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Go-to-Market

The go-to-market strategy is market-first, not fee-first.

The goal is to become the obvious starting point for OPCs, independent developers, small teams, and SMEs asking:

Which LLM stack should I choose?

Positioning

Choose the right LLM, then verify you're actually using it.

Do not position this as another benchmark. Position it as a decision aid.

Wedge audience

Start with the audience closest to GitHub:

  1. OPCs / one-person companies
  2. independent developers and freelancers
  3. small engineering teams adopting AI coding tools
  4. SMEs choosing their first AI workflow

This audience can discover, star, run, and give feedback without enterprise procurement.

Adoption funnel

GitHub / article / search
  → free CLI report
  → share anonymized result or open selection issue
  → lightweight review
  → selection memo / OPC stack recommendation
  → PoC plan
  → larger implementation only after ROI evidence

Why free first

A free first-pass report creates an option:

  • low user risk
  • low sales friction
  • high learning value
  • more case data
  • more GitHub activity

This is the financial leverage: small cost to create many potential future opportunities.

Channels

Developer channels

  • GitHub topics
  • README SEO
  • Hacker News / Show HN
  • V2EX
  • Reddit developer communities
  • Claude Code / Cursor / Cline communities

Chinese channels

  • 知乎
  • 掘金
  • 即刻
  • 小红书技术号
  • 微信公众号
  • AI 工具交流群

Content topics

  • 中小企业怎么选大模型？
  • OPC / 一人公司如何配置 AI 工具栈？
  • Claude Code、Cursor、Cline 该怎么选？
  • GLM、Qwen、DeepSeek、Kimi、Claude、GPT 怎么选？
  • 为什么选模型后还要验证 endpoint？

Demand-backed messages

Public GitHub issue patterns suggest the strongest early messages are:

  • "Which AI coding model/provider should I actually use?"
  • "OpenAI-compatible is not always compatible — verify before rollout."
  • "Avoid surprise cost, wrong routing, and model mismatch before scaling usage."
  • "Choose a model stack based on scenario, data sensitivity, budget, and deployment constraints."
  • "Private RAG and intranet AI need a different selection path than public API demos."

Use these messages in README copy, GitHub issues, articles, and short posts. Avoid claiming benchmark superiority.

Early success metrics

30-day signals:

  • 100+ GitHub stars
  • 10+ selection issues or discussions
  • 5+ anonymized cases
  • 3+ real user interviews
  • 1+ lightweight paid review or PoC conversation

90-day signals:

  • 500+ stars
  • 50+ selection reports shared
  • 20+ selection requests
  • 5+ real customer calls
  • 2+ paid engagements

Monetization principle

Do not scare users with heavy pricing upfront.

Use a ladder:

  1. free CLI report
  2. lightweight review
  3. personal/OPC stack recommendation
  4. SME selection memo
  5. PoC plan
  6. implementation support

The price should rise only as customer confidence and value evidence rise.