Our analytics experts have hands-on experience delivering a wide range of advanced analytics and AI use cases
We’ve captured their expertise into repeatable AI design patterns, which are aggregated into AI Solves™ at the use case level.
World-class data science – across the enterprise
The table below shows our current inventory of design patterns. The majority of AI use cases can be addressed by these design patterns, allowing us to create your Solves™ in a fraction of the time. If your use case requires a new design pattern, we estimate the maximum time to Solves™ delivery is three (3) months.
|Predictive Marketing (CMO)||Corporate Planning (CPO)||Supply Chain (COO)||Internet of Things (CIO)||Advanced Analytics (CTO)|
|Predictive CRM||Voice of Customer||Pricing||Intelligent Building||Item Recommendation|
|Growth Marketing||Revenue Forecasting||Unified KPIs||Edge Analytics||Forensic Text Analytics|
|Cohort / Campaign Optimization||Predictive Productivity||Customer Retention / Life-Time-Value||Real-Time Fraud Detection||Intelligent Agent (Chatbots, NLP)|
|Algorithmic Attribution||Opinion Mining||Intelligent Negotiation||Predictive Maintenance||Pattern Recognition|
|Customer Segmentation / AB Testing||Talent Matching||Logistics Science||Network Analytics||Semantic Search|
|Mobile Ads||Risk Profiling||Inventory Optimization||Streaming Prediction & Forecast||Intelligent Cloud Management|
Machines . . . Learning
Once we create the Solves™ for your use case, we support it by keeping it running . . . and learning.
Backend Operations & Learning Services
Fine tuning of model(s)
Modifications to WeR developed code for upgrades / maintenance in underlying applications (i.e. operating system releases)
Minor changes resulting from changes in data sources or output APIs
Monitoring metrics to ensure ML system is running correctly
Diagnosis of system or model performance issues
System maintenance / upgrades to ensure maximum performance
Analytics and AI data science model diagnostics
Bug-fixes at the Solve level
ETL diagnostics and fixes for data issues
Man Chan, Founder and CTO, WeR.ai
“In general, in machine learning there is no one-size-fits-all model. And in real practice, we will not allow a machine learning model to auto-drive itself for businesses without human monitoring. You must have a team of scientists and engineers at the ready to make sure the system is working within its operational parameters. WeR.ai expertise applied through the AIM is able to greatly minimize – but not eliminate – the human elements of it. WeR can automate as much as 95% of operations, and leave only 5% for the engineers. Only in some special cases can we make a self-autonomous ML system – which we have done in the past.
Once you engage engineers to work on your AI needs, you need to keep them close by as long as you have the system running. The same is true for WeR.ai – but we can do it better and at a far lower cost.”