Intelligent CIO North America Issue 55 | Page 54

FEATURE : DIGITAL TRANSFORMATION

AI IS BECOMING THE NEW SOFTWARE , BUT ITS WIDESPREAD ADOPTION FACES A CRITICAL CHALLENGE – RELIABILITY AND ACCURACY AT SCALE .

data-driven remediation strategies , and ultimately treat AI development with the same rigor as modern software engineering .
Building trustworthy high-performing AI applications is complex – requiring rapid iterations across models , prompts , and data while safeguarding against harmful outputs . Future AGI ’ s platform streamlines this entire lifecycle with rapid experimentation , deep multi-modal evaluations , real-time observability , and continuous improvement capabilities . The platform ’ s proprietary technology includes advanced evaluation systems for text and images , agent optimizers , and auto-annotation tools that can reduce AI product development time by up to 95 %. Users can complete evaluations in minutes and automatically optimize their AI systems for production , eliminating manual overhead and ensuring consistent performance .
“ AI is becoming the new software , but its widespread adoption faces a critical challenge – reliability and accuracy at scale ,” said Nikhil Pareek , CEO , Future AGI .
“ Today ’ s AI systems are probabilistic and error-prone , with improvement cycles taking 6 – 8 months . We ’ re building the foundational layer that ensures AI systems are trustworthy and reliable in production . Our platform isn ’ t just about workflow automation – we ’ re creating the data layer that continuously monitors , evaluates and improves AI systems across multimodal interactions .”
FutureAGI is making significant strides across various industries . A Series E sales-tech company leveraged FutureAGI ’ s LLM Experimentation Hub to achieve an impressive 99 % accuracy in agentic pipeline , accelerating their processes 10 times faster than previous methods , compressing weeks of work into just hours . This transformation has drastically improved their capacity for delivering personalized customer interactions at scale .
In another case , an AI image generation company utilized FutureAGI ’ s platform to streamline its image generation pipeline , resulting in a remarkable 90 % reduction in costs by decreasing reliance on human evaluators while maintaining 99 % accuracy for catalog and marketing images .
These examples highlight FutureAGI ’ s ability to optimize operations and drive substantial cost savings while enhancing performance .
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