Close Cookie Preference Manager
Cookie Settings
By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage and assist in our marketing efforts. More info
Strictly Necessary (Always Active)
Cookies required to enable basic website functionality.
Made by Flinch 77
Oops! Something went wrong while submitting the form.

Deploy AI and ML

“For us, AI is a foundational technology that in the next couple of years will be found in the vast majority of our propositions"

Jeroen Tas Chief Innovation and Strategy Officer, Philips

Software and Artificial Intelligence (AI) are now in most systems we interact with including aircrafts, satellites, cars, watches, and even doorbells. Companies that use AI in the design and operation of their products have reaped huge rewards. Take Tesla, for example, they have been able to reduce the energy consumption of their vehicles by nearly 30% using AI and real-world data. This has resulted in smaller batteries for their newer vehicles, lowering their costs and allowing them to launch a mass market vehicle like the Model 3 that is affordable to most US consumers. This has propelled them to be one of the most admired companies in the automotive industry - having roughly six times the combined market value of GM and Ford. 

Relatively cheap capital, lower computing costs, and higher caliber tech talent have all lowered the barriers to entry in system design. Thus the requirements to stay competitive in the global market have never been higher. It is almost imperative for technology companies to at the very least think about how AI can help them design the next generation of their systems faster and with less risk.

96%

of business executives plan to use AI simulations in 2022 - PWC

Why is AI important in system design?

Design projects generally start with requirements, but when faced with competing desired outcomes, requirements can get complicated quickly. For example, when designing a car, engineers will try to optimize performance, safety, cost, and efficiency. In such situations, AI can help engineers:

  • Train a system to solve a problem without explicitly programming the rules. This is especially useful for systems that do not have deterministic inputs or outputs. For instance, there is no numerical model that can accurately predict how much mileage a battery cell will give a car. In this case, AI can help engineers get to an accurate estimate without having to program all the variables that could affect battery life, saving time, money and de-risking the development quite substantially
  • Rapidly iterate through millions of potential designs and variables, help them discard the ones that are non-starters, and allow them to spend their time on the ones that show the most promise. This is especially helpful in systems design where it can be difficult, or sometimes impossible, to predict outcomes without performing experiments

A shift is underway — are your engineering tools helping or hindering?

Collimator is the only tool that allows you to import big data directly via API, design your system using neural networks from Tensorflow or Pytorch, and simulate millions of test cases using HPC in the Cloud so perception and control engineers can work off of a single source of truth - finally!

Traditional Applications

Cannot quickly ingest or export the amounts of data required
Difficult to use open source libraries because they run proprietary languages like Matlab
Complicated to use neural nets trained in libraries like Tensorflow and Pytorch
Involves extra time, effort and money to run HPC simulations
Difficult to collaborate without being in the same room
Traditional Applications UI
Collimator UI
Collimator Logo
Seamlessly ingest or export data by directly connecting to your database via API
Easily access Python libraries or call your own code directly within Collimator
Effortlessly import any neural net to deploy in your system
Quickly test or simulate performance over millions of runs
Efficiently collaborate with one source of truth and role based access control

Traditional Applications

Traditional Applications
Cannot quickly ingest or export the amounts of data required
Difficult to use open source libraries because they run proprietary languages like Matlab
Complicated to use neural nets trained in libraries like Tensorflow and Pytorch
Involves extra time, effort and money to run HPC simulations
Difficult to collaborate without being in the same room
Collimator Logo
Collimator UI
Seamlessly ingest or export data by directly connecting to your database via API
Easily access Python libraries or call your own code directly within Collimator
Effortlessly import any neural net to deploy in your system
Quickly test or simulate performance over millions of runs
Efficiently collaborate with one source of truth and role based access control

See Collimator in action

What our customers are saying

See Live Demo