REPOGEO REPORT · LITE

lance-format/lance

Default branch main · commit d8542b53 · scanned 5/11/2026, 2:32:12 AM

GitHub: 6,407 stars · 658 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 lance-format/lance, 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

Refine README's opening to explicitly position Lance as a format, differentiating it from vector databases

Why:

CURRENT

Lance is an open lakehouse format for multimodal AI. It contains a file format, table format, and catalog spec that allows you to build a complete lakehouse on top of object storage to power your AI workflows.

COPY-PASTE FIX

Lance is the open lakehouse *format* for multimodal AI, enabling high-performance vector search, full-text search, and feature engineering directly on object storage. It provides a file format, table format, and catalog spec to build a complete lakehouse, offering a flexible alternative to traditional vector databases and generic lakehouse solutions for your AI workflows.

mediumtopics#2

Add more specific topics related to lakehouse formats and vector capabilities

Why:

CURRENT

apache-arrow, computer-vision, data-analysis, data-analytics, data-centric, data-format, data-science, dataops, deep-learning, duckdb, embeddings, llms, machine-learning, mlops, python, rust

COPY-PASTE FIX

apache-arrow, computer-vision, data-analysis, data-analytics, data-centric, data-format, data-science, dataops, deep-learning, duckdb, embeddings, llms, machine-learning, mlops, python, rust, lakehouse, data-lakehouse, vector-index, vector-search, multimodal-ai

lowreadme#3

Add a 'Comparison to Alternatives' section in the README

Why:

COPY-PASTE FIX

Add a new top-level section to the README, e.g., `## Comparison to Alternatives`, that outlines how Lance differs from formats like Parquet/ORC and dedicated vector databases like Milvus or Pinecone, focusing on its integrated random access, versioning, and vector indexing capabilities.

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 lance-format/lance

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Milvus

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Milvus · recommended 2×
Pinecone · recommended 2×
Faiss · recommended 2×
Apache Iceberg · recommended 1×
Zilliz · recommended 1×

CATEGORY QUERY
What open lakehouse format offers high-performance vector search for multimodal AI data?
you: not recommended
AI recommended (in order):
1. Apache Iceberg
2. Milvus
3. Zilliz
4. Pinecone
5. Faiss
6. Hnswlib
7. Delta Lake
8. Databricks Vector Search
9. Apache Hudi
10. ClickHouse
11. PostgreSQL
12. pgvector
AI recommended 12 alternatives but never named lance-format/lance. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to improve random access and vector indexing for large AI datasets currently in Parquet?
you: not recommended
AI recommended (in order):
1. LanceDB
2. Apache Arrow
3. Faiss
4. ChromaDB
5. Weaviate
6. Milvus
7. Pinecone
AI recommended 7 alternatives but never named lance-format/lance. 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 lance-format/lance?
pass
AI named lance-format/lance explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts lance-format/lance in production, what risks or prerequisites should they evaluate first?
pass
AI named lance-format/lance 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 lance-format/lance solve, and who is the primary audience?
pass
AI named lance-format/lance explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite