AI & Graph Analytics
Charge up your power to predict, detect, and automate your enterprise.
How We Can Help
A few years ago, AI was viewed as a nice-to-have and probably-out-of-reach, not-for-us kind of thing. And then ChatGPT hit the market. Awareness of AI’s benefits skyrocketed, and the race was on to realize the benefits.
In addition to generative AI such as ChatGPT, there are many other forms of AI you should consider. Machine Learning is crucial for predicting outcomes and behaviors of customers, allowing you to optimize marketing, customer service and policies. Machine Learning can play an important role in automation, especially in combination with tech like Robotic Process Automation (RPA).
There are some solutions that benefit from a different way of organizing data. Graph Analytics structures data as nodes and relationships instead of the traditional tables and columns. Graph Analytics can be used to solve complex problems like route optimization, preventing supply chain disruption, and sales recommendations. When Machine Learning is layered on top of Graph Analytics, you can predict complex patterns of behavior such as fraud or criminal intent.
You don’t need to bring expertise in the technologies or methods. Tell us what you’d like to achieve, and we’ll show you how it can be done with AI.
When To Call Us
Here are some common reasons clients call on us.
- Desire to validate potential business cases for AI.
- Objective is to pressure test an AI concept.
- Want to optimize your business by predicting certain outcomes.
- Traditional reporting cannot solve your analytical problem.
- Need to automate a business process.
- Redesigning business process to leverage data and insights.
Key Activities
Don’t see what you’re expecting? Let’s talk about how to tailor to your needs.
TECHNOLOGY
- Assess potential data sources.
- Perform feature engineering.
- Train machine learning model (POC and production)
- Integrate ML model into operational systems.
- Implement drift monitoring.
- Deploy model into production.
PROCESS
- Establish model deployment and monitoring procedures.
- Support process change initiatives.
- Implement AI ethics steering.
- Define procedures to monitor ongoing model viability.
ORGANIZATION
- Develop the AI business strategy.
- Review and assess potential business cases for AI.
- Support adoption of AI solution.
- Advise executives on funding model for AI.
- Develop AI roadmap and budget.
