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Modern enterprises increasingly seek advanced computing, to augment the creativity and agency of their people, to tame the exploding complexity of the Digital Age.

Their people often face computationally intractable problems in areas as diverse as pricing, logistics optimization, recommendation engines, human resources and more. One cannot just throw more compute power or new computing paradigms as black-box services, at those challenges, and hope to surmount them.

Oneirix Labs offers advanced computing expertise to help you fully realize the business potential of artificial intelligence, machine learning, data science, and combinatorial optimization.

We help find tractable solutions, suited to the unique environment, constraints, and degrees of freedom of your changing enterprise.

We have the know-how and experience of architecting and engineering solutions to run on disparate infrastructure, including cloud services, compute clusters, custom multiprocessor systems, and supercomputers.

And we have created advanced algorithms and versatile software frameworks that let us solve sophisticated problems rapidly, correctly, and scalably.

EXAMPLE PROJECTS

Pricing

We create pricing models for companies using a judicious blend of techniques from economics, finance, management theory, algorithmic computing, A.I. and data science. Our pricing algorithms not only suggest prices, but also explain their suggestions in human-relatable terms: prediction of market demand, reasons for the predictions, reasons for pricing based on such predictions, and so forth.

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Logistics Optimization

Logistics problems tend to become extraordinarily difficult to optimize. For example, how can we package and transport millions of items, using thousands of vehicles, while managing complex schedules with varying delivery guarantees, and not lose money?

Logistics planning technology created by us excels at this task.

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Psychometric Testing

Oneirix Labs applies technology based on “item response theory”, to improve grading accuracy, and reduce bad bias in psychometric tests administered by Human Resources departments.

To this end, we have devised a graph-based bootstrapping algorithm to transform bare data about questions and answers into information about question difficulty, as well as person ability. Our algorithm achieves this with zero prior knowledge of difficulty or ability.

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Recommendation Engines

We have written multiple recommendation engines. Our engines maximize utility of finite attention spans of customers, by accurate, fast suggestions of related purchases. The engines also help educate customer about new products, based on subtle customer need discovery; a hallmark of good recommendation engines.

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