Modelplace.ai

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 designed to capture and analyze inbound interest efficiently.

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, providing real-time visibility 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 — indicating 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 major rework later.

Results depend on market conditions, budget, audience, and campaign setup.

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.