Product
Let Octai power your Revenue
Operations
Revenue
Product
Customers
Insights
Marketing
Efficiency
Revenue
Product
Customers
Insights
Marketing
Efficiency
Bypass the competition. With Octai you can pull ahead by rapidly building machine learning into every corner of your product and operations.
How it works
Turn your data into predictive models
Data in, Future out. Start with your data, train a custom machine learning model, and use it to make smarter real-time decisions.
Making ML Simple
Enhance your capabilities
Solve problems faster, more reliably and with higher accuracy
Move Faster
Build, validate, and deploy models in minutes instead of weeks.
Drive Impact
Find the most optimal models to achieve top-tier performance.
Reduce Risk
We worry about reliability so you and your team don’t have to.
Connect to your data
Integrate
Octai integrates a myriad of third-party data sources, so you can access all the data you need in one place.
Prepare and Store Data
Feature
Octai facilitates the discoverability, reuse and accuracy of features for ML and delivers more accurate data for models at any stage of the pipeline at any given time.
Register & Train Models
Model
Octai Model standardizes your ML project structure and automatically generates versions for data, code, and parameters on every model build. Deliver immutable and tested production grade artifacts continuously.
Observe & Forecast
Deploy
Deploy models to production with one click. Deploy and monitor models into a production environment to create real-world, data-driven solutions. Generate ongoing forecasts in minutes. Ask your current and historical forecasts and easily share with your team.
Statistics
Why data & AI?
Organizations recognize that data & AI are catalysts for enterprise reinvention, but also know that there’s work ahead to fully capitalize on data & AI’s potential.
0
%
of executives believe generative AI will be transformative to their company.
0
%
of organizations plan to increase their level of spending in technology and are prioritizing investments in data & AI.
0
%
of business leaders see data readiness as the top challenge to adopt AI.
0
%
of executives support a certain level of government regulation for AI.
0
%
of organizations are increasing their investment as a % of revenue, which was previously 89%.
0
%
of organizations have specific training programs planned for this year to ensure teams are prepared to adopt generative AI tools.
Resources
In the news
Keep up to date with our lastest news, blog posts, webinars and events.
blog
Journeying from Winning Kaggle Competitions to Solving Real-World Problems with AI
In Herman Hesse’s The Glass Bead Game, the main hero seeks to become a Magister Ludi, or Master of the Game. A Master must be on the way toward perfection by learning a variety of ...
blog
Is AI in Manufacturing Delivering the Dreams of Smart Factories and Industry 4.0?
Back in 1998, Steve Jobs had just returned to Apple and was preparing to release the first iMac desktop computer in August of that year. The only problem? Many people hadn’t started using ...
blog
From Better Fan Experience to Injury Prevention—3 Ways AI is Revolutionizing Sports
If you go almost anywhere in the world—from the streets of Beşiktaş to Brooklyn—you’ll see kids sporting the jersey of No. 30 for the Golden State Warriors, Steph Curry. In fact, Curry ranks...
blog
What’s Behind the Data Scientist Shortage?
If you’ve checked your LinkedIn feed over the last few years, you’ve probably noticed the tech industry’s wild roller coaster ride. First, the pandemic led to a huge wave of tech hirings—promptly...
blog
Why Some AI Projects Fail?
While AI has been around for decades, it has only recently begun to revolutionize the business world. The technical potential is enormous: AI can be used to analyze massive amounts of data and make...
blog
How You Deal With Data Matters for Your Next AI Project?
As artificial intelligence becomes more prevalent in our lives, the importance of data management will only continue to grow. If you're planning on embarking on an AI project...
blog
Does Your Company Really Need an In-House ML Team?
In recent years, machine learning has become an increasingly important part of our lives. It is used in a variety of different ways, such as identifying fraudulent activities, improving search ...
