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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
Plug & Play Machine Learning - No Data Scientists Needed
Recommendation Engines - Deployable in Weeks
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.
Instantaneously Absorb Human Expertise Then Automate It
Ultra-Intelligent Automation
Our Artificial Intelligence instantly absorbs human expertise then automates it.
- Recruiters apply ideal resumes to reqs. The AI finds and sends them candidates.
- Marketers train the AI on ideal customer profiles. The AI then automates Next Best Action experiences.
- Prior art routed to attorneys
- Tax laws routed to CPAs
- Etc.
Find Higher-Quality Results Faster and Accelerate Discovery
A Better Way to Search
Keyword and Boolean search is inefficient and ineffective. We invented a better way to find things.
- Train a personalized search session with examples of what you're looking for
- Select the search results you like and tell your ML engine to "Go find me more results like these"
Save time and money.
Accelerate discovery.
This Might Get a Little Geeky
The ImplicitSearch™ AI Bloodhound
Our breakthrough, patent-pending machine learning technology enables powerful-yet-intuitive approaches for finding, scoring, classifying, and recommending structured and unstructured data.
Some Conceptual Solution Ideas We're Able to Enable Quickly
Automate Compliance
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...
- Allow CPAs to detect Tax rule change "signal" from the "noise" from across 10,000 sites
- Help automate the assignment of the appropriate tax category to an online transaction
- Help Tax & Audit firms streamline their Indirect Tax audits
- Help States' Franchise Tax Boards / Departments of Revenue better understand owed revenues
Perform Sentiment Analyses - Without Sentiment Tools!
Our ML tech is instantly trained by example data to "recognize" and recommend similar data. Check out this amazing use case for sentiment detection:
- Select communications that experts determined were "Happy"
- Create a [Human+ML] Session and call it "Happy Messages"
- Take the communications that the experts said were "Happy" and apply them to the session (literally drag & drop)
- The ML part of the [Human+ML] session was just INSTANTLY TRAINED to understand what "Happy" communications look like
- Take that trained engine and have it analyze ALL customer interactions
- Customer interactions that are "Happy" will be given a high score by the "Happy Messages" engine
- Use that score to generate a weekly report of "Happy Messages"
- Or use that score to recommend IN REAL TIME the Best Next Action to take when you're on the phone / on a chat conversation with a "Happy" customer
- Now - follow the EXACT same process with "Angry" or "Sarcastic" or "At-risk Customer" messages - and deploy the engines to generate reports and optimized customer handling scripts.
Do you see how this on-the-fly [Human+ML] essentially obviates the need for AI Sentiment analytics toolkits?
Early Detection of At-Risk Adolescents or Students (Mental Health, Academic Performance, Suicide)
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:
- Create a [Human+ML] session and give it a name like "Teenage Suicide Risk"
- Experts find the profiles of adolescents who were at risk of or attempted suicide
- The ML engine is trained by those profiles and will "recognize" them
- Deploy that ML engine to analyze ALL student profiles
- When that engine gives a high AI score to a profile, that means that profile has strong implicit and contextual similarities to the profiles used to train the engine
- This means the AI just identified an adolescent posing a suicide risk
- Action is then taken to mitigate the risk (counselors, teachers, parents, etc.)
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.
Automate Loan Applications
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."
Product or Digital Product Recommendation Engine
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
HUGE Value Solutions for Attorneys and Law Firms
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:
- Prior Art Search: Instead of struggling with keywords, select a handful of seminal publications or pieces of prior art, and tell the On-The-Fly AI to "Go find more like these". Each attorney's personalized search experience gets better and better as they "Like" results they find relevant
- Electronic Discovery: More quickly navigate through data and content using a utility that allows your attorneys to "Seed" a search with an example of what it is they're looking for.
- Regulations Intelligence: Isolate meaningful regulation-related "signal" from the "noise" in a HUGELY personalized way. Attorney #1 might want to start a session and train it on Tax laws, so that it finds all relevant Tax law content. Attorney #2 might be interested in unemployment laws. Attorney #3 might be interested in healthcare or pharmaceutical regulations. Our AI is instantly trained by what the SPECIFIC USER deems relevant - and then goes and finds / routes content that is contextually and implicitly similar. There's not ONE ML model - there's as many as there needs to be.
- Case Law Research: Find case law more effectively because you can "check the box" next to a relevant case and tell your personalized, On-the-Fly ML engine to "Go find more like this". And it's not just at the overall level. What if you could "clusterize" Cases that have similar Procedural Postures, or Overviews, or Outcomes, or Juror Profiles? Current Case Law research state of the art involves a LOT of keywords and faceted search and clicking. But attorneys are experts. And our ML instantly absorbs their expertise. When an attorney chooses a relevant case, our ML will be instantly trained by it to start recommending other cases like it. Because we already include keywords and Booleans out of the box, we are already at parity with them. But when you add that ML "Check the box and show me more" capability - that's when things get VERY interesting.
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?
Better Search For - and Find - Stuff in General
If you've made it this far, you now know the following:
- Users can start a search with examples of what they hope to find more of ... kind of like image search on the big search engines except it's with data & documents instead
- Our search results have checkboxes (Read that in a "These go to eleven" voice)