New Dreamdata AI delivers the trust factor as B2B marketers move decision-making into LLMs

Dreamdata AI learns from go-to-market data organized by accounts and tied to revenue

Dreamdata, the leading B2B attribution platform, introduced Dreamdata AI, three new AI offerings that learn from trustworthy go-to-market (GTM) data and are built on consistent definitions to remove conflicts in AI output. 

While many B2B marketing AI tools fail to deliver on the ‘trust’ promise, Dreamdata AI - comprising Dreamdata Analytics Agent, MCP Server, and Data Warehouse - is inherently different. It removes the black box feel of today’s AI agents and analytic tools, and shows what the AI answer was built on. Marketing teams can validate the numbers and act with confidence, whether in-app, through a Model Context Protocol (MCP) server or via a data warehouse.

Built on Dreamdata’s account-based data model, B2B marketers can interrogate the data across the entire buyer journey, which now spans 272 days, 88 touchpoints and 10 stakeholders on average, according to the 2026 LinkedIn Ads B2B Benchmarks Report. 

According to HubSpot’s The State of Marketing 2026, 61% of marketers say marketing is experiencing its biggest disruption in 20 years due to AI. Alongside that disruption sits a trust gap. 

Also Read: Fusing Intelligence and Governance: Salesforce and Anthropic Launch "Claudeforce" to Drive Conversational GTM Execution

“The emergence of AI has left marketers with a bad trade-off. They can get an answer fast, or they can get one they can trust,” says Nick Turner, CEO at Dreamdata. “B2B marketing teams are already moving their analytics work into agents like Claude to be more efficient, but the pitfall is getting a wrong response, because it lacks structured data and context. The risk for marketing teams is to allocate budget to the wrong marketing activities or channels. 

“A governed semantic layer means that Dreamdata AI never recalculates the numbers itself so it cannot misrepresent the truth, which means you don’t have to trade speed for trust. That’s the difference between an agent that treats every prompt as a discussion about metric definitions and an agent that already knows your funnel.”

Dreamdata AI delivers trust wherever you work

The three new AI products are built around Dreamdata’s existing data model, providing account-based context that generic AI agents do not offer. They also offer customers the flexibility to work inside the Dreamdata platform or within their own agents.

• Dreamdata Analytics Agent: Customers ask in plain English and get the same report every time, no matter who asks across the organization, such as which campaigns drove pipeline the previous quarter. Every question uses Dreamdata’s account-based data model and a governed semantic layer, so metrics stay consistent and the right analysis is applied. It doesn’t stop at building the report; the agent also interprets the numbers and recommends what actions to take next.
• Dreamdata MCP Server: For customers already working inside a large language model (LLM) and who need the context, it offers the same functionality as the Analytics Agent without leaving the LLM the team already uses. Dreamdata’s account-based data model brings complete GTM data into the LLM and the governed semantic layer means definitions are consistent so users don’t have to re-explain the funnel from scratch with each query.
• Dreamdata Data Warehouse: Exports Dreamdata’s account-based data model as an out-of-the-box GTM warehouse with the analytics already built directly into the schema, so customers can add their own agent on top of it. Instead of building performance reports from scratch every time a marketer needs to decide which channels to invest in, teams can connect the agent straight to their own warehouse or a hosted MCP and go from there.

Nick Turner adds: “Today, you can try to upload your GTM data to a generic AI agent, but the problem is that the dataset is too large to fit into their context windows and it lacks context from the start. You end up getting inconsistent answers and re-explaining definitions, date ranges or scope, wasting the time you thought you’d won back.”

“We built Dreamdata AI to give B2B marketers an alternative. You don't have to choose between efficiency and trust. We're giving you both, because it understands your goals and gives you the math behind every number, so you walk into performance conversations with the board ready.”

The trust factor - Dreamdata AI technical information:

With Dreamdata AI, every customer gets an AI experience built for their organization. Dreamdata collects their unique GTM data in one place and organizes it around accounts with every touchpoint tied to revenue. This enables AI to answer from the full picture instead of piecing together the story from raw tables, so B2B marketers get reliable answers without depending on operations or data teams.

Dreamdata AI comes with a governed semantic layer that turns raw data into standardized definitions, metrics and calculations, each with one official meaning. Most semantic layers know what numbers mean and how they connect. Dreamdata also knows the right way to analyze them, learning from proven analyses. Dreamdata AI understands the questions asked and picks the right analysis, so marketers can trust it will give the same answer to the same question every time. 

Customers can open the Dreamdata AI report configurator on the in-app and MCP offerings to see the report behind answers and verify the output. When customers export Dreamdata’s unified, account-based model to their own warehouse, the schema is fully documented, so their AI agent reads the model the correct way, instead of guessing.

Customer quotes:

According to Jed Fudally, Director, Demand Generation at Siro, which provides AI sales coaching software: "With generic AI, I'm confident it'll give me a response. I'm just not confident that the response is accurate. The Dreamdata Analytics Agent shows me exactly how the report was built, the filters, the model, the date range, so I can check it for myself. That's what earns my trust." 

Harjeet Singh, Sr Director Marketing & Demand Gen Operations, at financial services software company, Finastra, adds: “Within an instant the agent builds a report so I can see what drove pipeline in the past three months and that decides where I invest next."