RRepoGEO

REPOGEO REPORT · LITE

Hawksight-AI/semantica

Default branch main · commit e04dc12e · scanned 6/17/2026, 2:26:26 PM

GitHub: 1,225 stars · 185 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 Hawksight-AI/semantica, 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 H1 to explicitly mention AI Agents

    Why:

    CURRENT
    ### The Context & Accountability Layer for AI Systems
    COPY-PASTE FIX
    ### The Context & Accountability Layer for Auditable & Explainable AI Agents
  • mediumtopics#2
    Add specific topics for provenance, explainability, and governance

    Why:

    CURRENT
    agent-memory, agentic-ai, ai-agents, ai-infrastructure, context-graph, context-management, data-infrastructure, developer-tools, graph-analytics, graph-modeling, graphrag, knowledge-engineering, knowledge-graphs, ontology-engineering, python-library, rag, schema-design, semantic-layer, semantic-web
    COPY-PASTE FIX
    agent-memory, agentic-ai, ai-agents, ai-infrastructure, context-graph, context-management, data-infrastructure, decision-traceability, developer-tools, explainable-ai, governance, graph-analytics, graph-modeling, graphrag, knowledge-engineering, knowledge-graphs, ontology-engineering, provenance, python-library, rag, schema-design, semantic-layer, semantic-web
  • mediumreadme#3
    Add a sentence highlighting the focus on local, open-source models

    Why:

    CURRENT
    Semantica is the Context and Accountability Layer that sits alongside your LLM, vector store, and agent framework. It complements your existing stack, not replaces it, adding structured intelligence, causal reasoning, and a full audit trail to every decision your agents make.
    COPY-PASTE FIX
    Semantica is the Context and Accountability Layer that sits alongside your LLM, vector store, and agent framework. It complements your existing stack, not replaces it, adding structured intelligence, causal reasoning, and a full audit trail to every decision your agents make. Unlike many alternatives, Semantica prioritizes seamless integration with local, open-source models for semantic search and RAG, ensuring full control and transparency.

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 Hawksight-AI/semantica
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
IBM Watson OpenScale
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. IBM Watson OpenScale · recommended 1×
  2. Microsoft Azure Machine Learning · recommended 1×
  3. Google Cloud AI Platform · recommended 1×
  4. H2O.ai Driverless AI · recommended 1×
  5. SeldonIO/alibi · recommended 1×
  • CATEGORY QUERY
    How to build auditable and explainable AI systems with decision traceability and governance?
    you: not recommended
    AI recommended (in order):
    1. IBM Watson OpenScale
    2. Microsoft Azure Machine Learning
    3. Google Cloud AI Platform
    4. H2O.ai Driverless AI
    5. Alibi Explain (SeldonIO/alibi)
    6. Aequitas (dssg/aequitas)
    7. Fiddler AI

    AI recommended 7 alternatives but never named Hawksight-AI/semantica. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for managing context and knowledge graphs for AI agent provenance?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. Grakn (now Vaticle's TypeDB)
    3. Ontotext GraphDB
    4. Amazon Neptune
    5. ArangoDB
    6. Stardog

    AI recommended 6 alternatives but never named Hawksight-AI/semantica. 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 Hawksight-AI/semantica?
    pass
    AI named Hawksight-AI/semantica explicitly

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

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

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

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