RRepoGEO

REPOGEO REPORT · LITE

archestra-ai/archestra

Default branch main · commit d110f794 · scanned 5/26/2026, 1:37:47 PM

GitHub: 3,742 stars · 875 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 archestra-ai/archestra, 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 README opening to clarify self-hosted enterprise AI platform

    Why:

    CURRENT
    # MCP-native Secure AI Platform

Simplify AI usage in your company, providing user-friendly MCP toolbox, observability and control built on a strong security foundation.
    COPY-PASTE FIX
    # MCP-native Secure AI Platform

Archestra is an open-source, self-hostable Enterprise AI Platform. It simplifies AI usage in your company by providing a user-friendly MCP toolbox, observability, and control built on a strong security foundation, offering an alternative to proprietary cloud MLOps solutions.
  • mediumtopics#2
    Add broader and more specific enterprise AI platform topics

    Why:

    CURRENT
    a2a, a2a-mcp, acp, agent, ai, chatgpt, chatgpt-api, claude, deepseek, gemini, k8s, mcp, mcp-client, mcp-gateway, mcp-host, mcp-server, mcp-servers, mcp-tools, openai, runtime
    COPY-PASTE FIX
    a2a, a2a-mcp, acp, agent, ai, chatgpt, chatgpt-api, claude, deepseek, gemini, k8s, mcp, mcp-client, mcp-gateway, mcp-host, mcp-server, mcp-servers, mcp-tools, openai, runtime, enterprise-ai, ai-orchestration, ai-guardrails, ai-security, cost-management, self-hosted
  • lowcomparison#3
    Add a 'Why Archestra?' section highlighting its unique approach

    Why:

    COPY-PASTE FIX
    ## ✨ Why Archestra?

Unlike generic MLOps tools or proprietary cloud platforms, Archestra offers a declarative, YAML-based approach to defining and orchestrating AI agents and workflows, inspired by Kubernetes. This provides unparalleled control, transparency, and security for your enterprise AI deployments, ensuring you own your AI stack.

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 archestra-ai/archestra
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Vertex AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Vertex AI · recommended 2×
  2. Azure Machine Learning · recommended 1×
  3. Amazon SageMaker · recommended 1×
  4. mlflow/mlflow · recommended 1×
  5. Domino Data Lab · recommended 1×
  • CATEGORY QUERY
    How to securely manage and orchestrate multiple AI models across an enterprise, controlling costs?
    you: not recommended
    AI recommended (in order):
    1. Azure Machine Learning
    2. Amazon SageMaker
    3. Google Cloud Vertex AI
    4. MLflow (mlflow/mlflow)
    5. Domino Data Lab
    6. H2O.ai MLOps
    7. Kubeflow (kubeflow/kubeflow)

    AI recommended 7 alternatives but never named archestra-ai/archestra. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Platform for deploying AI agents with built-in security, guardrails, and observability features?
    you: not recommended
    AI recommended (in order):
    1. Azure AI Studio / Azure Machine Learning
    2. Google Cloud Vertex AI
    3. AWS SageMaker
    4. AWS IAM
    5. VPCs
    6. KMS
    7. Amazon Comprehend
    8. AWS Lambda
    9. CloudWatch
    10. CloudTrail
    11. X-Ray
    12. Hugging Face Inference Endpoints
    13. Verta AI
    14. MLflow

    AI recommended 14 alternatives but never named archestra-ai/archestra. 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 archestra-ai/archestra?
    pass
    AI named archestra-ai/archestra explicitly

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

  • If a team adopts archestra-ai/archestra in production, what risks or prerequisites should they evaluate first?
    pass
    AI named archestra-ai/archestra 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 archestra-ai/archestra solve, and who is the primary audience?
    pass
    AI named archestra-ai/archestra explicitly

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

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