Modelplace.ai, a platform offering ready-to-integrate computer vision neural networks, approached us to test market demand in the U.S. Instead of building complex infrastructure upfront, we focused on creating a lean digital system capable of generating, capturing, and analyzing leads — fast.

Our Approach

01 — Strategy & Positioning

We began by defining the hypothesis: is there measurable interest in off-the-shelf AI models among U.S. developers and companies? We mapped the target audience, clarified the key "pain–benefit" messaging, and designed the full funnel structure — from ad click to first contact.

02 — MVP Analytics Infrastructure

Before launching any ads, we built a robust analytics setup integrating Google Analytics, Meta Pixel, and LinkedIn tracking. All data points were connected to a unified Notion-based CRM to visualize engagement and lead flow in real time.

03 — System Architecture & Integrations

We created a minimal viable backend for lead management — using Make.com webhooks to connect the website, Notion CRM, Mailchimp, and Calendly into one seamless automation chain. Slack alerts handled errors instantly, ensuring 100% transparency for the team. Every lead, tag, and booking updated automatically — no manual processing.

04 — Google Ads Campaigns

We launched contextual campaigns to test demand across multiple audiences and search intents. A/B testing helped isolate high-conversion segments and eliminate non-performing queries. All data was sent directly to dashboards for daily performance monitoring.

05 — Insights & Growth Loops

Within the first iteration, the system generated measurable leads and interaction data — validating user interest and informing future positioning. More importantly, the infrastructure remained reusable for other product hypotheses and future launches.

What We Delivered

End-to-end automated lead system for testing market traction
Unified analytics and CRM integration with Notion and Make.com
Hypothesis-driven ad campaigns in Google Ads
Real-time reporting and Slack-based error control
Ready-to-scale automation template for future startup launches

Result

The project demonstrated how early-stage validation can run on the same scalable logic as mature startups. By connecting automation, analytics, and CRM from day one, Modelplace.ai was able to test — learn — and scale, without rebuilding later.

Core Message

Market validation at startup speed. We built a fully automated lead & analytics system for Modelplace.ai to test real-world demand — using production-ready architecture from day one.