Hello everyone. On September 5 we host a very lively Product & Design Talks meetup. We meet peers from the industry and share how, at Novus, we build an AI product by keeping Product and Design shoulder to shoulder. We explain how we use AI tools in our workflow, what challenges we face, and how we manage communication throughout. This post serves as a tidy recap for those who cannot attend and a handy reference for those who do. At Novus, we keep communication open and sincere, and we treat the topic seriously. In the age of AI, we aim to lock in the right team rhythm and turn it into a continuous and measurable practice.

What Is Dot? What Are We Building?

Before anything else, we explain what we build as an AI product. Our flagship is Dot, an agentic AI framework. Dot runs multi model and multi agent architectures and focuses on orchestration. In practice, Dot brings dozens of models, tools, and integrations together under one intelligence backbone and routes each task to the best capacity.

This backbone stands on three legs:

• Autonomous Model Optimization makes real time decisions across the cost quality speed triangle and routes different LLMs and tools to the right context.
• Supervisor AI Agents control the workflow, manage decision points, step in when things go off path, and keep an auditable decision log.
• Chain of Thought and Environment Configuration preserve reasoning traces and the execution environment so work stays reproducible. As a result, we orchestrate many intelligences with a single integration, speed up our learning by doing a loop, and tie outcomes to measurable metrics in the field. Dot also runs in cloud, on prem, and hybrid environments.

Balance: How Product & Design Work Day to Day

We prefer Kanban over fixed sprints so we adapt to a fast moving AI world. We run our flow along Discovery to Alignment to Validation and keep Product and Design in constant handoff.

In Discovery, we frame the problem together with the business goal and define success metrics early. We run benchmarks, user interviews, and market and competitor scans. We surface assumptions, map constraints and opportunities, and shape the first PRD draft, user flow skeletons, and the measurement plan as our single source of truth.

As needs get clearer, we analyze and prioritize. We phase the scope and record decisions transparently on the roadmap. On the Product side, we deepen the PRD. On the Design side, we advance UX flows, interaction logic, and visual language from the same shared context. We validate risky assumptions early with clickable prototypes. Handover is not a one way file toss. We keep a two way dialogue enriched with prototypes, usage scenarios, and accessibility notes. After the Design handover, we get final designs and a ready to use prototype. We keep updating the PRD, decompose the work into small and tractable packages, and move into grooming. Because information and feedback flow well, grooming acts more like a kickoff than a debate. With development handover, we set the path to production, and the process does not end there.

In Validation, we run usability tests, A slash B experiments, and product analytics such as events, funnels, and retention. We feed results back into the backlog. Because we define success thresholds upfront, we decide based on data which features we keep, and we iterate or shelve what does not work.

Tools That Build the Builders: How AI Shapes Our Workflow

We build AI products, and we let AI tools shape how we work. We actively use Dot in our own kitchen. PRD Agent converts the problem, goals, scope, acceptance criteria, and measurement plan into a clean PRD by using past work and shared context. We version it and keep it as the single source of truth.Wireframe to Prototype Code Agent turns simple sketches and interaction notes into a working prototype, for example clickable Next.js components, so we test risky flows the same day.The Figma to PRD MCP bridge cross checks design decisions with requirements and automatically details the PRD based on diffs, including empty states, error messages, and accessibility.With Jira Agent through MCP, we generate epics, stories, and sub tasks from the PRD, set labels, priorities, and dependencies, and keep two way sync as things change.In production, Analytics Companion gathers telemetry and product analytics, proposes experiments, runs impact analysis, and points to the next iteration.

End result: our write, draw and ship loop accelerates while quality gates such as reviews, tests, and measurement trigger automatically.

Sharp Turns Ahead: The Realities of AI

The AI landscape moves fast. Norms are still forming. That speed is both a curse and a gift. We shorten the validation window with early prototypes and controlled experiments. We package the same core tech for different personas and industries and keep design decisions reusable, the architecture modular, and the positioning crisp. We keep the roadmap alive. We phase work by weighing value, effort, and risk, make changes visible, and share them across the company. Our roadmap is not a sacred manifesto. It is a living organism. Above all, we measure before we ship. We track feature performance, conversion, and retention closely, and we treat analytics and user feedback as the fuel of iteration.

We Communicate, Therefore We Ship

We repeat a few words often, by design. Clear communication keeps the system smooth and the chaos low. We maintain cross functional alignment so Product, Design, Engineering, and other teams move to the same rhythm, with agendas, decisions, and dependencies written, accessible, and transparent. With a single source of truth, we version PRDs, design files, flow charts, and metrics in one place so everyone points to the same reference. Product also centralizes incoming feedback, ideas, and suggestions, filters them, and makes them consumable. With a culture of continuous feedback, not only user tests but also internal comments and critiques flow into the backlog through regular rituals. Meeting hygiene and asynchronous habits favor written clarity. Meetings are decision oriented, and notes stay traceable and repeatable. Everyone has a voice. When needed, we prioritize and phase ideas, not just features.

Quick Wrap Up

Success in AI products is less about which model we use and more about the experience we deliver for the right user, in the right moment, with the right context. With Dot orchestration, Product and Design pass the ball faster, and with measurement and automation, we nurture a culture that learns continuously. That culture helps us build Dot on a stronger and more forward looking foundation. We keep communication steady, prioritize ideas and data, and treat not only the product but also product development itself as a living system. Our strongest muscle is not just processes or methodology enhanced by AI, it is our collective communication. We communicate, therefore we ship.

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