Machine learning models return biased results when the datasets used to train them contain bias. Instances of social bias, skewed model results, and outputs that don't represent the full scope of a business problem for a specific domain are some of the caveats when employing this technology.
Jelani Harper //
16 Jul 2025
AI stands beyond experimental status—it is a founding power for corporate productivity delivery. From automating repetitive workflows to enhancing complex decision making, AI transforms how work gets done.
Srinivas Sandiri //
06 Aug 2025
Synthetic data applications are at the intersection of some of the most meaningful developments in enterprise AI. Synthetic data techniques represent some of the earliest manifestations of generative AI and predate the widespread adoption of language models. In fact, employing synthetic data is one of the foremost methods of building—and fine-tuning—language models and foundation models in general.
Jelani Harper //
22 Apr 2025
Although the initial seeds of generative AI (GenAI) were planted in the 1960s, for the most part it didn't come into full bloom until the introduction of ChatGPT in November 2022. Before that, GenAI had all but the most nascent use in business. As the technology entered the public consciousness, it spread across industries from entertainment to manufacturing to healthcare for a wide variety of uses, including customer service.
Phillip Britt //
28 Apr 2025