Speed Up Your Design Workflow with AI Prototyping + Optimal

The pace of product development has never been faster, and the cost of building on assumptions has never been higher. At Optimal, we've spent nearly two decades helping teams get closer to their users, and what we're seeing right now is a fundamental shift in how research gets done. More teams are running research than ever before and timelines to act on findings are tighter, while the expectations for what research needs to deliver keep rising. 

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That shift is exactly what's driving Optimal 3.0, our most ambitious reinvention of the platform yet, designed to give every team the speed, depth, and flexibility that modern research demands. Today's release is the next step in that journey.

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Optimal's new mixed-methods research tool tears down the boundaries between methods. It brings prototype testing, live site testing, and surveys into a single, end-to-end study workflow. And grounded in our product principles: speed to insights, access for all, and communication. 
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A Unified Way to Test Usability
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True multi-method research

Optimal’s new Usability Testing tool marks the next step in the evolution of Optimal 3.0, giving teams the flexibility to evaluate experiences in whatever form they exist today.
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• Early-stage ideas and concepts
• Interactive prototypes
• AI-generated or experimental flows
• Live production experiences
• Competitor or benchmark sites
• Surveys and structured feedback
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Combine prototype testing, AI prototype testing, live site testing, and surveys in a single study. Test multiple prototypes side by side, compare different live URLs, or mix prototype and live site tasks together all in one workflow. Research can now mirror how products actually evolve, from early concept to shipped experience.
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Richer qualitative insight collection

New speak-aloud question types, custom message blocks, auto-generated transcripts and insights, citations and highlight clips help you capture the context and reasoning behind every action. AI-assisted analysis then helps you make sense of it all fast and communicate with impact.
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A redesigned results and insights layer

Review a study overview surfacing key themes, pain points, and sentiment analysis combining insights across all your study methods along with detailed results, task analysis and recordings, transcripts, key quotes, and automatically generated citations and video clips. 

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Coming soon: you can also use AI Chat to chat with your data directly, asking questions and pulling new insights and evidence across all your qualitative and quantitative inputs.

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Six ways to put it to work

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1. Compare design variations in a single study, such as multiple navigation layouts, checkout flows, or onboarding concepts
2. Explore early-stage concepts before committing to build
3. Benchmark current live experience vs a redesigned prototype
4. Test staging vs production, or two campaign landing pages
5. Validate end-to-end journeys from concept to live experiences
6. Compare your experience against competitors‍

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Why this matters

Modern product development is no longer linear. Teams continuously move between:
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• Discovery and validation
• Design and iteration
• Prototype and production
• Concept and reality
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Traditional usability testing tools were not built for this fluidity.  Optimal’s Usability Testing brings the flexibility to match how teams actually work today.

By combining multiple methods into a single study and pairing it with AI-powered synthesis, Usability Testing helps teams reduce setup and analysis time, recruit once, capture richer qualitative context, compare experiences more easily, move faster from feedback to action, and tell clearer, more compelling insight stories. 
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Learn how to get started with Usability Testing in Optimal and accelerate your path from idea to insight. Book a meeting, start exploring in your account, or join our live training webinar on June 24th to see it in action.

User interviews provide some of the richest insights a product team can uncover. But turning hours of recordings and transcripts into clear insights can often be slow and manual without the right tools.

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With automated insights and AI Chat in Optimal Interviews, you can accelerate that entire workflow, from extracting insights from interview recordings to transforming them into outputs that fit directly into the tools your team already works in.

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Instead of spending hours summarizing transcripts and translating research into stakeholder updates, AI Chat helps you quickly generate structured outputs for documentation, tickets, and decision-making.

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Deliver Interview Insights Directly into the Tools Your Team Uses

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AI Chat can surface key themes, quotes, and patterns across participant recordings. Once insights are identified, it can quickly transform them into formats your team already uses.

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You can control the output by specifying tone, length, structure, and level of detail directly in your prompt. The more explicit you are about the format you want, the better the output.

Simply specify the details of the deliverable you want, and AI Chat can structure the output for documentation, planning, and product tools.

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Here’s how teams can use AI Chat with some of the most common product, design, and research tools.
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Notion

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Notion is used by many teams for documentation, knowledge bases, product planning, and research repositories.

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Example AI Chat prompts

• Turn these interview insights into a structured Notion research summary with sections for Key Findings, Supporting Quotes, and Recommendations.
• Create a Notion page outline summarizing onboarding interview insights with headings and bullet points.
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Jira

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Jira is a widely used issue tracking and project management platform that product and engineering teams rely on to manage work, track bugs, and plan development tasks.

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Research insights often lead directly to product improvements, and AI Chat can translate insights into actionable tickets.

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Example AI Chat prompts

• Convert these interview insights into three Jira tickets including title, description, and acceptance criteria.
• Turn this usability issue into a Jira bug ticket.
• Create a Jira epic summarizing onboarding improvements suggested by interview feedback.
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Linear

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Linear is a modern planning and issue tracking tool designed for fast-moving product teams. It’s often used for planning product work, managing projects and engineering tasks, and tracking product improvements.

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AI Chat can quickly convert insights into structured Linear issues.

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Example AI Chat prompts

• Convert these insights into Linear.app issues with clear titles, descriptions, and priority levels.
• Create a Linear.app issue summarizing the navigation problem identified in these interviews.
• Generate a set of tasks for the Linear.app addressing usability problems mentioned by participants.
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Confluence

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Confluence is a team collaboration and documentation platform used to share knowledge, publish research reports, and maintain internal documentation.

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AI Chat can help transform research findings into polished documentation ready for stakeholders.
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Example AI Chat prompts

• Turn these interview insights into a Confluence page with sections for Background, Findings, and Recommendations.
• Create a Confluence page explaining the usability issues uncovered in onboarding research.
• Turn opportunities to improve into concise post-it notes, with one key point per note, written in simple, scannable language to use in a Confluence whiteboard.
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Best practice tip: For cleaner, copy-and-paste-ready outputs, consider adding “Do not include citations.” to any of these suggested prompts.


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Accelerate the Impact of User Research

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By combining automated interview insights with AI Chat, teams can quickly move from recordings to structured insights, and share them in formats that resonate with internal teams and stakeholders.

This makes it easier to clearly communicate what users are saying, build alignment across product, design, and engineering, get buy-in, and turn research insights into decisions that teams are ready to support and action.

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User interviews have always been one of the most trusted and powerful UX research methods. They give you something beyond dashboards or written surveys: real, in-depth conversations and context.

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But they’ve historically come with a cost – time, coordination, and a heavy lift to review recordings and turn videos into insights. Sometimes insights get buried. Recordings sit unused and research becomes challenging to revisit.

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In our recent webinar, we explored how that’s changing and how you can reduce the heavy lift of interview review, while building a research repository. 

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What is a research repository?

A research repository is a centralized system for storing, analyzing, and reusing research data, especially qualitative data like user interviews. It helps teams answer questions like what users said, what patterns emerged, and how past research can inform future decisions.
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For interviews, this means:

• Storing recordings and transcripts
• Organizing insights and themes
• Making research searchable
• Enabling teams to revisit past findings
• Supporting continuous discovery
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Optimal Interviews brings this to life by automatically capturing recordings, generating transcripts, structuring insights from the start, and making everything searchable so teams can easily revisit, build on, and continuously learn from their research.
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So what did we learn? Here are some key takeaways from this webinar, plus answers to the most common questions we heard.
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1. The biggest bottleneck isn’t conducting interviews. It’s everything surrounding it.

Running interviews isn’t just about talking to users. It’s everything before and after:

• Recruiting participants
• Coordinating calendars
• Managing reschedules and no-shows
• Setting up emails and reminders
• Transcribing, organizing, and synthesizing findings
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That overhead adds up quickly. There’s opportunity in automating these workflows and removing the friction around them. Optimal Interviews solves this by:
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• Creating a central calendar
• Emailing participants with confirmations and session reminders
• Automatically capturing recordings
• Generating transcripts
• Uploading and generating summaries and insights
• Structuring insights from the start
• Allowing you to explore instantly with AI Chat
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2. Speed matters more than ever (and it’s finally achievable)

Research isn’t slowing down. Product cycles are getting faster, and teams expect insights just as quickly.

What stood out most:

• Interviews can now go from recording → transcript → insights in minutes
• Teams can share highlight reels, clips and findings almost immediately
• Analysis can start while context is still fresh
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One team told us that a few years ago it took them three weeks to analyze user interviews for an initiative. When they replicated the same study in Optimal Interviews, they were able to generate usable insights in about five minutes.
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That shift from lagging insight to near real-time understanding is where the real impact lies.

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3. Scheduling should feel effortless

Interview scheduling sounds simple, but it’s often where things break down.
You can use Optimal Interviews to ensure:
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• Availability blocks with buffers
• Controlled rescheduling and cancellations
• Video conferencing integrations
• Support for collaborators
• Built-in, secure participant communication & messaging (coming soon)
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When done right, scheduling fades into the background so teams can focus on conversations, not coordination.
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4. AI is reshaping analysis but humans stay in control

AI is already proving its value in the analysis phase:

• Automatic transcription across multiple languages
• Theme and insight extraction across interviews
• Highlight reels and supporting evidence
• Natural language queries over your research
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But one point came through clearly: AI accelerates analysis but it doesn’t replace human judgment and sensitivity.
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Researchers still play a critical role in validating insights, interpreting nuance, and deciding what matters for the business. Think of AI as getting you to 80% faster, while you own the final 20%.

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5. The real unlock is continuous, reusable research

Here’s what you can achieve with Optimal Interviews:

• You can ask questions of past interviews using natural language
• Create new custom themes or topics on demand for AI to add new insights into
• Re-analyze old research with fresh context
• Add new interviews to your existing Optimal Interviews study and refresh the insights
• Identify gaps and spin up new studies faster
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This turns research from static storage into something dynamic, something you can continuously mine and build on.
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FAQs from the Webinar

Does the platform synthesize insights or just aggregate data?

Both. You can extract insights from individual interviews, but the real value often comes from patterns across multiple sessions. Aggregation helps surface stronger, more reliable themes, while still preserving standout moments from single participants.
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How is sensitive data handled?

Privacy is a core focus and consideration with Optimal Interviews. Some of the key protections include automatic redaction of personally identifiable information (PII) and enterprise-grade AI infrastructure with strict data isolation. We're also looking to expand Optimal Interviews anonymized scheduling and communication and manual redaction controls before analysis.
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What if I can’t connect my video conferencing tools?

Integrations are available for Google Meet, Microsoft Teams, and Zoom. 

You can still run everything without integrations:

• Set availability without integrations
• Add conferencing links yourself
• Manage sessions independently
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Integrations are helpful but not required.
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Can I search across multiple studies?

Today, teams often bring relevant interviews into one project for analysis. Looking ahead, the goal is broader. Optimal’s looking into how the platform can search and query across all research, use AI chat to explore insights across studies, and surface insights at a Workspace level.
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Can I query transcripts or AI summaries?

Yes. You can search transcripts directly and use AI-powered chat to explore themes, generate summaries, or even turn findings into shareable outputs like Slack posts or reports.

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Final thought

Interviews aren’t new. But the way we run them and what we can get out of them is changing fast.

By removing operational overhead and reducing time to insight, teams can talk to users more often, share insights faster, and build a research repository that becomes part of everyday product decision-making.
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If you want to experience the full walkthrough, demo, and Q&A from the session, we encourage you to watch the full webinar.
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👉 You can watch the full training webinar here.