Nuances of Build-or-Buy Decisions

The intricacies of the time-honored question of whether to build or buy take on new dimensions when it comes to enterprise AI. Organizations must not only account for traditional considerations like cost, time-to-market, and proof of concepts but also for those that are unique to advanced machine learning and language model deployments.

The Power of RAG

Think back a few years to OpenAI's initial introduction of its generative AI (GenAI) chatbot—the excitement, the promise of marvelous opportunities, the sense of a change for the better. It wasn't long before the euphoria of GenAI wore off and people started noticing that large language models (LLMs) could produce absolutely fabulous results, but they could also deliver flawed information that looked plausible but was untrue.

AI Techniques Powering Enterprise Productivity: From Automation to Augmented Intelligence

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.

The Rise of GenAI and LLMs

In 1950, Alan Turing suggested a test to determine if computers could mimic human intelligence well enough that an impartial observer could no longer tell the difference. We are still talking about the Turing Test almost 75 years after its inception.

Zuvu AI and Vana Collaborate on Decentralized AI in the Bittensor Ecosystem

Zuvu AI and Vana are announcing a strategic partnership to advance decentralized AI by integrating user-owned data, permissionless compute, and economic incentives.

SSI Strategy and Metalmind Collaborate on Life Sciences Enterprise AI Solutions

Through this partnership, SSI Strategy will leverage Metalmind's advanced capabilities in natural language processing (NLP) and large language models (LLMs) to enhance its service delivery.

Information Rich: Unifying Fragmented Data With Agentic Workflows in 2026

Building an AI-Ready Enterprise: Governance as the Foundation for Scalable AI

Solving the 95% Blind Spot

Solving the 95% Blind Spot: How to Find the Issues Your Contact Center Doesn't Know It Has Your Contact Center Is Flying Blind on 95% of Customer Data Most teams analyze only 5%—manual tags and sampled data. The rest stays hidden in transcripts, chats, and surveys. What This Costs You: Reacting weeks after problems start Missing emerging issues with outdated taxonomies Never catching the "unknown unknowns" that drain revenue Why AI deflection fails (and what works instead) The Avoidance Imperative—eliminate issues at the source How autonomous taxonomy delivers 100% coverage Get the Ebook

Spiral by UJET - The AI Issue Hub for Decision-Grade Data

What if anyone in your entire company—from an engineer to a marketing leader—could instantly ask a plain language question and get a definitive, quantified answer from 100% of your customer conversations? This datasheet shows you how. Learn how Spiral’s easy-to-use AI agent autonomously analyzes every call, chat, email, and review to find the ultra-specific root cause of every customer issue—transforming 100% of your customer conversations into actionable, predictive insights. Go from 'What if?' to 'Here's how.' Trusted by innovative teams at Turo, Owlet, and Whitepages