RRepoGEO

REPOGEO REPORT · LITE

zama-ai/concrete

Default branch main · commit ecb729bb · scanned 5/10/2026, 7:51:41 PM

GitHub: 1,552 stars · 210 forks

AI VISIBILITY SCORE
80 /100
Healthy
Category recall
2 / 2
Avg rank #3.5 when recommended
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 zama-ai/concrete, 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
  • highhomepage#1
    Add homepage URL to repository settings

    Why:

    COPY-PASTE FIX
    https://docs.zama.ai/concrete
  • highreadme#2
    Clarify the project's license(s) in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under the terms specified in the [LICENSE.txt](LICENSE.txt) file. Please refer to the file for full details.
  • mediumreadme#3
    Add a 'Core Differentiator' section to the README

    Why:

    COPY-PASTE FIX
    ## Core Differentiator
Concrete stands out as an FHE compiler that automatically transforms standard Python code into Fully Homomorphic Encryption (FHE) circuits, specifically leveraging TFHE. This approach significantly simplifies FHE application development by abstracting away low-level cryptographic complexities, making it accessible to developers without deep cryptography expertise.

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
2 / 2
100% of queries surface zama-ai/concrete
Avg rank
#3.5
Lower is better. #1 = top recommendation.
Share of voice
15%
Of all named tools, what % are you?
Top rival
PALISADE
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PALISADE · recommended 2×
  2. Lattigo · recommended 2×
  3. Pyfhel · recommended 1×
  4. TenSEAL · recommended 1×
  5. HEuReka · recommended 1×
  • CATEGORY QUERY
    How can I implement fully homomorphic encryption for Python applications securely?
    you: #5
    AI recommended (in order):
    1. Pyfhel
    2. TenSEAL
    3. HEuReka
    4. PALISADE
    5. Concrete ← you
    6. Lattigo
    Show full AI answer
  • CATEGORY QUERY
    What tools simplify fully homomorphic encryption development without deep cryptography knowledge?
    you: #2
    AI recommended (in order):
    1. Concrete ML
    2. Concrete ← you
    3. OpenFHE
    4. PALISADE
    5. Microsoft SEAL
    6. Lattigo
    7. HElib
    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 zama-ai/concrete?
    pass
    AI named zama-ai/concrete explicitly

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

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

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

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zama-ai/concrete — 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