From 0 Inbound Leads to a 7-Figure Pipeline

# Project summary

# Strategy

Second, Mint Studios built content specifically for a distribution channel that was still emerging in mid-2025: large language models (LLMs). They developed a framework called "GPT articles": short, factual articles produced one per buying prompt, clustered around topics and structured to be cited by systems such as ChatGPT, Claude, and Gemini.

# Execution

Every GPT article was written by a human. Creation began with repeated in-depth conversations with Fiska's founder and team to capture product specifics and domain expertise. Writers and reviewers refined each piece through multiple rounds of edits to reach the depth and accuracy needed for LLM citation.

To measure reach in this new channel, the team tracked visibility across more than 50 high-intent prompts using Peec.ai. That allowed them to see how their content performed against buyer-oriented queries and whether it surfaced in LLM responses.

# Results (as presented)

# Why this worked

• Human-authored, expert-reviewed content raises factual accuracy and trustworthiness—qualities that encourage citation by LLM systems.
• Tracking visibility at the prompt level provides actionable signals for optimizing which prompts and articles to iterate on.

# Practical steps for teams that want to replicate this

• Set narrow commercial targets tied to pipeline outcomes before creating content.
• Audit buyer journeys to identify bottom-of-funnel search terms or prompts buyers use while shortlisting vendors.

• Use human subject-matter experts and iterative edits to ensure accuracy and depth.
• Monitor visibility across specific prompts or questions (the case used Peec.ai across 50+ prompts) and prioritize revisions on prompts that show traction.

# What to watch for

• Quality matters: lightweight, low-quality content is unlikely to be cited even if it matches prompts.