To bring DesignPro's vision to life, Tintash built a sophisticated AI engine capable of providing actionable, real-time feedback on UI designs. At the core of the system is an Agentic AI-powered pipeline that mimics how a team of expert designers would collaboratively review a design - except it's all done instantly, and at scale.

Agentic AI for UI Design Review

We developed a hierarchical, multi-agent LLM system where 15-20 specialized agents analyze different aspects of a UI design - from CTA placement to branding alignment to marketing clarity. Each agent is tailored to a unique role:

• One agent focuses on understanding the product's audience and goals.
• Another benchmark against industry best practices for interaction design.
• Others specialize in content clarity, accessibility, and mobile responsiveness.

These agents work within a hierarchical agentic framework to communicate, cross-verify, and consolidate their outputs into a single, actionable set of feedback points.

DesignPro uses best practices and AI to ensure your designs follow each and every relevant guideline

Ensuring Accuracy, Reducing Hallucinations

To deliver reliable feedback, we implemented a layered verification system:

• Reflection Mechanism: Agents review each other's outputs to assess accuracy.
• Tool-Enabled Fact Verification: Custom tools extract data from uploaded design files (like Figma), and LLMs verify outputs against them - dramatically reducing hallucinations.
• Few-Shot Prompting: Some agents reference curated examples to ensure the feedback is both contextual and impactful.
• Fine-Tuned Verification Agents: Specialized models trained to flag irrelevant or inaccurate suggestions.

Data Ingestion & Personalization

DesignPro's analysis isn't generic. We built data ingestion pipelines that process:

• Branding guidelines
• Customer analytics
• Marketing copy
• Existing design systems and codebases

We extract this data from multiple sources - PDFs, Figma files, websites, and more - and transform it into vector databases that our RAG (Retrieval-Augmented Generation) agents use for deeply personalized feedback.

Custom Tools for Hybrid Tasks

Some design issues require more than just language understanding - they need computation. So we equipped our agentic system with custom-built algorithmic tools that LLM agents can call upon for specific tasks like layout scoring, color contrast analysis, and design token validation.

It specializes in content clarity, accessibility, and mobile responsiveness.

Collaborative, Iterative Product Development

Our partnership with the client went beyond technical execution. It was a deeply collaborative process led by a founder with extensive product and design experience.

From Intuition to Prototype

The client regularly brings new feature ideas based on conversations with senior designers and trends in the design space. Once an idea is shared, our AI team breaks it down into technical components. We:

• Hold exploratory sessions to identify feasibility
• Build focused proof-of-concepts (POCs) that address the core problem
• Iterate rapidly to test different AI approaches

From Prototype to Demo

Once a POC shows promise, our web team helps build a clickable demo for early user feedback. These demos are shared with designers in the client's network to:

• Observe real-world usage
• Gather feedback on usability and value
• Spot edge cases and common friction points

From Feedback to Full Feature

If the demo validates the core concept - technically and experientially - we move forward with productizing the feature. That includes:

• Enhancing UX
• Improving model robustness
• Integrating into the platform (Figma plugin, Chrome extension, or other MCP servers)

Integration & Delivery

Our architecture is designed for maximum flexibility:

Figma Plugin: For direct design feedback within a designer's workflow

Chrome Extension: For reviewing live websites

MCP Server & REST APIs: Allowing integration into other tools like Cursor or web apps

All core AI pipelines are modular, scalable, and API-driven, so the client can continue to evolve the product rapidly without heavy rework.

UX Critique & Onboarding Audit

Test designs using 130+ psychology principles - and uncover actionable UX and Growth insights

The final product was an Agentic AI design reviewer that functions like a virtual team of expert designers - delivering brand-aligned, real-time feedback on UI designs. Built as a modular, API-driven system, it includes a Figma plugin, Chrome extension, and flexible server integration.

At its core is a multi-agent LLM architecture, where 15–20 specialized agents assess everything from CTA clarity to accessibility and mobile responsiveness. These agents collaborate, cross-verify, and use RAG pipelines to personalize feedback based on brand assets, marketing copy, and customer data. Custom-built tools handle tasks like layout scoring and color contrast analysis - making design QA instant, accurate, and scalable across teams.

• Performed 10,000+ AI-powered design reviews to date.
• Gained trust from designers at Salesforce, Workstream and Miro
• Engages 40+ daily active users who rely on real-time insights.
• Enabled teams to ship higher-converting designs while reducing review cycles from days to hours.
• Raised $1.3M in pre-seed funding to scale the platform further.