Cint, a research and measurement technology company, is working with Potloc to develop a Model Context Protocol (MCP) that connects AI agents directly with Cint's platform. The MCP allows users to execute research and measurement workflows through natural language prompts, enabling them to define audiences, evaluate feasibility, launch studies and monitor projects without deep technical expertise. Potloc is already using the MCP in its sampling workflow. Last week, an automated run configured a 20-market consumer banking study on the Cint Exchange in one pass — work that previously required manual configuration for each market. Mathieu Dussart, director of sampling strategy at Potloc, said fieldwork managers can now draft, price and validate target groups conversationally in a fraction of the time. Built on the open Model Context Protocol standard, the tool complements Cint's existing API and platform interfaces. Cint is inviting additional AI-forward companies to become beta partners.

New MCP collaboration between Cint and Potloc unlocks advances in AI-native Research and Measurement intelligence. Collaboration will help shape Cint's Model Context Protocol (MCP) capabilities, designed to connect AI agents directly with research and media intelligence workflows. September 01, 2026 15:05 ET | Source: Cint NEW YORK, Sept. 01, 2026 (GLOBE NEWSWIRE) - Cint, a global leader in research and measurement technology, today announced it is working with Potloc to help develop and test a Cint Model Context Protocol (MCP), an AI-native capability designed to connect AI agents directly with Cint's research and measurement platform. The collaboration comes as Cint works with customers to shape how agentic AI can accelerate day-to-day research and media intelligence workflows. The MCP brings the power of Cint into the context of daily work, allowing customers to orchestrate and automate their workflows without deep technical expertise. Benefits include the ability to integrate Cint with multiple systems that users interact with, and create skills and personalized outputs that suit specific personalized needs. Built on the open Model Context Protocol standard, the Cint MCP will allow users to carry out their research and measurement workflows using natural language prompts via the LLMs of their choice, with the AI agent translating that intent into action. "AI is quickly changing how businesses access information, make decisions, and execute workflows, and we believe research and measurement need to be available within those environments," said Lindsay Fordham, SVP Product at Cint. "By working directly with customers such as Potloc as we develop the Cint MCP, we can build around real-world needs and create a more intuitive way for AI systems to access verified human and media intelligence." For research teams, the Cint MCP is being developed to support workflows across the research lifecycle, including defining audiences through plain-language prompts, evaluating feasibility across markets, receiving pricing guidance, creating and launching studies, monitoring fieldwork and optimizing projects in real time. On the measurement side, Cint is exploring how MCP-enabled workflows can make it easier for customers to activate measurement studies, monitor in-flight performance and generate tailored insights at scale. The collaboration with Potloc will provide Cint with direct customer feedback as it continues to develop and refine these capabilities. Potloc is already using the MCP server in its day-to-day sampling workflow. Last week, a single automated run set up a 20-market consumer banking study on the Cint Exchange - 20 target groups, each with its own locale, feasibility check and bid - in one pass, work that previously meant configuring each market by hand in the console. "We've embedded the Cint MCP directly into our sampling workflows: our fieldwork managers draft, price and validate target groups conversationally, in a fraction of the time," said Mathieu Dussart, Director of Sampling Strategy at Potloc. "Working with Cint at this stage means the tool is being shaped around how research teams actually operate." Building an MCP reflects a broader shift in how Cint sees customers interacting with its technology. As AI increasingly becomes the interface through which organizations access information and execute work, Cint is building toward a future in which its research and measurement capabilities can be accessed directly within the AI tools and agentic systems customers already use. The Cint MCP complements the company's existing API and platform interfaces rather than replacing them, providing customers with another way to interact with the same underlying technology. With Potloc as an early design partner, Cint is now inviting additional AI-forward companies to be beta partners, using their feedback to guide continued development of the MCP experience. About Cint Cint is the technology foundation for continuous research and measurement. With a global footprint and programmatic access to hundreds of millions of consumers across 130+ countries, Cint connects researchers, advertisers, and brands to reliable data from the world's largest network of high-quality sources. Researchers can collect human and advanced data at speed. Advertisers and their partners can measure campaign effectiveness at scale. Brands can move from insight to action with clarity. By operating the connective layer between asking questions and measuring advertising outcomes, Cint enables organizations to move faster and make confident business decisions. At Cint, we're feeding the world's curiosity. About Potloc The world's consulting and private equity powerhouses turn survey questions into strategic revelations with Potloc. Our all-in survey platform is designed to be the fastest, most reliable asset for understanding market shifts. With unrivaled sample quality, AI-powered analysis, and end-to-end research oversight, it all adds up to a competitive advantage. Since 2013, we've helped 500+ global firms collect dynamic insights from 343M+ B2B and B2C responses across industries and geographies. Learn how Potloc supports high-speed, high-stakes missions, including corporate strategy, due diligence, pre-due diligence, portfolio monitoring, and thought leadership at potloc.com.

Daily research news online. The global MR industry's daily paper since 2000. Follow DRNO on... Cint partners with Potloc to develop MCP. September 1 2026 Cint is teaming up with Potloc, a survey platform for consulting and private equity firms, to develop and test an MCP capability, connecting clients' AI agents directly with Cint's research and measurement platform. Potloc's end-to-end survey platform is designed to be 'the fastest, most reliable asset for understanding market shifts,' and is used to support corporate strategy, due diligence, pre-due diligence, portfolio monitoring and thought leadership by its global finance and consulting clients. The new offering will use the Model Context Protocol standard to integrate Cint's technology and marketplace into natural language existing conversations with their chosen LLMs. Users will be able to define audiences through plain-language prompts, evaluate feasibility across markets, receive pricing guidance, create and launching studies, monitor fieldwork and optimize projects in real time. Cint says it is also 'exploring' how customers can activate measurement studies, monitor in-flight performance and generate tailored insights at scale, and the collaboration with Potloc will provide it with direct customer feedback during the development. Potloc is already using the MCP server in its day-to-day sampling workflow and says that last week it set up a 20-market consumer banking study on the Cint Exchange - 20 target groups, each with its own locale, feasibility check and bid - in a single automated run. 'AI is quickly changing how businesses access information, make decisions, and execute workflows, says Lindsay Fordham (pictured), SVP Product at Cint, 'and we believe research and measurement need to be available within those environments. By working directly with customers such as Potloc as we develop the Cint MCP, we can build around real-world needs and create a more intuitive way for AI systems to access verified human and media intelligence.' Mathieu Dussart, Director of Sampling Strategy at Potloc adds: 'Working with Cint at this stage means the tool is being shaped around how research teams actually operate.' Cint, which says it is now inviting additional 'AI-forward' companies to be beta partners, notes that the MCP complements its existing API and platform interfaces rather than replacing them, providing customers with another way to interact with the same underlying technology. Web sites: www.cint.com and www.potloc.com. All articles 2006-23 written and edited by Mel Crowther and/or Nick Thomas, 2024- by Nick Thomas, unless otherwise stated. Most viewed items in the last week... Each (*) indicates > 1,000 views. Select a region below...