RRepoGEO

REPOGEO REPORT · LITE

Picovoice/porcupine

Default branch master · commit e8af9a3d · scanned 5/29/2026, 10:33:27 AM

GitHub: 4,837 stars · 577 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 Picovoice/porcupine, 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 highlight embedded/mobile problem-solution

    Why:

    CURRENT
    Porcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening voice-enabled applications.
    COPY-PASTE FIX
    Porcupine is a highly-accurate, lightweight, and **entirely on-device** wake word engine, purpose-built for **reliable, always-listening voice activation in embedded systems and mobile applications**. It empowers developers to build private, efficient, and customizable voice-enabled experiences.
  • mediumreadme#2
    Add a 'Why Porcupine?' or 'Key Differentiators' section

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Why Porcupine?', detailing its advantages like:
*   **On-device processing:** Ensures privacy, low latency, and offline functionality, unlike cloud-dependent solutions.
*   **Custom Wake Words:** Easily train and deploy unique wake words via Picovoice Console, offering flexibility beyond generic hotwords.
*   **Optimized for Embedded & Mobile:** Specifically engineered for resource-constrained environments (e.g., Arm Cortex-M, Raspberry Pi, Android, iOS) for maximum efficiency.
*   **Comprehensive SDKs:** Ready-to-use libraries across many platforms, simplifying integration compared to building from scratch with generic ML frameworks.
  • lowtopics#3
    Add more specific embedded and mobile development topics

    Why:

    CURRENT
    handsfree, hotword, hotword-detection, hotword-detector, keyword-spotter, keyword-spotting, on-device, speech-recognition, trigger-word-detection, voice-activation, wake-word, wake-word-detection, wake-word-engine
    COPY-PASTE FIX
    handsfree, hotword, hotword-detection, hotword-detector, keyword-spotter, keyword-spotting, on-device, speech-recognition, trigger-word-detection, voice-activation, wake-word, wake-word-detection, wake-word-engine, embedded-systems, mobile-development, iot-devices, edge-ai

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 Picovoice/porcupine
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Picovoice Porcupine
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Picovoice Porcupine · recommended 2×
  2. PocketSphinx · recommended 2×
  3. Sensory TrulyHandsfree · recommended 1×
  4. Kneron NPU · recommended 1×
  5. Kneron AI SDK · recommended 1×
  • CATEGORY QUERY
    How to implement reliable, always-listening voice activation on embedded devices?
    you: not recommended
    AI recommended (in order):
    1. Sensory TrulyHandsfree
    2. Kneron NPU
    3. Kneron AI SDK
    4. Picovoice Porcupine
    5. Google Coral Edge TPU
    6. TensorFlow Lite for Microcontrollers
    7. Qualcomm Snapdragon Voice Activation (SVA)
    8. STMicroelectronics STM32Cube.AI
    9. X-CUBE-MEMS1
    10. PocketSphinx
    11. Mycroft Precise

    AI recommended 11 alternatives but never named Picovoice/porcupine. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient libraries for offline hotword detection in a mobile application?
    you: not recommended
    AI recommended (in order):
    1. Picovoice Porcupine
    2. TensorFlow Lite
    3. PocketSphinx
    4. Mozilla DeepSpeech
    5. Vosk

    AI recommended 5 alternatives but never named Picovoice/porcupine. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 Picovoice/porcupine?
    pass
    AI named Picovoice/porcupine explicitly

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

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