Instant user feedback for every product decision, verified and trustworthy. Stop relying on backward-looking proxy data with Flowbird.
Backward-looking data used for product decisions in the absence of dedicated user feedback. Abundant in any organization. Explains the status quo, not the future.
survey results, analytics data, past research reports, NPS, app store reviews.
Validate your product ideas against discovered problems and prioritize what to build next.
AI Personal Shopper
A full conversational assistant that asks questions and provides curated recommendations, just like talking to an in-store expert.
Shop from a Screenshot
Lets Emily upload a screenshot from her son. The app's AI would identify the exact product in the image and take her directly to the product page, no typing needed.
Personal Style Profile
A one-time visual quiz helps Sofia define her core aesthetic (e.g., minimalist, streetwear). The app uses this deep understanding to hyper-personalize all her recommendations.
Instant Restock Alerts
An improved 'Notify Me' button that sends an immediate push notification the second an out-of-stock item is available again in a specific size, so you can buy it before it's gone.
Curated Style Boards
Lets Sofia create visual mood boards from products she likes. The app then generates recommendations based on the combined aesthetic of each board, treating it like a mini-profile.
Shop by Size Mode
A dedicated mode where Emily enters her son's size first, so all browsing and search results only show products that are currently in stock in that specific size.
Curated 'Easy Runs' Collection
A dedicated, hand-picked shop section for casual runners, featuring shoes perfect for comfort and everyday use, with simple explanations.
Drop ideas here to kill them
Flowbird’s user research agent simulates interviews with AI personas on-demand. Run interviews anytime, across any number of personas and get results in minutes.
Interview results are first triaged, then verified against other data sources. Critical findings only influence decisions when they are confirmed.
With Flowbird access to your audience is not a bottleneck anymore. Run interviews for every product iteration and across any number of user segments.
What are the biggest problems our users are facing today, and how do they think about it?
Does this idea solve an urgent problem across our users, or just a niche issue?
What do our users think about this idea? Is there a common perspective across all segments?
Are we missing anything to make this solve our users' problems? Is there a hidden showstopper we don't see yet?
What adoption drivers support this idea, and which user segment is most likely to act on it?
Should we commit our engineering resources to build this? Is there a less complex solution?
How should we specify this feature, so we maximize its impact for our users?
What is the key value driver of this feature? Can we get rid of everything else?
Findings that are not success-critical for an idea can be quickly confirmed by what you already know about your product.
Your proxy data is a treasure trove to see patterns & behaviors existing today. Interview findings tell us where to look and what patterns matter.
Some success-critical findings predict the future and can’t be verified in existing data, it’s just not there. Flowbird suggests how to verify these most effectively.
Flowbird creates AI personas from your existing research data and product context.
Our user research agent runs deep conversations with AI personas to uncover insights and emotional depth.
Results are triaged for success-critical insights, which get verified against existing data & knowledge.
Flowbird analyzes all evidence and recommends the best way forward: Build, Refine, Park or Kill.
The final deliverable is a pitch to help communicate your evidence-based decision to stakeholders.
Personas
AI Agents representing user segments.
Emily Carter
The Teen's Buyer
Her high-schooler has strong opinions on style and follows sneaker culture. Emily is the one with the credit card, using the app to fulfill her teen's specific requests and avoid buying the 'wrong' thing.
Profile complete
Sofia Garcia
The Style-Conscious Trendsetter
A fashion-forward individual who sees Nike as a core part of her everyday wardrobe, not just for sports. She engages with the app's editorial content for style inspiration and values personalized recommendations that match her aesthetic.
Profile complete
Alex Miller
The Casual Runner
Runs a few times a week for everyday fitness and buys shoes for comfort, not performance. Finds the catalog's technical jargon exhausting.
Profile complete
Leo Martinez
The Drop Hunter
Buys on release day, when a pair sells out in minutes. Knows exactly what he wants and has no patience for a slow app.
Profile complete
Select up to 3 personas to interview about this idea, in parallel.
0 of 3 critical confirmed
This idea rests on 3 critical assumptions that still need confirming.
A1 · Critical
Needs confirmingUsers experience significant time and mental energy loss when trying to find suitable gear for specific activities due to overwhelming catalog choices and technical jargon.
A2 · Critical
Needs confirmingUsers frequently experience frustration when they see a product they like in real life (e.g., on a friend) but cannot effectively search for it in the Nike App due to a lack of technical vocabulary or specific product names.
A5 · Critical
Needs confirmingUsers are attracted to the AI Personal Shopper primarily for its ability to provide quick, expert-level functional and technical product recommendations with clear justifications, leading to increased confidence and reduced mental effort in purchase decisions for specific activities or gift-giving.
Foundations confirmed
Robin recommends Refine.
Foundations hold. Before building, a real-world test is required for A9, A10: this idea is important enough that no existing data can settle those.
Pitch
Interviewed Emily Carter, Sofia Garcia · September 12, 2026
What it is about
Emily can upload a screenshot of the shoe her son wants. The app uses visual search to find the exact item and confirms it's a perfect match, preventing mistakes.
Audience
Emily Carter
The Teen's Buyer
Sofia Garcia
The Style-Conscious Trendsetter
The pitch
Emily does not care about sneakers. Her son Alex does, intensely, and Emily is the one with the credit card. Every few weeks he sends her a screenshot of the pair he wants, and she goes looking for it, buying for the most demanding customer she will ever have, blind. Sofia is the opposite. She knows exactly what she is looking at and buys for herself on drop day, when a pair sells out in minutes. Different buyers, different reasons, and both of them keep ending up with the wrong shoe.
Nike sells the same silhouette in dozens of colorways, and to Emily they all look alike. So she zooms. She compares the stitching in Alex's screenshot to the stitching on the product page, and it takes twenty minutes. Sofia has the opposite problem: on a drop she has maybe a minute, the thumbnails are near-identical, and she has bought the wrong colorway twice. Our own data agrees with both of them. Gifting sessions spend three times longer on product pages than any other kind, and 'wrong version or colorway' is the stated reason for a third of gifted footwear returns.
Practically every time she buys shoes for Alex... probably twenty minutes just zooming in, trying to see if the stitching was different, or if the sole was a slightly different shade.
On a drop I have maybe a minute. I've bought the wrong colorway twice because the thumbnails were basically identical.
Instant user feedback does not slow down the process, but critically improves every product decision.
Iterate before you build and avoid expensive rework when the product is live.
Transform stakeholder meetings from opinions to evidence-based conversations.
Better understand your audience, so you can build what creates most value for them.
Flowbird is trained on Customer Outsight™, a methodology developed over 30 years of real-world experience and 10,000+ user interviews. You get research-grade quality without needing research skills.
Flowbird’s interviews take about 5 minutes to complete. You can even run multiple interviews in parallel. Get user feedback for any product idea and iteration, learn and refine. Then do it again.
Every product decision is important, as it shapes what your users experience. To make sure you make your decisions with trustworthy data, we verify all success-critical assumptions with other data sources.
Flowbird fully focuses on user research for product teams, nothing else. Our approach makes use of well-known product management frameworks and established interview methods.
We think of Flowbird as a partner to what you do. Our ultimate goal is to help you understand what your users think for every product decision. We do this by providing skills and doing a lot of the busy-work. The final call is always yours.
Making a decision gets you only half-way, now it’s time to convince stakeholders and management that it is the right decision. Storytelling with data is an art and it can quickly transform how you operate.
Stop using proxy data for your product decisions, get instant user feedback today:
Flowbird is user research for product teams. It simulates interviews with AI personas built from your existing research and product context, verifies success-critical findings against your existing data & knowledge, and recommends whether to Build, Refine, Park or Kill an idea. Interviews take about 5 minutes, so you get instant user feedback for every product decision and can iterate before you build.
They don’t replace real users, and they don’t need to. The interviews tell us what to look for. Success-critical findings are then verified against your existing data & knowledge. Where the data doesn’t exist yet, Flowbird suggests how to verify it most effectively. Use Flowbird to iterate fast, and real users to confirm the few decisions that matter most.
No. Flowbird for every decision, real users for the few that matter. Real user research is slow, expensive and hard to schedule, so it gets rationed for big bets. Flowbird covers the everyday product decisions that never get research, and helps you decide which ones deserve real users and what to test with them.
Building is cheap now. Getting a prototype in front of the right users isn’t. You still have to find them, schedule demos, run interviews and make sense of what you heard. That’s worth it for key decisions, but you can’t do it for everything. Flowbird lets you iterate before you get there, and tells you which assumptions your prototype needs to test.
Yes. Access is the hurdle most teams underestimate. Finding users who are willing to talk to you once is hard, finding them again for every iteration is nearly impossible. With Flowbird, access to your audience is no longer a bottleneck: run interviews for every product iteration and across any number of user segments.
Flowbird creates AI personas from your existing research data and product context. The more you know about your users, the closer the personas get to them. Success-critical findings are always verified against your existing data & knowledge before they influence a decision.
Product teams: Heads of Product, product managers and anyone who makes product decisions. Flowbird is built around known product management frameworks and established interview methods, and is trained on Customer Outsight™, a methodology developed over 30 years and 10,000+ user interviews. You get research-grade quality without needing research skills.
AI-moderated interview tools use AI to run interviews with real participants. You still recruit, pay incentives and wait for people to show up, so it stays a study, not an everyday habit. Flowbird simulates interviews with AI personas, so a round of interviews takes minutes, and it adds what those tools leave to you: verification against your existing data & knowledge and a clear recommendation to Build, Refine, Park or Kill.
