RRepoGEO

REPOGEO REPORT · LITE

run-llama/sec-insights

Default branch main · commit a9b6da0f · scanned 5/29/2026, 1:48:18 PM

GitHub: 2,600 stars · 691 forks

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 run-llama/sec-insights, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Emphasize 'reference architecture' and 'production-ready example' in the README's opening

    Why:

    CURRENT
    SEC Insights uses the Retrieval Augmented Generation (RAG) capabilities of LlamaIndex to answer questions about SEC 10-K & 10-Q documents.
    COPY-PASTE FIX
    SEC Insights is a full-stack, production-ready reference application demonstrating Retrieval Augmented Generation (RAG) with LlamaIndex. It answers questions about SEC 10-K & 10-Q documents, serving as a robust template for building your own real-world generative AI applications.
  • mediumcomparison#2
    Add a 'Comparison to Alternatives' section to clarify its role versus traditional search engines

    Why:

    COPY-PASTE FIX
    Compared to traditional search engines like Elasticsearch or Solr, SEC Insights offers a complete, full-stack RAG application for semantic Q&A and insight extraction from documents, rather than just keyword search and indexing. It provides the full LLM orchestration, citation, and user interface for a ready-to-deploy solution.

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 run-llama/sec-insights
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Azure Cognitive Search
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Azure Cognitive Search · recommended 2×
  2. Amazon OpenSearch Service · recommended 2×
  3. elastic/elasticsearch · recommended 1×
  4. apache/solr · recommended 1×
  5. opensearch-project/OpenSearch · recommended 1×
  • CATEGORY QUERY
    How to build a production-ready application for querying large document sets?
    you: not recommended
    AI recommended (in order):
    1. Elasticsearch (elastic/elasticsearch)
    2. Apache Solr (apache/solr)
    3. OpenSearch (opensearch-project/OpenSearch)
    4. Azure Cognitive Search
    5. Amazon OpenSearch Service
    6. MongoDB Atlas Search

    AI recommended 6 alternatives but never named run-llama/sec-insights. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good full-stack reference architectures for generative AI applications?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Diffusers
    3. Hugging Face Inference Endpoints
    4. TGI (Text Generation Inference)
    5. Hugging Face Datasets
    6. LangChain
    7. LlamaIndex
    8. Gradio
    9. Streamlit
    10. Pinecone
    11. Weaviate
    12. ChromaDB
    13. Amazon Bedrock
    14. Anthropic Claude
    15. AI21 Labs Jurassic
    16. Amazon Titan
    17. Amazon SageMaker
    18. Amazon EC2
    19. Amazon ECS
    20. Amazon EKS
    21. AWS Step Functions
    22. Amazon S3
    23. Amazon OpenSearch Service
    24. Amazon Aurora
    25. pgvector
    26. AWS Amplify
    27. Next.js
    28. React
    29. Amazon DynamoDB
    30. Amazon RDS
    31. Google Cloud Vertex AI
    32. Gemini
    33. PaLM 2
    34. Google Kubernetes Engine (GKE)
    35. Cloud Run
    36. Google Cloud Workflows
    37. Google Cloud Storage
    38. Google Cloud AlloyDB AI
    39. Google Cloud Vector Search
    40. Firebase
    41. Google Cloud Firestore
    42. Google Cloud SQL
    43. Azure OpenAI Service
    44. GPT-4
    45. DALL-E 3
    46. Azure Machine Learning
    47. Azure Kubernetes Service (AKS)
    48. Azure Container Apps
    49. Azure Logic Apps
    50. Azure Data Factory
    51. Azure Blob Storage
    52. Azure Cognitive Search
    53. Azure Database for PostgreSQL
    54. Azure Static Web Apps
    55. Azure Cosmos DB
    56. Azure SQL Database
    57. FastAPI
    58. PyTorch
    59. TensorFlow
    60. Llama.cpp
    61. vLLM
    62. Triton Inference Server
    63. Apache Airflow
    64. Prefect
    65. MinIO
    66. Qdrant
    67. Milvus
    68. PostgreSQL
    69. MongoDB
    70. Redis
    71. Vercel AI SDK
    72. OpenAI
    73. Anthropic
    74. PlanetScale

    AI recommended 74 alternatives but never named run-llama/sec-insights. 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 run-llama/sec-insights?
    pass
    AI named run-llama/sec-insights explicitly

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

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

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

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