DataArt vs Dataiku

DataArt Introduces Domain Deployed Engineering™ for Industry-Specific, Outcome-Based AI Deployment

DataArt introduces Domain Deployed Engineering™, a new service offering for industry-specific, outcome-based AI deployment. The company also announced partnerships with OpenAI and Travelport, and a GDS modernization project with zero downtime.

DataArt launches Domain Deployed Engineering for outcome-based AI

DataArt, a global data and AI transformation partner, has introduced Domain Deployed Engineering (DDE), a new delivery model designed to help enterprises move AI from pilots to production. The model embeds small, industry-fluent teams within client organizations, with accountability for business results rather than deliverables. DDE squads combine agentic AI engineers, industry experts, and change management leads, and are scaled based on client maturity. The approach focuses on overcoming barriers to AI adoption, such as stalled pilots and vendors measured by inputs. Examples include a secure internal AI platform delivered to a global financial group's 73,000 employees in five months, and a compliance-ready AI platform for a contract research organization with 500% ROI within 30 days.

• DataArt introduces Domain Deployed Engineering (DDE) model.
• DDE embeds small, industry-fluent teams inside client organizations.
• Squads include agentic AI engineers, industry experts, and change management leads.

DataArt Named OpenAI Select Partner

DataArt, a global data and AI transformation partner, announced it has been named an OpenAI Select Partner within the OpenAI Partner Network. The designation positions DataArt to work with OpenAI to build, deploy, and scale AI solutions for enterprises, using OpenAI models (including GPT 5.6) and products such as ChatGPT Work. DataArt said it will expand OpenAI-powered offerings, invest in AI talent and delivery capabilities, and build on a stated $100 million commitment to advance data and AI. Yuri Gubin, CTO at DataArt, commented that the partnership will help organizations move beyond pilots by integrating governance, engineering discipline, and industry expertise into enterprise AI adoption.

• DataArt announced it has been named an OpenAI Select Partner within the OpenAI Partner Network.
• The OpenAI Partner Network is a global program for partners to build, sell, and deliver AI solutions with OpenAI.
• DataArt said it will help organizations build, deploy, and scale AI solutions and will expand OpenAI-powered offerings.

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.