Enterprise teams still copy data between systems all day. Work gets stuck in emails, legacy UIs, and handoffs. That chaos is costly, slow, and risky.
We're a fast-moving team on a mission to end it for good. Traction is strong and we're solving real problems for real customers—but to win, we need exceptional talent. We stay humble, do the work, and let results speak.
What we are buildingWe're building the AI operations platform for retail and CPG enterprises—a horizontal platform where AI agents execute end-to-end work across UIs and APIs with governance built in.
Where copilots stop, Duvo finishes the job. Business users specify the outcome; agents plan, act, request approvals on exceptions, and learn with every run. We start with a retail wedge (category management, supply chain, finance ops) where ROI is obvious, then expand to adjacent functions and sectors.
Velocity is our moat: ship fast, iterate faster, compound learning.
The roleYou will own the "what" and "why" behind what your team builds — defining problems worth solving, setting priorities, and making sure every feature ships with a clear success metric. You work at the intersection of customer needs, business goals, and engineering reality.
This is not a "write specs and hand them off" role. You're technically fluent and you lead through clarity, conviction, and the willingness to do the work.
Your unit of ownership: product outcomes for your area — roadmap, prioritization, success metrics, and the customer feedback loop that drives iteration. You'll own product surfaces like the assignment builder, case management queues, Clarity process analysis, and the integration marketplace.
We're a growing product team scaling into multiple initiatives. You'll be one of the first PMs — initially owning all surfaces, then focusing as the team grows.
What we're looking forThese are non-negotiables—the things we'll specifically evaluate you on:
Systematic customer discovery. You talk to customers regularly, run structured interviews, and synthesize patterns into actionable insights. You distinguish signal from noise and make sure the team builds for real problems, not assumptions.
Prioritization and focus. You ruthlessly scope and sequence work. You can say no to good ideas to protect great ones. You define MVPs that are genuinely minimal but still deliver value.
Outcome-driven thinking. You define success metrics before building starts, instrument them, and hold yourself and the team accountable to results. You kill work that isn't moving the needle.
Cross-functional leadership. You align engineering, design, and stakeholders around a shared plan without relying on authority. You communicate clearly, resolve conflicts, and keep everyone pointed at the same goal.
Technical fluency. You can read a pull request and understand its product implications, write SQL queries to analyze user behavior, prototype ideas using AI coding tools, set up and interpret analytics dashboards, and debug user-reported issues by reading logs. You don't write production code — but you understand the system well enough to make informed product tradeoffs without waiting for an engineer to explain everything.
AI product sense. You understand the unique challenges of AI-powered products — confidence thresholds, human-in-the-loop design, error recovery, and building user trust in autonomous systems. You'll prioritize across product surfaces with incomplete data and make bets on customer needs before you have full certainty.
Have enterprise product experience — navigating procurement, compliance, multi-stakeholder buying, and long sales cycles.
Have a background in automation, workflow tools, or RPA — familiarity with the problem space we're disrupting.
Have shipped 0→1 products, not just iterated on existing ones.
You write PRDs and hand them to engineers — here, you're in the work with the team.
You want to experiment for months before committing to a direction — we iterate fast, make a judgment, and move on.
TypeScript-first
React and Fastify
Postgres, GCP
Latest AI primitives
These are real tradeoffs we've made, not aspirations:
Initiative-driven. We organize around customer problems, not org charts. Problems surface through product feedback, competitive analysis, and direct customer conversations — then we prioritize, build, and ship weekly.
Customer-obsessed. We solve real problems, not hypothetical ones. Features that don't move customer metrics get cut.
Iterative by default. We ship small, learn fast, and never get attached to yesterday's code. This means things break sometimes — we fix forward.
AI-first leverage. We use AI to move faster and focus human time where it matters most. If a tool can do it, a person shouldn't.
Direct feedback. We give each other actionable feedback immediately. This can feel uncomfortable — we think that's worth it.
Autonomy with accountability. We trust people to make decisions and hold them to outcomes, not process.
Unlimited AI budget. We don't just allow AI tools — we strongly encourage them. Want to try a new tool? Buy it. Want to automate part of your workflow? Do it.
Autonomy to do your best work. Want to meet someone to learn from? Set it up. Want a mentor? Go get one. Want to fly out to talk to an important customer? Just ask.
A real AI product with real customers. You're not building demos or internal tools. Enterprise customers use what you ship, and their feedback drives what you build next.
A sharp, motivated team that values ownership and candor.
Competitive compensation with a meaningful equity component. You can trade salary for additional equity if you prefer more upside.
We respect your time and aim to move fast:
Hiring manager screen (30 min). We'll talk about products you've built, how you prioritize, and whether there's mutual fit.
Remote task (async, time-boxed, ~1 hour). A realistic product problem — define the opportunity, scope an MVP, and show how you'd measure success.
Product interview (Prague, ~1 hour). Meet the team. We'll go deeper on customer insight, prioritization tradeoffs, and cross-functional collaboration. No trick questions — we want to see how you think about product.
On-site trial day (2 days). Work alongside us on a real product problem and see how we operate. Fully compensated.
