Optimal Blog

AI-moderated interviews are moving from experiment to everyday workflow. But for anyone who cares about user experience, the real question isn’t whether an AI can ask good questions. It’s whether the person on the other side wants to answer them. And that comes down to the design more than the model.

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People are more comfortable talking to AI than you might expect. In an Adobe Express survey of 1,005 US consumers, 61% said they were comfortable having conversations with AI-powered systems. A COPC study across six countries found people who knew they were talking to AI reported 34% higher satisfaction than those who weren’t told.

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But a quick chat with a bot isn’t a research interview. So we put it to the test. We ran a study using Optimal’s AI Interviewer with participants at different levels of AI and UX experience, including some who were skeptical going in (and rightfully so!). Because if an AI interview only works for people who already love AI, it doesn’t really work.

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What participants told us

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Most participants described the experience as seamless, smooth, natural and even fun, including several who had reservations going in. Three things stood out.

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1. It felt like a conversation, not a script

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Participants noticed that follow-ups were built on what they had just said. One put it simply:

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“I really felt like I was talking to a real person.”

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Another appreciated that the AI summarised their points before moving on, instead of asking the same question in different ways. That was a frustration they’d had with other AI interview tools.

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2. Less pressure made it easier to open up

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Several participants found the AI easier to talk to than a live interviewer. Without a person across the table, there was less pressure to perform.

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“I have no problems speaking freely.”

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On-screen text alongside the spoken conversation also mattered. One participant with auditory processing difficulties called it out as a genuinely useful accessibility feature.

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Not everyone was fully won over. One participant felt a human would better grasp the emotion behind their answers, though they still rated the experience positively.

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3. Choice built confidence

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Participants could pick how the conversation flowed. One chose “submit response” mode, deciding when each answer was finished. After a good first experience, they said they’d be happy to try “free form” mode next, where the AI responds naturally once they stop speaking.

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Want to hear it from participants directly?
‍Watch the highlight reels in our full study write-up →

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What designers can take from this

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The takeaway reaches well beyond research tools: whether an AI conversation works depends less on the model and more on the experience designed around it. If you’re designing any conversational AI, these are worth stealing.

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• Prove you’re listening. Responses that reference the user’s own words build trust faster than clever phrasing. A short summary before moving on signals “I heard you.”
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• Don’t make people repeat themselves. Rephrasing the same question feels robotic. Move the conversation forward instead.
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• Lower the stakes. Removing social pressure can make people more candid. Clear instructions and a predictable flow help them settle in.
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• Offer more than one channel. Pairing voice with on-screen text supports people who process information differently, and makes the experience easier for everyone.
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• Give users control over pacing. Letting people choose when to submit, or when to go free form, turns uncertainty into confidence.
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• Be honest about limits. Some people will still prefer a human for emotionally nuanced topics. Design AI as an option, not the only path.

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See how Optimal’s AI Interviewer works

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We built these lessons into Optimal’s AI Interviewer, an AI-moderated research tool designed to work with you, not instead of you. It helps teams run more interviews without losing the depth that makes qualitative research valuable.

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1. Add your context. Research goals, audience details and supporting material guide every follow-up.
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2. Build your discussion guide. Set sections, questions, goals and how deeply the AI should probe.
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3. Let it run the interview. The AI listens, adapts and explores relevant threads while staying on your goals.
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4. Get cited insights. An AI-generated summary and key themes, with citations at study, section and question level, plus highlight reels that jump to the moment behind each insight.

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Take this two-minute interactive tour to see it for yourself.

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The bottom line

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AI interviews aren’t about replacing the human side of research. They give teams another way to listen, at a scale that used to need a lot more people and time. And when the experience is designed well, participants are happy to talk.

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Optimal’s AI Interviewer is now in Early Access, book a demo to learn more.

Optimal gives you two ways to use AI to ask questions about your research and explore your findings: AI Chat in Optimal Interviews and Optimal MCP.

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Both let you ask questions in natural language, but they’re designed for different types of research exploration.

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The simple difference: Use AI Chat to explore interview data in depth. Use MCP to explore individual studies, connect research from different studies, connect to other tools, automate existing or new workflows

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AI Chat vs. MCP: Which should I use?

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Use AI Chat when you want to:

• Explore a specific interview topic
• Find supporting evidence in interview sessions
• Find new video clips
• Generate new highlight reels
• Investigate participant reactions in more detail
• Follow evidence back to timestamped interview sessions

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Use MCP when you want to:

• Search across multiple studies
• Compare findings across studies or time periods
• Identify recurring themes and trends
• Combine research from different methods
• Explore individual studies using participant results, study setup and metadata
• Analyse transcripts, task results, user paths and other available research data
• Connect Optimal research to ChatGPT, Claude, Gemini, Cursor or other MCP-compatible tools
• Bring research into broader AI-powered workflows and automate research tasks

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In short

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Go deeper into interviews → AI Chat

Use AI Chat when your question is focused on what participants said, how they reacted, or finding evidence within interview sessions.

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Go broader across research → MCP

Use MCP when your question spans multiple studies, research methods, time periods or data sources, or when you want to bring Optimal research into another AI workflow.

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What is AI Chat in Optimal?

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AI Chat is an AI-powered research assistant built into Optimal Interviews. It helps you explore interview data using natural-language questions.

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Use AI Chat to find what participants said, investigate specific topics, discover supporting evidence and explore interview findings in more depth.

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For example, you can ask:

• “What frustrations did participants mention about checkout?”
• “Show me quotes about trust in the product.”
• “What did participants find confusing about onboarding?”
• “Find examples of participants struggling with the navigation.”

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You can ask follow-up questions to refine your search or investigate a finding further. AI Chat can also surface participant quotes, video clips and highlight reels, with citations that take you back to the supporting evidence in your interviews. Learn more about AI Chat.
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What is Optimal MCP?

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Optimal MCP connects your Optimal research to MCP-compatible AI assistants and tools, including ChatGPT, Claude, Gemini and Cursor.

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MCP is designed for broader research exploration. It lets you work with research data across individual studies or your wider Workspace, making it useful for research synthesis, cross-study analysis and AI-powered research workflows.

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For example, you can ask:

• “What usability issues have appeared across our research this year?”
• “Compare our findings from March and June.”
• “What themes are recurring across our recent studies?”
• “Which studies included feedback about onboarding?”
• “Create a Notion page outline summarising these research findings.”

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Depending on the data available to you, MCP can help you explore study information, participant results, supported survey results, transcripts, task results, user paths, study metadata and AI-generated insights. Learn more about Optimal MCP.

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AI Chat vs. MCP example

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Imagine you want to understand problems with onboarding.

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Use AI Chat

Ask: “What did participants say was confusing about onboarding? Show me examples.”

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Use MCP

Ask: “What onboarding issues have appeared across our research over the past six months?”

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What research data can AI Chat and MCP access?

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AI Chat focuses on your Interviews data, helping you explore interview responses and supporting evidence such as quotes and highlight reels.

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MCP provides read-only access to research data available to you in Optimal, including study information, participant results, supported survey results, and AI insights.

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Best practices for using AI Chat and MCP

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Whichever tool you use, you’ll get more useful results by giving the AI enough context.

• Ask specific questions. Include the product, feature, audience, study or timeframe where relevant.
• Start broad, then dig deeper. Use follow-up questions to explore interesting findings.
• Ask for evidence. Request quotes, examples, citations or the studies supporting a finding.
• Specify the format. Ask for a summary, presentation-ready bullets, or another format that suits your needs. With MCP, you further specify formats, such as charts, tables, or interactive deliverables. 
• Check important findings. Use AI to accelerate analysis, but review the underlying research before making important decisions.

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Find out more in our AI Chat best practices.

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Already using AI Chat or MCP? We’d love to hear how you’re using AI to explore your research. Share your use case, workflow or favorite way to work with AI with our team.

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Not using Optimal yet? See how Optimal can help you bring AI into your research workflow.
Book a demo.

What if you could turn real user feedback into your next design iteration with a single prompt?

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With Optimal MCP and Figma Agent, you can turn user research to design updates in minutes. Pull insights from your Optimal studies into Figma, then let its AI design agent update multiple frames and screens based on real participant feedback. As you gather more feedback, you can keep refining your designs, testing updated prototypes with real users and using what you learn to shape the next iteration.

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Together, Optimal MCP and Figma Agent help close the loop between research and design, helping you turn real user feedback into better designs with every iteration.
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How does the Optimal MCP & Figma Agent work? 

Optimal MCP and Figma Agent help you connect the dots between research and design. Connect Optimal to Figma and draw on findings from one or multiple studies, including participant feedback, usability insights, transcripts and prototype task results. Figma’s AI design agent can use these insights to update existing designs or generate new ones based on your prompts.
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Then, bring your updated prototypes back into Optimal to test them with real participants and see how your changes perform.
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So what does this look like in practice? Imagine you've run a usability study and discovered that participants are struggling to find an important action in your product. Here’s how to put this research design loop into action.

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From research insight to your next breakthrough
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1. Turn user insights into new design possibilities

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Reference Optimal MCP and one or more Optimal studies in Figma’s agent chat. Ask the agent to explore solutions to the usability issues your research revealed and generate design changes to address them.

Example prompt: "Connect to Optimal Workshop MCP, look at study "Mobile banking app", read the feedback and update the prototype based on the participants' feedback. Add these updated prototype screens to a new page in this file, side-by-side with the original artboard."

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2. Explore further. Iterate faster.

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Use AI to explore alternatives, challenge your first approach and move through design possibilities faster. Then bring your expertise to the process. Evaluate the trade-offs, review the prototype flows, refine the details and decide which ideas best serve your users.

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As more participant feedback to your Optimal studies comes through, ask the agent to pull feedback from those new participants to bring those fresh insights into your Figma iterations.

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3. Put your changes to the test

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Here's where the loop gets even more exciting.

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Put your newly updated prototype to the test with a new Optimal study. Have you made that important action easier to find? Does the new design solve the original problem? What could be even better?

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You can test multiple prototypes or combine multiple methods in one study, whether you're working with a prototype, live website or web app, or AI prototype with Optimal’s Usability Testing tool or keep it focused on one prototype with Optimal’s Prototype Testing tool.

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Every round of testing gives you more to work with. Every new insight creates another opportunity to improve.
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Ready to bring user research into your next design iteration?

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AI is changing how quickly we can move from an idea to a design. Make the most of your research by connecting Optimal to Figma’s AI Design Agent with Optimal MCP to build experiences and products people love.

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Get started with the Optimal MCP setup guide or check out the interactive demo.

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AI is opening up new possibilities for qualitative research. But for researchers, it’s not enough for an AI Interviewer to ask relevant questions. An effective AI Interviewer needs to create an experience where participants feel comfortable sharing their perspectives, have the space to explain their thinking and explore their answers in more depth.

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To find out more, we ran a study using Optimal’s AI Interviewer to look at general attitudes towards AI, better understand what participants valued about an AI Interviewer experience, and what worked well and where they saw room for improvement.

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One theme stood out: the value of AI interviews depends not just on the technology, but on the experience it creates for the people taking part.

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What do people think about interacting with AI?

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As AI becomes more familiar in everyday life, the conversation is shifting from whether people will use it to when, why and under what circumstances they find it useful. In a survey of 1,005 US consumers led by Adobe Express, 61% said they were comfortable having conversations with AI-powered systems.

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Another survey of more than 1,000 participants across six countries found that people who knew they were interacting with AI reported satisfaction rates 34% higher than those who weren't informed.

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These findings suggest that interacting with AI isn't inherently uncomfortable for everyone. But comfort with AI doesn't automatically translate into a positive research experience. A quick customer-service interaction is different from a qualitative research interview, where people need to reflect on their experiences, explain their reasoning and sometimes share sensitive or complicated perspectives.

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For researchers, the more useful question is: what makes an AI-led conversation feel comfortable, natural and valuable for participants? 

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What makes an AI-moderated research experience successful?

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In qualitative research, the quality of the conversation matters just as much as the number of responses collected.

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A good AI-moderated interview should give participants the freedom to express themselves in their own words, ask relevant follow-up questions and create opportunities to explore an answer beyond the initial response, and go beyond simply moving people through a series of questions.

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Here are considerations that stood out from participants interacting with Optimal’s AI Interviewer for the first time.

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1. Making AI interviews feel natural and conversational

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Qualitative research aims to uncover the reasoning behind people's choices, not just capture their first response. Relevant follow-up questions can encourage participants to elaborate, clarify an opinion or explore an experience in more depth. The ability to answer in their own words, rather than select from predefined options, is also central to gathering richer feedback.

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In our study, the majority of participants described their experience with Optimal’s AI Interviewer as “seamless,” “smooth”, “natural”, and even "fun". Several were pleasantly surprised by how the interaction felt, including participants who had reservations beforehand. One participant said, "I have no problems speaking freely."

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Automated highlight reel: "The overall experience was widely rated as positive and enjoyable"

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On participant remarked, "It was pleasant and I enjoyed it. And you were great." Others described the AI interview format as something they had “come to expect,” framing it as routine rather than novel. 

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Automated highlight reel: "Overall satisfaction with the AI interview experience was high"

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These responses offer an encouraging perspective on what AI-moderated research can feel like for participants with the right cadence and context. Multiple participants also highlighted that the AI Interviewer’s replies were clearly based on what they had just said, rather than following a rigid script.

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One participant noted it felt like talking to a real person because responses were grounded in their own words: "I really felt like I was talking to a real person and that the responses were based off of what I what I said."

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Automated highlight reel: "Contextual follow-up questions made the conversation feel natural and responsive."

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Another participant highlighted how the AI concisely summarized their points before moving on, without asking the same question in different ways — something they’d found frustrating when using other AI interview tools.

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2. Helping participants feel comfortable and confident

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People are more likely to engage when they understand what to expect and feel comfortable taking part. Several participants found the AI interview easier to talk to than a traditional interview. For some, the absence of a human interviewer reduced the pressure of the interaction, making it easier to share their thoughts. 

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Automated highlight reel: "Talking to an AI felt easier than a live human interview"

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The experience wasn't just about how the conversation sounded either. Participants also found the on-screen text helpful for following along with the spoken interaction. One participant who experiences auditory processing difficulties specifically highlighted this as a useful accessibility feature.

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These observations suggest that small details in the participant experience can make a meaningful difference. Clear instructions, the ability to follow a conversation in more than one way, and a format that feels comfortable can all help people engage with a study.

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One participant noted that they felt a person would more deeply grasp the feelings and emotions behind their answers, but overall rated their experience with the AI Interviewer positively.

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Comfort will vary by person and research context, so the goal isn't to assume AI moderation will suit everyone and every situation, but to provide another considered way for people to share their perspectives. 

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3. Giving participants control over their experience

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A positive AI interview experience doesn't mean every participant will prefer the same format.

With Optimal’s experience, participants appreciated the ability to choose a preferred conversational style that works best for them. One participant, who preferred greater control, initially chose the “submit response” mode. This allowed them to decide when to submit an answer and move on to the AI Interviewer's next question or follow-up.

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After a positive experience with the AI Interviewer, they said they would be willing to try the “free form” option. In this mode, the AI Interviewer responds naturally when the participant finishes speaking. This highlights the value of offering choice and helping participants gain familiarity and comfort.

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Clip: New comfort and willingness to try "free form"

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A new way to engage with participants

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For UX research, design, and product teams, AI Interviews offer another option for gathering qualitative feedback, creating more flexibility in how conversations take place, and supporting gathering evidence at scale.

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The opportunity isn't to replace the human aspect of research. It's to give researchers more ways to listen, learn and make informed decisions.

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Ready to explore AI interviews? Get in touch for a demo or find out how to access Optimal’s AI Interviewer in Early Access.

How to Recruit B2B Participants for User Research

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Recruiting B2B participants is harder than recruiting consumers, and the gap is wide. Decision-makers are scarce, gatekeepers are real, and generic panels rarely reach the job titles you need. The good news: a structured, multi-channel approach consistently works.

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Why B2B Recruitment Challenges Are Different

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Consumer research has a ready supply of willing participants. B2B research does not. The people you need, e.g. procurement leads, IT directors, operations managers, finance executives, are exactly the people who have the least spare time and the most filtered inboxes.

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Gatekeepers are a structural problem. Executive assistants, spam filters, and company email policies all block outreach before it reaches the right person. Gartner research shows 73% of B2B buyers actively avoid suppliers that send irrelevant outreach – a dynamic that applies equally to research recruitment. If your first contact feels like a sales pitch, the door closes.

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Response rates for B2B surveys and studies have also declined over time. Busy professionals receive more outreach than ever, and the bar for what earns a reply keeps rising. That makes the quality of your approach more important than the volume of contacts you reach.

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The hardest participants to recruit are rarely hard to find because of their job title alone. The real difficulty sits one layer deeper: behavioral and firmographic complexity. A finance manager is not a useful targeting label on its own. A finance manager who actively uses invoicing software to manage vendor payments for a team of 20 is. Small business owners are another example; add a revenue tier to your criteria and your incidence rate drops sharply. These layers matter, and ignoring them produces a sample that looks right on paper but fails in the room.

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Participant Recruitment Strategies That Work for B2B

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No single channel fills a B2B research pipeline on its own. The teams that recruit consistently well use several channels at once and rotate based on what each study needs.

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Direct Outreach via LinkedIn

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LinkedIn is the most direct route to specific job titles and industries. You can filter by seniority, company size, sector, and geography, and then reach out with a short, personalised message that explains exactly what the study involves and what you're asking of them. Keep the ask small in the first message. Invite them to a two-minute screener, not a 60-minute interview. The lower the initial commitment, the higher the reply rate.

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Industry Associations and Professional Communities

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Trade associations, Slack communities, and industry forums often have members who are genuinely interested in contributing to research that improves their field. A well-placed post in the right community can outperform hundreds of cold emails. Find the communities where your target participants already gather and ask moderators about appropriate ways to recruit.

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Referral Networks

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Your existing customers and past participants are an underused recruitment channel. A warm referral from a peer carries far more weight than a cold message from a stranger. Build referral into your study design: after each session, ask participants if they know one or two colleagues who fit your profile. Offer a small bonus for successful referrals. This compounds quickly across a research program.

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Specialist Research Panels

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Some platforms maintain curated panels of verified B2B professionals. These panels charge a premium over consumer panels, but they save weeks of sourcing time and reduce the risk of fraudulent or unqualified participants. When evaluating a panel provider, ask specifically how they verify job titles and company information - not all B2B panels apply the same rigour.

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Access B2B Participants Through Your UX Research Platform

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Many UX research platforms have built-in recruitment offerings that remove the sourcing problem entirely. Instead of building a B2B pipeline from scratch, you tap into a panel already vetted for the firmographic and behavioral detail B2B studies need. Some platforms, like Optimal, also offer specialized recruitment and will help you source hard-to-reach participants, or if you are recruiting for moderated research. This is especially helpful if you have criteria that goes beyond demographic and firmographic criteria.

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The advantage isn't just speed. Handling recruitment, screening, and research in one place removes the handoff friction of juggling separate tools and you'll get an additional layer of quality assurance from the platform itself. This typically creates a smoother experience for participants as well.

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When evaluating this option, look for the same rigor you'd expect from any specialist panel; verified job titles, current company information, clear screening criteria, plus the convenience of running the whole study in a single workflow. You can also ask the platform directly about their audience size for a specific segment and cost estimate as part of the evaluation.

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Strategies for Recruiting Niche and Hard-to-Reach Professionals

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Some roles are genuinely hard to reach through any standard channel. C-suite executives, highly regulated professionals (healthcare, finance, legal), and senior buyers in niche industries need a different approach.

Understanding the ecosystem around a target participant is as important as knowing their title. If you're recruiting finance managers who evaluate invoicing tools, your outreach should reflect that specific context, not just their seniority or sector. The more precisely you understand what they do and which tools they rely on, the more relevant your message feels, and the more likely they are to respond.

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Lead with Value, Not the Ask

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Hard-to-reach professionals respond to relevance. If your outreach message leads with what they'll get – a summary of findings, a chance to shape a product they use, a peer benchmarking report – you shift the dynamic from "favour request" to "professional exchange." Frame the study as something worth their time, not something you need from them. Be upfront about the time commitment too. Transparent communication about how long the session runs and exactly what it involves increases willingness to engage, especially among senior participants who guard their calendars closely.

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Use Warm Introductions

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A cold message to a VP of Procurement has a low hit rate. A message from someone they already know has a much better one. Map your internal network; sales, customer success, account management, and identify who has existing relationships with the profiles you need. A brief internal email asking for an introduction often works faster than any external outreach.

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Work Through Recruiters and Staffing Agencies

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Specialist B2B recruiters maintain databases of professionals in specific industries and roles. Some research recruitment agencies focus exclusively on hard-to-reach segments, such as senior executives, regulated industries, enterprise buyers. Their networks take years to build and are genuinely difficult to replicate through DIY outreach alone.

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Time Your Outreach Carefully

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Timing matters more in B2B than in consumer research. Avoid outreach on Monday mornings and Friday afternoons. Mid-week, mid-morning tends to produce the best reply rates. For senior roles, budget and planning cycles also affect availability. For instance, a CFO is easier to reach in a quieter quarter than during year-end close.

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Screening Methods That Protect Study Quality

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Recruiting the wrong participants wastes everyone's time and produces data you can't use. A well-designed screener is the most important quality control tool you have.

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The biggest risk with screeners is leading participants to the right answer. When someone suspects what you're looking for, they'll guess their way through. The fix is to write questions that surface behavior indirectly. Instead of asking "Do you shop at Amazon?" ask participants to list all the online retailers they've visited in the past month. You get the same signal without telegraphing what qualifies. The same principle applies in B2B: instead of asking "Are you responsible for software purchasing decisions?" ask "Which of the following best describes your involvement in evaluating new tools for your team?" - then list options that include both qualifying and disqualifying answers.

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Defining your criteria precisely before you write a single question is equally important. Job title alone is rarely enough. For small business owners, you may need to specify a revenue tier - and that extra layer will reduce your incidence rate. For software-adjacent roles, you may need to confirm which specific tools a participant actively uses. Know those requirements going in, or your sample will drift.

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Check for consistency. If someone claims to be a Director at a 500-person company but also says they have no budget authority, that's a flag worth following up on. A short phone screen before a longer session catches most of these cases early.

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Best Practices for Incentives and Scheduling

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Incentives that work for consumer panels often fall flat with senior B2B professionals. A $50 gift card is meaningful to a general consumer but barely registers for a Director earning a six-figure salary. The wrong incentive doesn't just fail to attract, it signals that you haven't thought carefully about who you're asking.

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What Incentives Actually Work

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• Charitable donations in the participant's name - many executives prefer this to personal compensation
• Exclusive research summaries - sharing aggregated findings gives participants something professionally useful
• Access to peer benchmarking data - senior buyers often want to know how their organisation compares
• Monetary compensation at a rate that reflects their time - for senior roles, $150-$400 per hour is a reasonable starting point; for C-suite participants, calibrate higher to reflect the real value of their time

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Be transparent about incentives upfront. Burying the compensation detail at the end of a screener reduces completion rates and creates friction.

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Scheduling Flexibility

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Offer a wide range of session times, including early morning and late afternoon slots. Use a self-scheduling tool so participants can book without email back-and-forth. Every extra step in the scheduling process loses participants - especially busy professionals who won't chase a researcher to find a time.

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Key Takeaways

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Recruiting hard-to-reach B2B participants takes more effort than consumer recruitment - but the effort is predictable and manageable with the right approach.

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• Use multiple channels simultaneously: LinkedIn, referrals, associations, and specialist panels
• Lead with value in every outreach message - relevance beats volume
• Write screeners that verify qualifications without telegraphing correct answers - ask about behaviors, not just roles
• Define firmographic and behavioral criteria precisely before writing a single screener question
• Match incentives to the seniority and professional context of your participants
• Be transparent about time commitments and session structure to increase engagement
• Choose platforms that verify B2B credentials, not just self-reported job titles

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The teams that recruit well don't just find participants - they build relationships that make the next study easier. Start with the channels closest to your existing network, then expand outward as your program grows.

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Ready to run your next B2B study? Talk to sales to find the right plan for your team and learn more about Optimal's recruitment capabilities.

Research data becomes even more valuable when it can be used alongside the other data your business relies on.

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The Optimal API gives organizations secure, read-only access to their Optimal research data, so you can connect research with the tools and workflows your teams already use. Bring research data into a data warehouse, build dashboards, automate reporting, or combine research results with product analytics and business metrics.

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Instead of manually exporting research data or relying on static reports, you can create a more connected way to access and use your research at scale.

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What is the Optimal API?

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The Optimal API is a secure, read-only way to access research data from Optimal programmatically. It allows your developers, IT or systems administrators, and API or integration managers to connect Optimal with other business systems.

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For example, you can use the API to:

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• Centralize research data in a data warehouse such as Snowflake.
• Build research dashboards in Power BI, Tableau, Looker, or other business intelligence tools.
• Automate reporting so research data can flow into recurring reports and workflows.
• Create an internal research repository that brings research data together in one place.
• Connect research with business data to explore research alongside product analytics, customer data, or business metrics.
• Build custom applications and integrations around your organization's research data.

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Why connect your research data?

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As research programs grow, manually exporting and combining data can become time-consuming. Connecting research data to your existing data infrastructure can make it easier for teams to find, analyze, and share research across the organization.

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For example, a research team could send Optimal data to Snowflake and combine it with product analytics. A research operations team could build a centralized dashboard showing research activity across teams. A data or analytics team could automate recurring reports instead of relying on researchers to export data manually.

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The API helps make research data more accessible beyond the research team.

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What research data can you access?

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The initial release of the Optimal API provides read-only access to:

• Research activities
• Research metadata
• Participants
• Responses and results
• Insights and AI-generated insights

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This means you can retrieve research data from Optimal without changing or deleting it through the API.

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Who can set up the Optimal API?

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Depending on how your organization manages technology and integrations, the person setting up access to the API might be a:

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• Developer
• IT or systems administrator
• API or integration manager
• Data or analytics team member

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You don't need to be the person building the integration to create the API application. The organization admin is responsible for creating the application and securely sharing its credentials with the appropriate person.

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How to keep your API credentials secure

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Your Client Secret works like a password for your API application. Treat it as sensitive information and only give access to people or systems that need it.

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Follow these best practices:

• Never share your Client Secret publicly. Don't send it through Slack, email, direct messages, or other unsecured channels.
• Use a password manager or secrets manager. Store credentials in a secure tool such as 1Password or your organization's approved secrets manager.
• Don't add secrets to source code. Never commit your Client Secret to a Git repository or include it in configuration files, screenshots, or other files that could be shared.
• Limit access to credentials. Only give API credentials to the people or systems that need them.
• Make application ownership clear. Use a descriptive application name so your team can identify what the application is used for and who manages it.
• Delete applications you no longer use. Removing unused applications helps reduce unnecessary access.

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Build a more connected research workflow

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The Optimal API makes it possible to bring research data into the systems your organization already relies on.

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Whether you're building a research repository, connecting research to business intelligence, automating reporting, or combining research with product data, the API can help reduce manual data handling and make research easier to use across your organization.

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The Optimal API is now currently available to multi-team organizations on Enterprise plans.