How Zestminds Built Genhance, an AI Visibility Platform for Businesses

Genhance helps businesses check how they appear across AI tools, what sources influence those results, and where stronger visibility can improve outcomes.

Zestminds worked with Rajesh Gautam, PhD, Founder & CEO of Genhance to turn that idea into a live MVP. Our team handled product planning, UX, development, AI integrations, testing, and deployment to take Genhance from concept to launch.

Quick Project Snapshot

The Problem

Before Genhance, AI visibility checks were mostly manual.

If a business wanted to understand how it appeared across AI tools, someone had to test prompts on multiple platforms, compare answers one by one, inspect possible source references separately, and then guess what action might improve future visibility.

That created three practical problems:

• too much manual effort,
• poor visibility into supporting sources,
• and no simple workflow for turning findings into action.

The founder wanted to turn that gap into a product businesses could actually use.

How We Approached the Build

This was not a small feature build. The product had to be understood, scoped, designed, built, tested, and launched from scratch.

Zestminds handled the engagement end to end, starting with requirements analysis and feasibility review, then moving into MVP planning, UI/UX design, frontend and backend development, AI integrations, source review workflows, testing, and deployment.

The goal was clear from the start: keep the product easy enough for business users to understand while making sure the underlying system could support complex workflows reliably.

What Zestminds Built

For the Genhance MVP, Zestminds delivered the core product modules needed to make the platform usable from the first live release.

Product foundation

• marketing website and product pages,
• user login and secure dashboard,
• admin support modules,
• email and system communication workflows.

Core workflow

• query submission workflow,
• AI tool selection,
• multi-engine answer tracking,
• query history.

Source review and insight layer

• source visibility panel,
• visual page review with supporting captures,
• downloadable records for deeper review,
• reporting and insight workflows,
• recommendation flows to highlight visibility opportunities.

This gave the founder a working SaaS product instead of just a concept or prototype.

How Genhance Works for Users

The product was designed to be useful for non-technical users as well.

A typical user can enter a business-related query, choose the AI tool or tools to check, review the answers returned, see whether their business appears, inspect the sources influencing those answers, and understand where stronger listings, broader presence, or better authority signals may help.

This helps users move from “How do AI tools see my business?” to “Now I know what is happening and where I should improve.”

What Made This Product Complex

Genhance needed more than a simple interface. Several parts of the build required careful technical and workflow handling.

Working across multiple AI tools

The platform had to support multiple AI engines while keeping the user experience simple and consistent.

Handling large amounts of information

The system had to gather, separate, and present source-related information in a way users could actually follow.

Keeping the product usable

Even when the backend work was complex, the frontend needed to stay clear and easy to use.

Managing performance carefully

Automation-heavy workflows can put pressure on infrastructure if they are not planned well, so the system had to be built with stability and resource-awareness in mind.

Turning raw information into useful insight

Showing data was not enough. The product had to help users understand what the findings meant and what to do next.

How the MVP Helped the Founder

For the founder, this project turned an emerging idea into a working product that could be shown, tested, used, and improved.

• a live product in 18–20 weeks,
• a foundation for demos and investor conversations,
• a usable workflow for early customers,
• and a strong base for continued feature development.

The product also gained early traction with 70–80 users in the first month.

Planning a similar AI or SaaS product? Talk to Zestminds about your build.

How It Improved the User Workflow

For users, Genhance made AI visibility review much easier.

Instead of checking multiple platforms manually and trying to connect the dots themselves, they could review outputs, sources, and supporting context inside one structured workflow.

This contributed to up to 90% lower manual search effort in key tasks and made the results easier to understand and act on.

Results and Early Traction

Delivery proof

• MVP launched in 18–20 weeks
• 4 AI engines integrated
• built from scratch by Zestminds
• delivered by a focused 5-person team

Early traction

• 70–80 early users in the first month
• continued feature development after launch

Public proof available

• homepage and product introduction,
• logged-in dashboard,
• query form with AI tool selection,
• results view with history, output, and source panel.

Detailed report screens are excluded from the public case study due to NDA restrictions.

Client Feedback

“Zestminds helped us take Genhance from idea to live product. Their team handled strategy, design, engineering, testing, and deployment with strong ownership throughout.”

Rajesh Gautam, PhD

Founder & CEO, Genhance

Tech Stack Used

Frontend

• Next.js

Backend

• Python
• FastAPI

Automation and workflow support

• Playwright

Cloud and infrastructure

• AWS EC2
• AWS S3
• AWS Lambda
• AWS SES

AI integrations

• OpenAI / GPT
• Gemini
• Grok
• Perplexity

Planning a Similar Product?

If you are building an AI product, a workflow-heavy SaaS platform, or a technically complex MVP, Zestminds can help you move from idea to launch with product planning, design, development, testing, and delivery support.