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CX platforms process billions of unstructured interactions a year: Survey forms, review sites, social feeds, call center transcripts, all flowing into AI engines that trigger automated workflows touching payroll, CRM, and payment systems. No tool in a security operation center leader’s stack inspects what a CX platform’s AI engine is ingesting, and attackers figured this …

How attackers hit 700 organizations through CX platforms your SOC already approved Read More »

Late last year, Google briefly took the crown for most powerful AI model in the world with the launch of Gemini 3 Pro — only to be surpassed within weeks by OpenAI and Anthropic releasing new models, s is common in the fiercely competitive AI race. Now Google is back to retake the throne with …

Google launches Gemini 3.1 Pro, retaking AI crown with 2X+ reasoning performance boost Read More »

Despite growing chatter about a future when much human work is automated by AI, one of the ironies of this current tech boom is how stubbornly reliant on human beings it remains, specifically the process of training AI models using reinforcement learning from human feedback (RLHF). At its simplest, RLHF is a tutoring system: after …

Rapidata emerges to shorten AI model development cycles from months to days with near real-time RLHF Read More »

Traditional ETL tools like dbt or Fivetran prepare data for reporting: structured analytics and dashboards with stable schemas. AI applications need something different: preparing messy, evolving operational data for model inference in real-time. Empromptu calls this distinction “inference integrity” versus “reporting integrity.” Instead of treating data preparation as a separate discipline, golden pipelines integrate normalization …

The ‘last-mile’ data problem is stalling enterprise agentic AI — ‘golden pipelines’ aim to fix it Read More »

Agents built on top of today’s models often break with simple changes — a new library, a workflow modification — and require a human engineer to fix it. That’s one of the most persistent challenges in deploying AI for the enterprise: creating agents that can adapt to dynamic environments without constant hand-holding. While today’s models …

New agent framework matches human-engineered AI systems — and adds zero inference cost to deploy Read More »

Alibaba dropped Qwen3.5 earlier this week, timed to coincide with the Lunar New Year, and the headline numbers alone are enough to make enterprise AI buyers stop and pay attention. The new flagship open-weight model — Qwen3.5-397B-A17B — packs 397 billion total parameters but activates only 17 billion per token. It is claiming benchmark wins …

Alibaba’s Qwen 3.5 397B-A17 beats its larger trillion-parameter model — at a fraction of the cost Read More »

Typically, when building, training and deploying AI, enterprises prioritize accuracy. And that, no doubt, is important; but in highly complex, nuanced industries like law, accuracy alone isn’t enough. Higher stakes mean higher standards: Models outputs must be assessed for relevancy, authority, citation accuracy and hallucination rates.  To tackle this immense task, LexisNexis has evolved beyond standard …

When accurate AI is still dangerously incomplete Read More »

The chatbot era may have just received its obituary. Peter Steinberger, the creator of OpenClaw — the open-source AI agent that took the developer world by storm over the past month, raising concerns among enterprise security teams — announced over the weekend that he is joining OpenAI to “work on bringing agents to everyone.” The …

OpenAI’s acquisition of OpenClaw signals the beginning of the end of the ChatGPT era Read More »

Anthropic on Tuesday released Claude Sonnet 4.6, a model that amounts to a seismic repricing event for the AI industry. It delivers near-flagship intelligence at mid-tier cost, and it lands squarely in the middle of an unprecedented corporate rush to deploy AI agents and automated coding tools. The model is a full upgrade across coding, …

Anthropic’s Sonnet 4.6 matches flagship AI performance at one-fifth the cost, accelerating enterprise adoption Read More »

Building retrieval-augmented generation (RAG) systems for AI agents often involves using multiple layers and technologies for structured data, vectors and graph information. In recent months it has also become increasingly clear that agentic AI systems need memory, sometimes referred to as contextual memory, to operate effectively. The complexity and synchronization of having different data layers …

SurrealDB 3.0 wants to replace your five-database RAG stack with one Read More »