom AI and data infrastructure, algorithms, and machine learning solutions. Some of the world’s most ambitious global companies and high-growth startups have worked with us to solve cutting-edge technical problems.

“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
"The customer prediction tools that Manifold created have significantly impacted our business. We are able to make quick and aggressive decisions with confidence and have a focus on quality instead of just quantity."
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.

Computer Vision

We develop sophisticated computer vision models to solve problems involving techniques such as object recognition, segmentation, real-time gesture recognition, and 3D computer vision using LIDAR and imagery—and in some cases requiring novel methods for data augmentation.

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Natural Language Processing

We develop NLP pipelines to do entity resolution among multiple large datasets, create structured data from unstructured text, and optimize search/information retrieval using modern embedding techniques.

Causal Inference

We develop structured causal models and observational data to answer causal questions—about both average treatment effects and heterogenous treatment effects. Though causal inference is still in its infancy, it has already been called one of the biggest advances in statistics in the last 50 years.

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