RRepoGEO

REPOGEO REPORT · LITE

zama-ai/concrete-ml

Default branch main · commit e56714c7 · scanned 5/16/2026, 4:41:28 PM

GitHub: 1,428 stars · 198 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
63 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 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-ml, 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 directly address ML on encrypted data

    Why:

    CURRENT
    Concrete ML is a Privacy-Preserving Machine Learning (PPML) open-source set of tools built on top of Concrete by Zama.
    COPY-PASTE FIX
    Concrete ML is a Privacy-Preserving Machine Learning (PPML) framework that allows data scientists to perform predictions and training on encrypted data using Fully Homomorphic Encryption (FHE), built on top of Concrete by Zama.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://docs.zama.ai/concrete-ml
  • lowlicense#3
    Clarify the project's license(s) in the README

    Why:

    COPY-PASTE FIX
    Concrete ML is licensed under [License Name(s)]. See the [LICENSE](LICENSE) file for details.

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
1 / 2
50% of queries surface zama-ai/concrete-ml
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
Microsoft SEAL
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Microsoft SEAL · recommended 1×
  2. TenSEAL · recommended 1×
  3. OpenFHE · recommended 1×
  4. TFHE · recommended 1×
  5. PySyft · recommended 1×
  • CATEGORY QUERY
    How can I perform machine learning predictions on encrypted data without decrypting it?
    you: not recommended
    AI recommended (in order):
    1. Microsoft SEAL
    2. TenSEAL
    3. OpenFHE
    4. TFHE
    5. PySyft
    6. FATE
    7. Conclave

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

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries allow building privacy-preserving ML models using homomorphic encryption easily?
    you: #3
    AI recommended (in order):
    1. TenSEAL (OpenMined/TenSEAL)
    2. PySyft (OpenMined/PySyft)
    3. Concrete ML (zama-ai/concrete-ml) ← you
    4. HEuReka (IBM/HEuReka)
    5. PySEAL (OpenMined/PySEAL)
    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-ml?
    pass
    AI named zama-ai/concrete-ml 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-ml in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zama-ai/concrete-ml 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-ml solve, and who is the primary audience?
    pass
    AI named zama-ai/concrete-ml 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-ml — 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