buildnext

Synthesize customer feedback into actionable product themes. CLI + MCP server.

Point buildnext at your interview transcripts, Canny board, Slack channels, or Airtable tables. It uses an LLM to find cross-cutting themes, cite exact quotes as evidence, and suggest what to build next.

npm install -g buildnext

# Create a config file
buildnext init

# Set your API key
export ANTHROPIC_API_KEY=sk-ant-...

# Put interview transcripts in ./feedback/
# Run synthesis
buildnext run

1. Ingests customer data from multiple sources (files, Canny, Slack, Airtable, Gong)
2. Synthesizes themes across all feedback using an LLM
3. Outputs a themed report with exact quotes, urgency ratings, and suggested actions

The output is markdown. Pipe it, save it, or read it in your terminal.

| Source | What it pulls | Auth | |--------|-------------|------| | Files | .md, .txt, .docx from a directory | None | | Canny | Feature requests, votes, comments | API key | | Slack | Messages from specific channels | Bot token | | Airtable | Records from any table | Personal access token | | Gong | Call transcripts | Access key + secret |

Create .buildnextrc.json in your project root:

{
  "llm": {
    "provider": "anthropic",
    "model": "claude-sonnet-4-20250514"
  },
  "sources": {
    "files": {
      "directory": "./feedback",
      "extensions": [".md", ".txt", ".docx"]
    },
    "canny": {
      "apiKey": "your-key",
      "boardIds": ["board-id"],
      "since": "2025-01-01"
    },
    "slack": {
      "botToken": "xoxb-your-token",
      "channels": ["C0123FEEDBACK"],
      "since": "2025-01-01"
    },
    "airtable": {
      "apiKey": "pat...",
      "baseId": "appXXX",
      "tableId": "tblXXX",
      "contentFieldId": "fldXXX",
      "titleFieldId": "fldXXX",
      "dateFieldId": "fldXXX"
    }
  },
  "output": {
    "maxThemes":