> For the complete documentation index, see [llms.txt](https://resource.consumr.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://resource.consumr.ai/release-notes/release-notes-library/may-2026.md). # May 2026 Release Calendar

Release (Features)	Status	Date of Release
See Trace in Qualitative Research	Live	May-26-2026
Study Queue for AI Twins and Respondents	Live	May-26-2026
Instant Study Preview in Calendar	Live	May-28-2026
B2B Behavior Report power by LinkedIn only	Live	May-26-2026
A Cleaner flow for creating AI Twins	Live	May-26-2026
Bookmark & Search Research Studies	Live	May-26-2026

## See Trace in Qualitative Research Every AI-led Qualitative Research output has to answer one question clearly: what is this based on? On consumr.ai, users can conduct 1:1 Interviews with AI Twins, Focus Groups with a group of AI Twins, Custom Focus Groups, Creative Feedback studies, Brainstorming Sessions, and other Qual Research workflows that help teams understand consumer thinking with speed and depth. These workflows make it possible to ask strategic questions and receive responses from AI Twins that represent real consumer cohorts. But speed is not enough. For Market Research and Consumer Research teams, every insight also needs to be inspectable, explainable, and defensible. That is why we introduced Trace. Trace was built to help users verify the source of AI Twin responses. It gives teams a way to inspect whether a core data point shared by an AI Twin is grounded in real and verifiable consumer signals. Now, Trace goes deeper. ### What’s New Users can now select a specific sentence, claim, phrase, or summary point from an AI Twin response and trace it back toward the underlying data source. This means that when an AI Twin gives an answer during a 1:1 Interview, Focus Group, Creative Feedback study, or any other Qualitative Research workflow, users are no longer limited to accepting the response at face value. They can inspect the origin of the response, understand the data path behind it, and get closer to the source from which the insight was formed. ### Select Any Claim and Trace It Back In Qual Research, insights are often expressed as stories, explanations, or interpretations. An AI Twin may respond with a nuanced answer that combines multiple signals into one coherent point of view. For example: “When I look at digital experience design, I’m searching for a modular, phased approach. I need to see a clear line between a creative AI implementation and the specific KPIs it’s going to move; whether that’s operational efficiency or customer conversion. I’ve seen your work with that national retailer, and while the vision was there, my focus is always going to be on the attribution.” If you try to trace terms like “modular” or “phased approach,” you may not always get a report as a direct source. The AI Twin may have used “modular” because the response later defines that idea through references to AI implementation, specific KPIs, operational efficiency, and customer conversion. This is an important distinction. Some parts of an answer may be directly grounded in source reports. Other parts may come from the AI Twin’s contextual synthesis of linked data points. Trace now helps users inspect that difference more clearly. ### Introducing Grounded Score Because AI Twins use generative reasoning to turn linked datasets into a contextual response, some connecting language may be synthesized rather than directly sourced. To bring more transparency to this process, we are introducing Grounded Score. Grounded Score checks how strongly a selected piece of information is supported by the underlying data source. The algorithm follows the trace path back to the data and evaluates whether the claim is grounded in the available evidence. A higher Grounded Score means the claim is more directly supported by source data. A lower Grounded Score means the claim may rely more on contextual synthesis and should be reviewed with more caution. This gives users a clearer way to distinguish between strongly grounded claims and areas that may need further review. ### Why This Matters for Research Teams Trace and Grounded Score give research, strategy, insights, marketing, and product teams more confidence in AI-led Qualitative Research. They help teams move faster without losing the discipline expected from serious research. A user can run Focus Groups, 1:1 Interviews, Creative Feedback, Brainstorming Sessions, or AI Twin conversations, and still inspect where important findings came from. This is especially useful when insights need to be presented to internal stakeholders, clients, leadership teams, or decision-makers who want to understand not just what the AI Twin said, but why it said it. For consumr.ai, this is central to how we approach AI-led Consumer Research: fast enough for modern decisions, but transparent enough to be inspected, challenged, and trusted. *** ## Study Queue for AI Twins and Respondents Research consistency matters, especially when teams are running multiple studies at the same time. On consumr.ai, a researcher may initiate several workflows to answer a broader research objective. These may include 1:1 Interviews, Focus Groups, AI Surveys, Short Surveys, Concept Testing, Product Testing, Creative Feedback, or Brainstorming Sessions. In many cases, these studies may rely on the same AI Twins, Segments, or Respondent groups. If the same AI Twin or set of Respondents participates in multiple studies at the same time, especially studies with similar or overlapping questions, their responses may vary because each study has its own context. To protect the integrity of the research, we are introducing a study queue. ### What’s New When a selected AI Twin, Segment-generated Respondent group, or set of Respondents is already participating in another study, the next study will automatically enter a queue. The study is not lost, and the user does not need to restart the workflow. It will begin automatically when the relevant AI Twins or Respondents are available. This helps ensure that AI Twins and Respondents can preserve the relevant context from one completed study before participating in the next one. ### How the Queue Works If a user launches a study using AI Twins or Respondents that are already active elsewhere, consumr.ai will gently indicate that those participants are currently engaged in another study. The new study will then wait in sequence. When multiple studies are queued, they are processed in chronological order, based on when they were created or submitted. This allows the same AI Twins or Respondents to participate in studies in a more disciplined sequence, rather than being pulled into parallel conversations or surveys at the same time. ### Why This Matters for Research Quality This update is designed to protect consistency across AI-led Market Research and Consumer Research. In Qualitative Research, the same AI Twin may participate in multiple 1:1 Interviews, Focus Groups, Creative Feedback studies, or Brainstorming Sessions. Preserving chronology helps the AI Twin maintain the right contextual continuity across related discussions. In Quantitative Research and AI Surveys, the same Respondent group may be used across Short Surveys, Concept Testing, Product Testing, or other survey-grade workflows. Queuing helps avoid simultaneous participation in overlapping studies, improving consistency in how responses are generated and interpreted. The result is a more reliable research workflow where speed does not come at the cost of context. ### No Need to Stay on the Page Users do not need to remain on the screen while a study is waiting in the queue. Once the relevant AI Twins or Respondents become available, the study will begin automatically. When the study is complete, the user will receive an email with a direct link to access the completed study or report. This update is part of consumr.ai’s commitment to making AI-led Market Research faster, while preserving the consistency, chronology, and transparency that serious research teams expect.Top of Form *** ## Instant Study Preview in Calendar View As research teams run more studies on consumr.ai, finding the right study quickly becomes increasingly important. Teams may be managing Quantitative Research, Qualitative Research, Focus Groups, AI Surveys, Short Surveys, Concept Testing, Product Testing, Creative Feedback, trackers, and other recurring workflows from the Calendar View. Over time, a study name alone may not provide enough context, especially when teams are reviewing older studies or trying to locate a specific report. To make this easier, we have introduced an instant study preview in Calendar View. ### What’s New When a user clicks a study in Calendar View, consumr.ai now opens a quick preview with the context needed to confirm whether it is the right study. Instead of opening the full report each time just to verify the study, users can now review the study details directly from the calendar interaction and decide whether they want to proceed to the report. ### Why This Matters for Research Teams Research teams often run multiple studies around the same brand, audience, product, campaign, or business question. As those studies accumulate, it can become harder to identify the right one from the study name alone. The instant preview helps users confirm the context of a study before opening it. This is especially useful when revisiting completed studies, comparing older research, managing recurring trackers, or locating a specific output from a larger research program. ### Fewer Clicks, Faster Access This is a focused workflow improvement designed to reduce unnecessary navigation and make study retrieval more intuitive. Users can now move from Calendar View to the right study with fewer clicks and more confidence. This update helps teams spend less time searching for studies and more time acting on the insights inside them.

## B2B Behavior Reports Now Powered by LinkedIn-Based Professional Signals B2B research needs professional context. When teams study an ICP, buying committee, professional audience, industry, company type, or job function, the strongest signals should come from environments where people show up in a professional capacity. A person’s consumer behavior and professional identity can be very different, and mixing those contexts can sometimes create noise in B2B Market Research. To make B2B Behavior Report's data signals more real and verifiable, consumr.ai’s Pro Module now uses LinkedIn-based professional data signals as the foundation for these reports, by ensuring single source of truth. ### What’s New B2B Behavior Reports in the Pro Module are now built entirely from LinkedIn-based professional data signals. Earlier versions used a combination of LinkedIn-based professional signals and non-professional channel signals. While this expanded coverage, it also meant that professional and consumer-channel contexts could sometimes appear together in the same report. With expanded LinkedIn-based data coverage, B2B Behavior Reports now rely on one coherent professional data foundation. ### From Mixed Signals to Professional Context Non-professional channels were not designed to represent professional identity with the same depth as LinkedIn. LinkedIn-based professional signals give consumr.ai stronger coverage across B2B audience dimensions such as job titles, industries, companies, seniority, demographic context, firmographic patterns, and other professional identifiers. This shift helps reduce cross-context noise and makes B2B Behavior Reports more aligned with the way professional audiences actually need to be understood. ### Why This Matters for B2B Research For teams using consumr.ai’s Pro Module, this update improves the quality of professional intelligence across B2B workflows. It helps users build clearer B2B segments, create stronger Professional AI Twins, support ICP research, run more focused Qual Research, and improve the inputs used for Quant Research and AI Surveys. It also gives strategy, marketing, sales, and research teams a more dependable view of professional audiences when studying decision-makers, influencers, buyers, and category stakeholders. B2B Behavior Reports are now more focused, more consistent, and better suited to the professional context they are meant to represent. *** ## A Cleaner Flow for Creating Segments and AI Twins Segmentation studies are one of the most important workflows in Market Research and Consumer Research. They need structure, respondent-level inputs, statistical logic, and clear controls for turning the output into action. For that reason, we are simplifying the way segmentation is handled inside consumr.ai. The “I want to create segments” option is being removed from Research Setup. The capability is not going away. It is moving into a stronger and more complete workflow inside Standard Survey.

### What’s Changing Earlier, users could start segment creation from Research Setup. This included options such as Build using portfolio and Provide an audience panel brief, which helped users run a segmentation survey and then create AI Twins from the resulting segments. We have now moved this workflow into Standard Survey and improved it with a stronger report and clearer user controls.

### Where to Create New Segments Users who want to create new segments should now go to Standard Survey. Inside Standard Survey, they will find Build using portfolio and Provide an audience panel brief lined up as part of the segmentation workflow. This is a more natural home for the process because segmentation is fundamentally a Quantitative Research workflow. It relies on structured survey inputs, Respondents, statistical outputs, and segment creation logic. Housing it inside Standard Survey gives users a clearer flow and a better report. Once the segmentation survey is complete, users can decide whether to create AI Twins from one segment, selected segments, or all resulting segments. ### What Happens to First-Party Behavior Signals The Use 1st Party Behavior Signals option will now be merged into I have segments. This creates a simpler mental model: · If you want to create new segments, start with Standard Survey. · If you already have segments or want to use first-party behavior signals, go to I have segments.

### Why This Matters This update removes duplication from Research Setup and makes the segmentation journey easier to understand. It also gives segmentation studies a stronger Quant Research workflow, richer reporting, and clearer AI Twin creation controls. Users can move from segment creation to AI Twin creation with more confidence, while keeping the process better aligned with how research teams think about Segmentation Surveys, Quantitative Research, and Consumer Research. The result is a cleaner setup experience and a more powerful path from segmentation to AI Twins. *** ## Search and Bookmark Research Studies As the consumr.ai study catalog grows, users need a faster way to find the right research workflow without scrolling through every option. Teams may be moving between Quantitative Research, Qualitative Research, Signals, Focus Groups, AI Surveys, Short Surveys, Concept Testing, Product Testing, Creative Feedback, trackers, and other recurring study types. When users frequently run the same studies, they should be able to access them quickly and start setup with less repetitive navigation. To make this easier, we are introducing search and bookmarks for Research Studies. ### What’s New Users can now search for a study type when creating a new Research Study. They can also save frequently used study types as bookmarks, making it easier to return to the workflows they use most often. This helps users move faster from intent to setup, especially when they already know the type of study they want to run. ### Bookmarked Studies in Calendar View When users create a new study from Calendar View, they will now be able to see the study types they have bookmarked. This makes recurring workflows easier to access. For example, a user who regularly runs Brand Track Surveys, Short Surveys, Focus Groups, Creative Feedback studies, or Product Testing workflows can keep those study types within easier reach.

### Search in New Research Study The New Research Study pop-up now includes a search bar at the top. Instead of manually browsing through the full study catalog, users can search directly for the study type or research workflow they want to launch.

### Why This Matters This update improves study discovery and setup speed. As users work across more Market Research and Consumer Research workflows, navigation becomes an important part of the research experience. Search helps users find the right study faster. Bookmarks help them personalize the setup experience around the workflows they use most. This update helps users spend less time finding the right workflow and more time setting up the research that matters.