Poe
What is Poe?
Poe gives people access to the best AI models, across modalities, all in one place. Claude, GPT-4, Gemini, Grok, and many others, through a unified platform built for consumers, creators, and developers. A single subscription for every AI need, through a chat interface or the API.
It launched within Quora. Small team. Establishing a new playbook in a rapidly evolving space, changing faster than any roadmap could anticipate.
Moving at the speed of the product
Quora operates on a quarterly planning cadence. For a product with a relatively stable roadmap, that works. Poe was different. Priorities that made sense on Monday could be obsolete by Friday. The feedback from product was clear: design wasn't iterating fast enough to keep up.
The problem wasn't effort. It was that the operating model was designed for a different environment. Three changes were adopted, each targeting a different source of lag.
We moved design reviews to a daily cadence, as needed, with the CEO and head of product in the room. Decisions that previously took days to reach the right people now happened same-day to unblock the team. Slack check-ins each morning kept overhead low. If there was nothing to review, we cancelled.
We invested in real-time user sentiment tooling. Quarterly surveys are useful but always looking backward. Combining in-product surveys, lapsed user research, and feedback from highly engaged power users gave us a clearer picture: what features were causing users to leave, and what would make the product worth paying for.
We created a dedicated table stakes workstream to act on those signals consistently. Led by the design manager already closest to the core product work, paired with the lead PM. Resourced based on what the team could absorb each cycle. It became one of the most productive and consistently shipping workstreams on the team.
Freeing the team to do the work that matters
Keeping a product's brand and story current requires constant upkeep. New models launching on a daily basis, landing pages updating, blog posts giving social traffic somewhere to land, legal copy staying current, translations managed across markets. None of it is product work. But all of it was sitting in the engineering queue, competing with product work.
Every ticket was a tradeoff.
The queue wasn't a prioritization problem. It was an ownership problem. Engineering bottlenecking work that others were fully capable of doing themselves. The goal was to eliminate the bottleneck entirely.
A no-code solution meant designers could build and update the marketing site without filing a ticket. Marketers could tell the product story in real time. Legal and ops could do their jobs without waiting in line. Engineering's roadmap stayed focused on the product.
The tooling unlocked capabilities that didn't exist before. Automated AI translations that streamlined ops workflows, templated social assets that freed up design resources, and landing page experimentation that previously would have required data science involvement.
Getting there required making the business case to the CEO and CFO directly. The pitch wasn't about design preferences. It was about organizational efficiency. Each function needed to understand the process change and feel ownership over it. A clear matrix of ownership and a roadmap for rollout made it a straightforward decision once the full picture was visible.
Closing the gap between design and engineering
Quora's engineering culture is full-stack with a backend lean. But frontend work, the layer closest to what users actually see and touch, didn't have a natural owner with deep expertise. Components got built and rebuilt independently. CSS accumulated. A small visual change to something like a modal dialog could take days because there were dozens of separate implementations of the same element.
An early investment in a design engineering consultant got the basics in place: a stable token system for spacing and color. But without dedicated, sustained effort, components stayed inconsistent. The gap between what was designed and what shipped grew.
Transformation didn't happen overnight. It took a series of steps (and missteps) to ensure we solved the problem holistically. Bringing on a full-time design engineer to standardize the component library. The technical work was only part of it. Getting teams to adopt new components over familiar patterns required active evangelism, education, and a real migration effort.
Then a hackathon week became the turning point. The design team had been vocal about wanting more ownership over craft and quality. AI tooling had advanced to the point where designers working directly in the codebase was genuinely viable. We set the team up with access to Claude and the codebase, which required earning engineering trust and passing security review. We spent the week migrating components and shipping real improvements to the product. The results made the case for what came next.
We formalized it under a Design Quality Initiative. At the core, enabling designers to prototype, produce production-level code, fix UI bugs, and contribute to migration directly. Continued advancements in the latest generation of LLMs allowed us to create a single source of truth for our Design System. That source is machine created, machine updated, and machine readable. Anyone building a new feature while leveraging AI could achieve a consistent, quality UI.
This was a huge success. Our UI looked cleaner and more consistent than ever. Most importantly, the design and eng gap started to close. Improved code quality reduced bloat and maintenance overhead that our entire team was happy about. The mobile engineering team saw what was happening and asked design to bring the same approach to the native apps.
