Research Synthesis
Turn interviews, tickets, and docs into actionable design insights and themes.
Scale Your Team with Gen AI in Product Design
Empower your product teams to move from initial concept to validated design faster. Lyzr is the secure, enterprise-grade Gen AI platform for modern product design.
Achieve faster concepting
Create spec-aware designs
Experience fewer iterations
AI-Assisted Design
for Your Workflow:
Lyzr supports your entire product design lifecycle. From synthesizing research to accelerating prototypes and ensuring governed handoffs, we streamline every step of the way.
Idea Generation
Produce diverse concepts based on your constraints, personas, and project goals.
Prototype Acceleration
Create user flows and initial wireframes much faster for validation and iteration.
Handoff Readiness
Output spec-aligned assets and developer notes for engineering teams.
System Adherence
Ensure all generated components align with your established design system rules.
Workflows
Workflows
Select a workflow and empower your team to deliver faster, more consistent results while improving cross-functional collaboration and alignment.
From PRD to Concepts
Convert requirements into multiple UI/UX directions with constraints.
Design System Growth
Generate compliant new components and variants aligned to design tokens and rules.
Usability Insights
Summarize user testing feedback and propose fixes prioritized by impact and effort.
Move beyond slow cycles and stakeholder churn. Deliver confident, spec-aligned designs with speed and clarity.
Unlock Measurable Value
For Your Design Team
Shorter Design Cycles
Reduce time from initial idea to a testable prototype without sacrificing quality.
Higher Concept Coverage
Explore more creative options each sprint while keeping all constraints consistent.
Better Spec Alignment
Ensure designs stay tied to requirements, accessibility, and system rules.
Stronger Collaboration
Align PM, design, and engineering with shared, clear artifacts.
Enterprise Capabilities
for Product Design
Our platform supports multimodal inputs, robust governance, and seamless integration into your product design workflow from start to finish.
Multimodal Inputs
Use PRDs, sketches, user feedback, and screenshots as inputs to generate outputs.
Constraint Generation
Generate concepts that strictly follow brand, accessibility, and platform rules.
Automated Design QA Checks
Automatically flag inconsistencies in flows, copy, components, and requirements.
Workflow Integrations
Connect to your existing tools like Jira, Figma, and Confluence for usable artifacts.
Enterprise Governance
Utilize permissions, audit logs, and secure data handling controls.
Comparing Lyzr to
Generic AI Tools
| Feature | Generic AI Tools | Copywriting AI | Lyzr |
|---|---|---|---|
| PRD to Design Flow | Manual process | Not applicable | Automated concept gen |
| Design-System Adherence | No awareness | Style guide only | Built-in compliance |
| Multimodal Input | Text-only input | Text-based | Text, image, and docs |
| Integrations | Requires API work | Limited plugins | Native Jira/Figma sync |
| Audit Controls | No audit trails | User-level only | Full enterprise controls |
| Spec and Access Checks | No validation | Not supported | Automated QA checks |
| Fast Concept Iteration | Manual edits | Copy edits | Instant design variants |
| Research Synthesis | Manual analysis | Limited scope | Automated insight reports |
| Handoff Documentation | Manual annotation | Not applicable | Auto-generated specs |
| Enterprise Deploy | Public SaaS | SaaS only | VPC or On-Premise |
The Enterprise Choice
for Design AI
Workflow-first AI
Built for product design steps, not isolated chat interactions.
Design-safe Outputs
Produces consistent assets aligned to your specs, patterns, and constraints.
Toolchain Ready
Fits into your existing design, PM, and engineering tools with minimal disruption.
Secure by Design
Supports robust permissions, data controls, and compliance-friendly operations.
Trusted by Industry
Leading Design Teams
Leading B2B SaaS and enterprise software companies trust Lyzr to accelerate their design cycles, enhance collaboration, and maintain quality at scale.
Lyzr has fundamentally changed our design sprints. We're now moving from a detailed PRD to a range of high-fidelity, testable concepts in days, not weeks. The alignment between product specs and the initial design outputs has eliminated at least one major rework loop for every new feature.
Head of PD · Fintech B2B SaaS Company
Zero
Data exfiltration incidents
Your Path to AI-Powered
Product Design
Workflow Discovery
Identify your top design bottlenecks and target use cases.
Ingest Data & Rules
Connect PRDs, your design system, project constraints, and policies.
Pilot Sprint
Run a controlled pilot with clear, measurable cycle-time reduction goals.
Scale and Govern
Expand to more teams, add integrations, and set governance KPIs.
Your Questions Answered on
Gen AI in Product Design
What is Gen AI in product design used for in real teams?
In real-world teams, it's used for concept ideation automation, turning product requirements into multiple design directions instantly. It also accelerates prototyping by generating wireframes and user flows, and synthesizes user research into actionable insights for faster, data-informed decisions.
How does Gen AI in product design fit an existing process?
It integrates seamlessly, acting as a co-pilot. It doesn't replace designers but augments their workflow. It can connect to tools like Figma and Jira, taking inputs like PRDs to produce initial designs, which are then refined by the design team, saving significant upfront effort.
What inputs work best for Gen AI in product design today?
Multimodal inputs deliver the best results. This includes structured product requirement documents (PRDs), user personas, raw user feedback, existing design system rules, and even rough sketches or wireframes. The more context the AI has, the more relevant and aligned its outputs will be.
Can it follow our design system and accessibility rules?
Yes. Enterprise-grade platforms are designed for this. You can provide your design system tokens, component libraries, and accessibility standards (like WCAG) as constraints. The AI then generates designs that are compliant from the start, reducing manual checks and rework.
How does this support AI-assisted design and quality?
By automating repetitive tasks, AI-assisted design frees up designers to focus on strategic thinking, complex problem-solving, and creativity. Quality is enhanced by ensuring all generated concepts adhere strictly to predefined constraints, specs, and accessibility rules from the outset.
Does it integrate with Figma, Jira, and Confluence?
Yes, robust platforms offer native or deep integrations. This allows for a smooth AI product design workflow, such as pulling requirements from a Jira ticket, generating designs that can be pushed to Figma, and documenting the rationale and specs back into Confluence automatically.
How do you prevent hallucinations or off-spec designs?
This is managed through strong constraint-based generation and grounding. By providing the AI with specific rules from your design system, PRDs, and accessibility guidelines, its creative space is narrowed. This ensures outputs are relevant, consistent, and adhere strictly to project specs.
What governance controls exist for enterprise product design teams?
Enterprise solutions provide comprehensive governance, including role-based access controls, detailed audit logs to track generation history, and secure handling of all proprietary data. You can manage who can use which models and data sources, ensuring full compliance and security.
How do we measure ROI for generative AI for product development?
Key metrics for ROI include reduction in design cycle time from concept to prototype, an increase in the number of design variations explored per project, and a decrease in rework caused by misaligned specs. You can also measure improvements in design-to-development handoff efficiency.
How fast can a team pilot an AI product design workflow?
A focused pilot can be launched within a few weeks. It typically starts by identifying a specific, high-value use case, connecting the necessary data sources like your design system and a sample PRD, and then running a single design sprint to measure the impact on speed and quality.
Got a use case in mind?
8 weeks from use case to
8 weeks from use case to
agents running in production.
Platform, people and FDEs, all in. Bring your environment. We’ll co-build and stay until it’s
live.