Industry News
Industry News
May 20, 2024

The need for AI speed.

The need for AI speed.

One of the challenges with building AI enhancements into existing business processes, tasks, or critical functions is the speed of the AI models. For instance, if support, sales, or operations personnel are working through an existing process, the delay that a custom AI assistant or a custom-trained LLM introduces into the process may undermine its value. We’ve all experienced periods where AI systems just seem to sit and ponder their response, only to eventually return the information slowly (and sometimes iteratively) or never at all. 

This is why OpenAI’s recent announcement about increasing the speed of the models (as well as memory and costs) will be greeted warmly by enterprises seeking to move beyond the AI hype and into true AI production systems.

Gartner predicts that, by 2026, 80% of enterprises will have deployed generative AI, LLM or machine learning solutions into their production systems, up from just 5% in 2023.

The speed of the models is one (though just one) of the challenges holding back adoption. And even with speed enhancements many implementations of AI are not live ‘conversations’, but rather integrated steps in an existing process. This is one of the reasons many AI-enabled solutions are asynchronous today. 

Contextual makes it simple to add AI-enriched data transformation, categorization, prioritization, machine-learning evaluation, and more into existing business functions and tasks. Contextual delivers a complete solution— including data coordination and persistence, multi-model coordination, low-code business logic, and simplified integrations across multiple AI models.

The arms race from companies like OpenAI and Anthropic for model speed, power, and efficiency will not slow down any time soon, and as a result, enterprises seeking to realize the benefits of AI solutions will require the flexibility to choose between the models, test their performance in production-level A/B workflows that route to distinct models, and to annotate the behavior and success of a given model over another in order to continuously improve prompting and result formatting. 

This need will only increase as function-specific AI solutions like those available at RapidAPI continue dominating the task-specific action market. These AI-enriched APIs are not complete solutions but rather explicitly formed genAI and LLM solutions designed to execute a narrowly defined task like data categorization. Contextual solves a critical need by organizing all of these into a complete solution.

Contextual makes it simple to design, develop, and deploy AI-enhanced business solutions that seamlessly evolve alongside your business—in weeks, not months. Contact us today to get started.