REPOGEO REPORT · LITE

openai/privacy-filter

Default branch main · commit f7f00ca7 · scanned 6/21/2026, 1:32:12 AM

GitHub: 2,472 stars · 212 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)

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

AI VISIBILITY SCORE

28 /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

2 / 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 openai/privacy-filter, 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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

pii-detection, data-privacy, text-anonymization, natural-language-processing, machine-learning, on-premises, token-classification, llm, privacy-filter

mediumreadme#2

Strengthen the README's opening to highlight core differentiators

Why:

CURRENT

OpenAI Privacy Filter is a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text. It is intended for high-throughput data sanitization workflows where teams need a model that they can run on-premises that is fast, context-aware, and tunable.

COPY-PASTE FIX

OpenAI Privacy Filter is a powerful, LLM-powered bidirectional token-classification model for personally identifiable information (PII) detection and masking in text. Unlike traditional regex or rule-based systems, it leverages deep semantic understanding for high-throughput data sanitization workflows, ideal for teams needing a fast, context-aware, and tunable on-premises solution.

lowhomepage#3

Add a homepage URL to the repository metadata

Why:

COPY-PASTE FIX

[Insert relevant project or documentation URL here, e.g., https://openai.com/research/privacy-filter]

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 openai/privacy-filter

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

microsoft/presidio

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

microsoft/presidio · recommended 2×
explosion/spaCy · recommended 2×
nltk/nltk · recommended 2×
Private AI · recommended 1×
NIST PII Toolkit · recommended 1×

CATEGORY QUERY
Need a fast, on-premises solution for PII detection and text anonymization.
you: not recommended
AI recommended (in order):
1. Presidio (microsoft/presidio)
2. Private AI
3. NIST PII Toolkit
4. John Snow Labs NLP (JohnSnowLabs/spark-nlp)
5. spaCy (explosion/spaCy)
6. NLTK (nltk/nltk)
AI recommended 6 alternatives but never named openai/privacy-filter. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best lightweight models for PII redaction that run locally?
you: not recommended
AI recommended (in order):
1. Presidio (microsoft/presidio)
2. spaCy (explosion/spaCy)
3. Flair (flairNLP/flair)
4. Hugging Face Transformers (huggingface/transformers)
5. dslim/bert-base-NER
6. Davlan/bert-base-multilingual-cased-ner-hrl
7. DistilBERT
8. NLTK (nltk/nltk)
AI recommended 8 alternatives but never named openai/privacy-filter. 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 openai/privacy-filter?
pass
AI did not name openai/privacy-filter — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts openai/privacy-filter in production, what risks or prerequisites should they evaluate first?
pass
AI named openai/privacy-filter 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 openai/privacy-filter solve, and who is the primary audience?
pass
AI named openai/privacy-filter 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 openai/privacy-filter. 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)

[![RepoGEO](https://repogeo.com/badge/openai/privacy-filter.svg)](https://repogeo.com/en/r/openai/privacy-filter)

HTML

<a href="https://repogeo.com/en/r/openai/privacy-filter"><img src="https://repogeo.com/badge/openai/privacy-filter.svg" alt="RepoGEO" /></a>

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openai/privacy-filter — 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