This review is part of a larger series of LinkedIn newsletters titled AI in Market Research: Reviews of AI tools, platforms, and solutions that market researchers should use today.
As GenAI evolves, the most powerful tools aren’t necessarily the ones with the most conversational prowess—they’re the ones that connect. Enter Lindy (https://www.lindy.ai/), a platform designed not just to chat, but to do—by linking together the messy backend of research workflows with intelligent automation that saves time and delivers real value.
Most AI tools are still used in isolation: one for writing, another for analysis, and another for storing or organizing data. Lindy links these tools together into end-to-end workflows, all without needing to write code. For example, if your team stores interview transcripts from a tool like Fathom in a shared spreadsheet, Lindy can monitor that file, automatically generate a summary for each new entry, and send it to the appropriate stakeholder—streamlining reporting and follow-up across projects.
Lindy works by connecting your existing tools—like spreadsheets, documents, or even your Gmail account—to large language models such as Claude, ChatGPT, or Perplexity. You can define workflows that pass data between these systems and generate useful outputs. For example, if you maintain a spreadsheet with company names or product listings, Lindy can initiate a secondary research task (OSINT), pull relevant information, summarize the results, and draft an email or report—automatically triggered each time a new row is added.
Another potential use case is market or technology trend monitoring. Think of it as a more flexible and customized version of Google Alerts: Lindy can track specific terms, summarize what’s happening in the market, and deliver a digest on a recurring basis.
It’s also useful for meeting preparation. Lindy can pull together relevant context before a client call or stakeholder meeting—surfacing past email threads, LinkedIn profiles, CRM notes, and related research. Instead of manually piecing this together, Lindy delivers it in minutes—turning what used to be a scattered, time-consuming process into a streamlined, automated one.
From a security standpoint, Lindy’s model is built to prioritize data privacy. While it leverages models like OpenAI and Claude for some tasks, it doesn’t retain data in a way that puts sensitive materials at risk—a critical consideration when you’re working with client-confidential interview data or product roadmaps. Importantly, when Lindy uses these platforms, the data you submit is not stored or used in the training data for OpenAI, Anthropic, or other model providers. This ensures that your proprietary information stays private and isn’t repurposed to train future versions of those models.
Lindy isn’t alone in this space. Platforms like Make (formerly Integromat) also enable users to connect tools and automate workflows using logic and triggers. We’ll be taking a closer look at how tools like Make compare—and where they might complement or overlap with solutions like Lindy in research and beyond.
Ratings:
Usability:
- 4.5 stars. Smooth interface with thoughtful defaults and easy integration setup.
Power:
- 4.5 stars. Automating workflows like interview summarization and meeting prep saves hours each week.
Flexibility:
- 4 stars. Not an open-ended agent, but plenty of room to customize within specific tasks and flows.
Cost:
- TBD. Pricing is use-case and volume dependent, but the time savings for research teams could justify the spend quickly.
In Sum:
Lindy shifts how market researchers should think about AI—not as standalone assistants, but as connected tools that plug into real workflows. Whether it’s prepping for client meetings or automating summary delivery, Lindy brings structured intelligence into your daily process. For teams looking to scale without losing quality, it’s a quiet powerhouse. And it’s just one sign amongst many that research ops is changing.nt.
