REPOGEO REPORT · LITE

zama-ai/concrete-ml

Default branch main · commit e56714c7 · scanned 6/27/2026, 2:17:02 PM

GitHub: 1,437 stars · 199 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

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 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

highhomepage#1
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://docs.zama.ai/concrete-ml
```

highreadme#2

Strengthen README's opening to emphasize 'private ML model deployment'

Why:

CURRENT

Concrete ML is a Privacy-Preserving Machine Learning (PPML) open-source set of tools built on top of Concrete by Zama. It simplifies the use of fully homomorphic encryption (FHE) for data scientists so that they can automatically turn machine learning models into their homomorphic equivalents, and use them without knowledge of cryptography.

COPY-PASTE FIX

**Concrete ML** is a Privacy-Preserving Machine Learning (PPML) framework that empowers data scientists to deploy and run machine learning models directly on encrypted data, ensuring robust privacy for predictions. Built on top of Concrete by Zama, it simplifies the use of Fully Homomorphic Encryption (FHE) by automatically transforming traditional ML models into their homomorphic equivalents, eliminating the need for cryptographic expertise.

mediumreadme#3

Clarify the repository's license in the README

Why:

COPY-PASTE FIX

This project is licensed under [insert specific license name(s) here, e.g., 'the Zama Research License and Apache 2.0 License']. Please see the [LICENSE](LICENSE) file for full 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

0 / 2

0% of queries surface zama-ai/concrete-ml

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Microsoft SEAL

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Microsoft SEAL · recommended 1×
OpenFHE · recommended 1×
IBM HElib · recommended 1×
TensorFlow Federated (TFF) · recommended 1×
PySyft (OpenMined) · recommended 1×

CATEGORY QUERY
How can I deploy machine learning models while ensuring data privacy for predictions?
you: not recommended
AI recommended (in order):
1. Microsoft SEAL
2. OpenFHE
3. IBM HElib
4. TensorFlow Federated (TFF)
5. PySyft (OpenMined)
6. Flower
7. TensorFlow Privacy
8. Opacus (PyTorch)
9. Google's Differential Privacy Library
10. MP-SPDZ
11. FHE.org
12. Microsoft Azure Confidential Computing
13. Google Cloud Confidential Computing
14. Intel SGX SDK
AI recommended 14 alternatives but never named zama-ai/concrete-ml. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a Python library to add fully homomorphic encryption to existing ML models.
you: not recommended
AI recommended (in order):
1. TenSEAL
2. PySyft
3. Concrete ML
4. HEuReka
5. PySEAL
AI recommended 5 alternatives but never named zama-ai/concrete-ml. 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 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?

Embed your GEO score

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/zama-ai/concrete-ml.svg)](https://repogeo.com/en/r/zama-ai/concrete-ml)

HTML

<a href="https://repogeo.com/en/r/zama-ai/concrete-ml"><img src="https://repogeo.com/badge/zama-ai/concrete-ml.svg" alt="RepoGEO" /></a>

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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