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Breakthrough Machine Learning AI that Recommends, Discovers, and Automates
WeR.ai's Patent-Pending ImplicitSearch™ and Unique Subscription Model Accelerates AI Launches and De-risks AI Investments
WeR.ai's Patent-Pending ImplicitSearch™ and Unique Subscription Model Accelerates AI Launches and De-risks AI Investments
WeR.ai does not sell tools or billable consultants.
What we do instead is enable the machine learning capabilities in your products or enterprise business processes.
We do it faster than "The Big Shops" or if you tried to do it yourself.
And we do it for a fraction of the cost via our unique, de-risked subscription model.
WeR.ai's Founder & CTO is one of the world's best recommendation engine scientists. He built the billion-dollar machine learning system that recommends the content on a major streaming service.
WeR.ai's ImplicitSearch™ plug & play system can enable recommendations in your products or within your business - in weeks.
Our Artificial Intelligence instantly absorbs human expertise then automates it.
Keyword and Boolean search is inefficient and ineffective. We invented a better way to find things.
Save time and money.
Accelerate discovery.
Our breakthrough, patent-pending machine learning technology enables powerful-yet-intuitive approaches for finding, scoring, classifying, and recommending structured and unstructured data.
In geek-speak, the technology is a vertical, personalized search engine that doesn’t require expensive knowledge graphs. The engines are instantiated limitlessly at the personalized [Human+ML] level, and are pristine when instantiated.
That's right ... there is no inherent ML bias.
This really is a breakthrough in ML/AI.
ImplicitSearch™ is a context-sensitive, human-in-the-loop search and recommendation engine that uses search criteria and implicit user feedback to recommend high-quality results.
In a matter of minutes.
We work with your business or product users to understand your use case and design a high-value MVP experience.
We work with your IT to securely integrate our ML system with your data sources - on premise or in your preferred cloud.
We tune the ML to your data, schema, and needs.
We then deploy and support as a service.
Attorneys, compliance leaders, CPAs and other experts continually monitor the regulatory landscape to understand changes (or anticipate what will be changed).
For example, in the United States there are over ten-thousand Tax jurisdictions. In our digitally transformed world with the ability to buy online 24/7, the behind-the-scenes step of applying the proper sales tax to a transaction actually involves a LOT of work! With our ML technology we can...
Our ML tech is instantly trained by example data to "recognize" and recommend similar data. Check out this amazing use case for sentiment detection:
Do you see how this on-the-fly [Human+ML] essentially obviates the need for AI Sentiment analytics toolkits?
This is a powerful example of a highly repeatable use case.
Our staffing solution allows a recruiter to create a req, give it examples of resumes they hope to find from candidates, and then save it. That req becomes its own AI search session and finds candidates "like" those whose resumes were used to train it.
We can apply the EXACT SAME design pattern to help identify children / students posing academic performance risk, mental health risk, or suicide risk. The Staffing Solution featured on our Client Cases page lays it out, but it's worth repeating given how important this is:
Because we can limitlessly create [Human+ML] sessions, users could limitlessly create persona profiles and train the ML on examples of those personas.
There's no inherent ML bias either. The ML is INSTANTLY trained by what matters to the expert.
Perhaps the profile of a suicide risk teenager in Southern California is different from the profile of at-risk teens in Detroit.
Not a problem. The experts in Southern California apply their expertise to feed the ML with examples of their target persona. And the experts in Detroit do the same thing.
Because it's NOT ONE ML system but instead an unlimited number of them - this enables hugely flexible, and potentially life-saving early detection capabilities that we would argue should become a standard in all school districts.
A mortgage expert can create a new [Human+ML] session and give it a name like "Low-risk Applications to Automate". They can then feed the ML engine examples of applications that turned out to be low risk.
Once that engine is trained (again, as mentioned above, literally in minutes) that engine can be deployed to analyze ALL applications - including those coming into the bank or credit union in real time.
When an application gets scored highly by that engine, that means it had implicit and contextual similarities to the low-risk applications that were used to train it.
Because we employ INDIVIDUAL engines - and those engines are unique identified - you can set up a rule that says:
IF "Low-Risk" engine score of an application is equal to or greater than X, THEN route the application to "Automated Approval Process 123".
IF "Low-Risk" engine score of an application is less than X, THEN route the application to "Manual Review Process 456."
We would be remiss to not mention that we can enable a product recommendation engine capability for your company in a matter of weeks and keep it running as a subscription service - for about 10% of the price of you hiring expensive data scientists and trying to to it yourself, or hiring "The Big Firms" to come in and do it via a consulting engagement.
Plus, our SLAs give you comfort knowing that as your products change, and your data changes, and promotions and optimization strategies come and go - we keep your system running as it needs to.
Sounds like a no-brainer, doesn't it? #AIDadJoke
There's a HUGE opportunity in the legal space for our On-the-Fly, No-Knowledge-Graph Machine Learning Recommendation and Search technology - further amplified by our white-label AIaaS model.
Here are a few ideas based on discussions with several attorneys:
WeR.ai is all about white-labeling and "powering" others' solutions.
If you're a Law Firm, or a Technology Firm serving Attorneys and practices, then what do you think about deploying these solutions above, via a white-label model with industry-leading SLAs on the ML, and bringing them to market in a few months or less?
If you've made it this far, you now know the following:
WeR High-Performance Data Science as a Service