RRepoGEO

REPOGEO REPORT · LITE

Deodat-Lawson/LaunchStack

Default branch main · commit 892c0266 · scanned 6/5/2026, 10:41:31 AM

GitHub: 861 stars · 121 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Deodat-Lawson/LaunchStack, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening statement to clarify its role as an AI engine for specific applications.

    Why:

    CURRENT
    **The TypeScript engine for AI-native Next.js apps.** Ingestion, OCR, RAG, knowledge graph, LLM abstractions, and background jobs — ports-based, framework-agnostic, and designed to be wired into the Next.js app you already have.
    COPY-PASTE FIX
    **The AI-powered Engine for Next.js StartUp Accelerators.** Ingestion, OCR, RAG, knowledge graph, LLM abstractions, and background jobs — designed to power document-centric AI applications and workflows.
  • mediumtopics#2
    Add more specific topics to improve category visibility.

    Why:

    CURRENT
    claude, docker, knowlege-graph, langchain, mcp, n8n, neo4j, nextjs, ollama, open-source, openai, pgvector, postgresql, rag, self-hosted, typescript, workflow-automation
    COPY-PASTE FIX
    claude, docker, knowlege-graph, langchain, mcp, n8n, neo4j, nextjs, ollama, open-source, openai, pgvector, postgresql, rag, self-hosted, typescript, workflow-automation, startup-accelerator, document-processing, ai-engine, ai-workflows, knowledge-base
  • lowabout#3
    Refine the 'About' description to reinforce its role as an AI engine for specific applications.

    Why:

    CURRENT
    AI-powered StartUp Accelerator Engine built with Next.js, LangChain, PostgreSQL + pgvector. Upload, organize, and chat with documents. Includes predictive missing-document detection, role-based workflows, and page-level insight extraction.
    COPY-PASTE FIX
    A TypeScript engine for AI-native Next.js applications, providing core functionalities for document ingestion, RAG, knowledge graphs, and AI-powered workflows. Designed for building custom StartUp Accelerator platforms.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface Deodat-Lawson/LaunchStack
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchainjs
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchainjs · recommended 2×
  2. vercel/next.js · recommended 1×
  3. vercel/ai · recommended 1×
  4. openai/openai-node · recommended 1×
  5. GPT-4 · recommended 1×
  • CATEGORY QUERY
    How to build an AI-powered Next.js application with document ingestion and RAG capabilities?
    you: not recommended
    AI recommended (in order):
    1. Next.js (vercel/next.js)
    2. Vercel AI SDK (vercel/ai)
    3. LangChain.js (langchain-ai/langchainjs)
    4. OpenAI API (openai/openai-node)
    5. GPT-4
    6. GPT-3.5 Turbo
    7. Anthropic's Claude
    8. Pinecone (pinecone-io/pinecone-client-js)
    9. Supabase (supabase/supabase)
    10. Cloudinary (cloudinary/cloudinary_npm)
    11. AWS S3 (aws/aws-sdk-js-v3)
    12. Google Cloud Storage (googleapis/nodejs-storage)

    AI recommended 12 alternatives but never named Deodat-Lawson/LaunchStack. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good open-source TypeScript engines for AI apps with knowledge graphs and vector search?
    you: not recommended
    AI recommended (in order):
    1. LangChain.js (langchain-ai/langchainjs)
    2. LlamaIndex.TS (run-llama/llama_index)
    3. TypeORM (typeorm/typeorm)
    4. Prisma (prisma/prisma)
    5. pgvector (pgvector/pgvector)
    6. Neo4j JavaScript Driver (neo4j/neo4j-javascript-driver)
    7. Pinecone (pinecone-io/pinecone-ts-client)
    8. Weaviate (weaviate/weaviate-ts-client)
    9. Qdrant (qdrant/qdrant-js)

    AI recommended 9 alternatives but never named Deodat-Lawson/LaunchStack. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of Deodat-Lawson/LaunchStack?
    pass
    AI named Deodat-Lawson/LaunchStack explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Deodat-Lawson/LaunchStack in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Deodat-Lawson/LaunchStack explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo Deodat-Lawson/LaunchStack solve, and who is the primary audience?
    pass
    AI named Deodat-Lawson/LaunchStack explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Brand-free category queries5 vs 2 in Lite
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