QuanticLeads – From AI Concept to Go-To-Market Execution

Building the AI Agent

The Problem
Industrial companies often keep vast “warm” lead lists — prospects they’ve connected with before, who may still be interested in doing business. The challenge? These lists can have thousands of entries, and sales teams rarely have the time to manually sort and prioritize them. This results in missed opportunities and wasted effort chasing low-potential leads.

My Approach
I worked closely with our data and development teams to create an AI agent that could automatically:

• Scan large volumes of leads quickly.

• Match them against the company’s Ideal Customer Profile (ICP) using criteria such as industry, company size, and location.

• Rank leads by likelihood to convert, making the sales team’s next steps crystal clear.

Customer Insights
Before finalizing the ranking logic, I reached out to current customers to understand:

• How they currently rank and prioritize leads.

• Which signals matter most to their sales process (e.g., deal size potential, buying intent, relationship history).

• What pain points they faced with manual lead sorting.

These conversations directly influenced how the AI weighs different factors — ensuring the scoring system aligned with real-world sales priorities rather than theoretical assumptions.

My Contributions

• Defining the ICP matching logic based on both customer interviews and historical sales data.

• Refining the scoring algorithm in collaboration with developers to align with customer-defined priorities.

• Scalability planning to ensure the AI could process thousands of records in seconds.

Impact
By automating lead qualification, the AI agent drastically reduces time spent sorting leads — turning a multi-hour process into something sales reps can do in minutes.

Creating the Go-To-Market Strategy

The Goal
Even the best product can fail without a clear, focused launch plan. My responsibility was to create a go-to-market (GTM) strategy that would make QuanticLeads visible, credible, and desirable to the right audience from day one.

Defining the Market

• Targeted mid-to-large industrial companies with active outbound sales teams.

• Focused on industries where long sales cycles and high deal values make lead prioritization critical.

Crafting the Messaging
After interviewing customers, I learned that their top two concerns were time wasted on unqualified leads and missing high-value opportunities. I shaped the core message around solving both:

“Find your best leads in seconds, not days.”

Channel Strategy

• Direct outreach to decision-makers, backed by pilot success stories.

• LinkedIn advertising targeting sales leaders in manufacturing, logistics, and engineering.

• Partnerships with CRM integration providers to drive adoption in existing sales workflows.

Pilot Program
Designed and executed a trial program with selected customers to:

1. Validate product-market fit.

2. Collect measurable success metrics (conversion lift, time saved).

3. Build case studies for marketing content.

Designing the User Experience

Website Wireframe
I designed the wireframe for the QuanticLeads website to:

• Explain the product in clear, simple terms.

• Showcase benefits with data-backed proof points.

• Encourage trial sign-ups with prominent CTAs.

Browser Extension Concept
The extension lets users:

• See AI-ranked leads inside LinkedIn Sales Navigator or their CRM.

• Get instant recommendations without leaving their current workflow — boosting adoption by keeping the process frictionless.

Reflection

This project combined product management, user research, design, and go-to-market execution.

The biggest lesson? Customer insight is the compass. By involving existing customers early, we built an AI scoring system that truly reflected how sales teams think — not just how we thought they should think.

QuanticLeads is now positioned to be a sales productivity multiplier for industrial companies, and I’m proud to have played a key role in its journey from concept to market readiness.