Dataiku vs Rankscale.ai

Discover Dataiku Agent Management: every agent on the record

Dataiku announced the launch of Dataiku Agent Management, a new product feature for managing AI agents, along with the announcement of the AI Success Frontrunner award winners.

12,000 Attend Ai4 2026 to Discuss AI's Invisibility

The article reports on the Ai4 2026 conference in Las Vegas, which saw record attendance of over 12,000, up from 8,000 the previous year. Dataiku's keynote highlighted that 96% of enterprise leaders believe employees are using unsanctioned generative AI tools, and 80% of CIOs see their jobs at risk without measurable AI ROI. Pat Gelsinger argued that AI economics must improve dramatically, while Geoffrey Hinton, Fei-Fei Li, and Andrew Ng jointly endorsed AI regulation. The conference emphasized the need for AI governance, budget ownership, and cross-functional staffing. Speakers from Cisco, Nvidia, Uber, PayPal, and other companies discussed the shift from assistants to autonomous agents and the importance of accountable AI deployment.

• Ai4 2026 attendance exceeded 12,000, up from 8,000 the previous year.
• Dataiku's survey found 96% of enterprise leaders believe employees use unsanctioned generative AI tools.
• 80% of CIOs say their job is at risk without measurable AI ROI.

Governed RAG: Data, Context & Lineage for Enterprise AI

The article describes risks introduced by Retrieval-Augmented Generation (RAG) when enterprise data is exposed to vector search pipelines and proposes a three-part Governed RAG architecture: (1) ingestion with cryptographic embedding lineage and metadata, (2) query-time contextual Attribute-Based Access Control (ABAC) embedded into vector search queries, and (3) outbound payload sanitization (PII/PHI masking, indirect injection removal, and context length minimization). It argues that enterprises must enforce retrieval-time access controls, maintain graph-based data lineage, and implement real-time index freshness/eviction to prevent privilege escalation, prompt-injection attacks, stale-context hallucinations, and to meet compliance requirements.

• Retrieval-Augmented Generation (RAG) pairs LLMs with vector databases and knowledge graphs to ground agents in proprietary corporate knowledge.
• Vector stores typically do not preserve fine-grained document-level ACLs or cryptographic data lineage by default, creating over-permissioned retrieval risks.
• The proposed Governed RAG pipeline has three security boundaries: ingestion with cryptographic embedding lineage, query-time contextual ABAC inside the vector search, and outbound payload sanitization.

Appearing in ChatGPT Gives Brands an Advantage — Rankscale

Mathias Ptacek, founder and CEO of Rankscale.ai, describes his startup’s work measuring brand and content visibility inside AI search systems and chat assistants. Rankscale statistically analyzes large sets of prompts sent to systems such as ChatGPT, Copilot, Gemini, Perplexity and Grok to determine which sources and entities are cited and where brands appear within model answers. The company is self-funded with strategic investors and business angels, runs a small team (~9 employees) with plans to grow, and offers features including Prompt-Research, Facts pages and a Visibility Score. Ptacek stresses model differences (e.g., Copilot leans on SEO tools, ChatGPT often cites Reddit or tech sites), recommends structured, authoritative content and offsite PR for AI visibility, and notes legal/regulatory questions about content use remain unresolved.

• Mathias Ptacek is founder and CEO of Rankscale.ai.
• Rankscale analyzes frequency and position of brands, products and content in answers from AI systems such as ChatGPT, Copilot, Gemini, Perplexity and Grok.
• Rankscale is self-funded (no VC), backed by strategic investors and business angels.