{"id":12460,"date":"2026-04-22T14:21:14","date_gmt":"2026-04-22T14:21:14","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/12460\/"},"modified":"2026-04-22T14:21:14","modified_gmt":"2026-04-22T14:21:14","slug":"tredence-powers-up-gemini-powered-agentic-ai-accelerators","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/12460\/","title":{"rendered":"Tredence powers up Gemini-powered agentic AI accelerators"},"content":{"rendered":"

Data science platform Tredence<\/a> has detailed its suite of agentic AI accelerators<\/a>, developed in close collaboration with Google Cloud. But wait, agentic functions already accelerate, so what is an agentic AI accelerator? These are ready-to-deploy, pre-built industry-specific AI solutions designed to bypass lengthy development cycles. So can the company justify accelerating an accelerator in this sense?<\/p>\n

Targeting rapid deployment in what the company defines as proven use cases that integrate with existing data and workflows, Tredence says its suite includes software services that speed up data modernisation. How does that work? By simplifying migration from legacy systems through a process of unifying fragmented data into a governed, AI-ready data foundation.<\/p>\n

Application in functional business zones<\/p>\n

Built on that data foundation, purpose-built AI agents work alongside people across the functional areas that organisations rely on most, helping teams sense, decide, and act in real time.<\/p>\n

While there is no defined set of \u201cmost functional business areas\u201d for any given company, we can of course infer finance, core operations management, logistics and sales as key areas that might fall into this classification. Tredence actually uses a broader brush definition and says that in supply chain, this means replacing siloed tools with a \u201csingle AI-driven decision layer\u201d that drives real-time visibility, coordination and faster response across all operations.<\/p>\n

AI agents as digital co-workers<\/p>\n

For customer-facing functions, enterprises can anticipate needs and personalise interactions at scale. At the core, a unified intelligence layer brings these capabilities together, where AI agents operate as digital co-workers, collaborating, reasoning, and executing decisions to drive faster, scalable enterprise outcomes.<\/p>\n

Sumit Mehra, co-founder and chief technology officer at Tredence says that the goal today is all about how quickly enterprises can operationalise agentic AI.<\/p>\n

\u201cWith Google Cloud, we\u2019re bringing together the power of Gemini Enterprise with solutions we\u2019ve already tested in real-world environments. At Tredence, we don\u2019t just build with Gemini Enterprise, we run on it. It is our trusted LLM across functions and teams, deployed at scale across our own organisation. That firsthand experience is what allows our customers to move beyond pilots and actually scale AI by embedding it into day-to-day decisions and delivering outcomes they can measure,\u201d said Mehra.<\/p>\n

For completeness, Gemini Enterprise is categorised as Google\u2019s premium AI platform for commercial business use with access to more advanced models, full-speed connectivity access to the service itself, enterprise-grade security and a more sophisticated set of software application development tools for building agents that will reside in complex (previously human-only and some newly machine-generated) professional workflows and data analysis jobs.<\/p>\n

How to build a data & AI platform<\/p>\n

Tredence deployed Gemini Enterprise, Vertex AI (Google\u2019s platform for building, deploying and scaling machine learning and generative models) and BigQuery (Google Cloud\u2019s managed, serverless data warehouse for high-speed analysis of petabyte-scale datasets using standard SQL queries and built-in machine learning capabilities) as the foundation of a complete data and AI platform modernisation.<\/p>\n

This new data and AI platform effectively replaced what the company classifies as a fragmented legacy environment, with a unified, intelligent platform that powers hyper-personalised customer experiences, AI-driven product innovation, and smarter decision-making across the workforce.<\/p>\n

Building on this foundation, Tredence accelerated enterprise AI adoption by deploying advanced AI and multi-agent systems on Vertex AI, Gemini Enterprise and Gemini Enterprise for Customer Experience, automating up to 98% of manual processes, reducing operational effort by up to 70%, and compressing deployment timelines from months to weeks.<\/p>\n

\u201cThe impact has been global and cross-functional, from unifying supply chain intelligence across thousands of stores to rapidly launching full-scale agentic platforms, this demonstrates how the Tredence\u2013Google Cloud partnership translates AI ambition into real, scalable business outcomes\u201d, said Rakesh Sancheti, chief growth officer at Tredence.<\/p>\n

Tredence to the test<\/p>\n

We can put Tredence up against a number of different competitors i.e. there\u2019s a mix of pure-play analytics firms and larger AI technology specialists (by which we don\u2019t just mean software vendors, but specialist software consultancies also) that work at this level, including Fractal Analytics, Tiger Analytics, Mu Sigma, Infosys and Razorthink for data engineering and AI overlap. While it\u2019s hard to rank one above another definitively \u2013 primarily because they do operate in this overlapping space of competencies \u2013 it;s hard not to think of the younger operators in this space as better-aligned to the age of agentic functions.<\/p>\n

This new suite of Tredence Agentic AI accelerators aims to directly address the \u201cLast Mile\u201d challenge faced by companies, by combining Tredence\u2019s deep industry expertise with Google Cloud\u2019s full enterprise AI stack- including Gemini Enterprise, Gemini for Customer Experience, Vertex AI and BigQuery.<\/p>\n","protected":false},"excerpt":{"rendered":"Data science platform Tredence has detailed its suite of agentic AI accelerators, developed in close collaboration with Google…\n","protected":false},"author":2,"featured_media":12461,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[179,7493,24,201,223,132,314],"class_list":{"0":"post-12460","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-agentic-ai","8":"tag-agentic-ai","9":"tag-agentic-artificial-intelligence","10":"tag-ai","11":"tag-cloud","12":"tag-generative-ai","13":"tag-google","14":"tag-security"},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/12460","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=12460"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/12460\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/12461"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=12460"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=12460"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=12460"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}