RRepoGEO

REPOGEO REPORT · LITE

nesaorg/nesa

Default branch main · commit c48412c6 · scanned 5/26/2026, 3:43:09 AM

GitHub: 3,149 stars · 245 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 nesaorg/nesa, 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
    Clarify the project's core purpose in the README's opening paragraph

    Why:

    CURRENT
    <p>Nesa: Run AI models end-to-end encrypted.</p>
    COPY-PASTE FIX
    <p>Nesa provides an API for running AI models with end-to-end encryption, ensuring full privacy and blindness on queries for large language models (LLMs) and other AI inference without compromising performance.</p>
  • highlicense#2
    Add a LICENSE file and declare the license in the README

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root directory with your chosen open-source license (e.g., MIT, Apache-2.0). Then, add a 'License' section to your README.md, stating: 'This project is licensed under the [Your Chosen License Name] License - see the [LICENSE](LICENSE) file for details.'
  • mediumabout#3
    Expand the repository's 'About' description for better category matching

    Why:

    CURRENT
    Run AI models end-to-end encrypted.
    COPY-PASTE FIX
    Nesa enables secure, private AI inference by running large language models (LLMs) and other AI models with end-to-end encryption, ensuring zero visibility on data and full blindness on queries without performance compromise.

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 nesaorg/nesa
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Microsoft SEAL
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Microsoft SEAL · recommended 2×
  2. PySyft · recommended 2×
  3. MP-SPDZ · recommended 2×
  4. Intel HE-Transformer · recommended 1×
  5. TenSEAL · recommended 1×
  • CATEGORY QUERY
    How can I ensure privacy for AI model inference without compromising performance?
    you: not recommended
    AI recommended (in order):
    1. Microsoft SEAL
    2. Intel HE-Transformer
    3. TenSEAL
    4. PySyft
    5. MP-SPDZ
    6. Sharemind
    7. TensorFlow Federated (TFF)
    8. PyDP
    9. Intel SGX (Software Guard Extensions)
    10. Microsoft Azure Confidential Computing
    11. Graphene-SGX
    12. TensorFlow Lite
    13. PyTorch Mobile
    14. ONNX Runtime

    AI recommended 14 alternatives but never named nesaorg/nesa. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide end-to-end encryption for deploying large language models securely?
    you: not recommended
    AI recommended (in order):
    1. Azure Confidential Computing
    2. Google Cloud Confidential Computing
    3. AWS Nitro Enclaves
    4. Microsoft SEAL
    5. Google's TF Encrypted
    6. IBM HElib
    7. MP-SPDZ
    8. FHE.org's Concrete
    9. HashiCorp Vault
    10. AWS KMS
    11. Azure Key Vault
    12. Google Cloud KMS
    13. OpenSSL
    14. TensorFlow Federated
    15. PySyft

    AI recommended 15 alternatives but never named nesaorg/nesa. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 nesaorg/nesa?
    pass
    AI named nesaorg/nesa explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of nesaorg/nesa. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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nesaorg/nesa — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite