REPOGEO REPORT · LITE

lance-format/lance

Default branch main · commit 6ba89d51 · scanned 6/21/2026, 5:21:41 AM

GitHub: 6,693 stars · 729 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

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

Reposition README's initial paragraph to emphasize unique differentiators

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 an open lakehouse format for multimodal AI, designed for 100x faster random access, vector indexing, and data versioning. It allows you to build a complete lakehouse on top of object storage to power your AI workflows, especially for large-scale multimodal datasets.

mediumtopics#2

Add specific topics for vector search, lakehouse, and random access

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, vector-database, vector-search, lakehouse, data-lakehouse, data-versioning, random-access

lowcomparison#3

Add a 'Comparison with Alternatives' section to the README

Why:

COPY-PASTE FIX

## Comparison with Alternatives

Lance offers a unique blend of capabilities, distinguishing itself from both dedicated vector databases and traditional open lakehouse formats.

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

Faiss

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Faiss · recommended 1×
Annoy · recommended 1×
Hnswlib · recommended 1×
Weaviate · recommended 1×
Pinecone · recommended 1×

CATEGORY QUERY
How to achieve faster random access and vector search for large AI datasets?
you: not recommended
AI recommended (in order):
1. Faiss
2. Annoy
3. Hnswlib
4. Weaviate
5. Pinecone
6. ScaNN
AI recommended 6 alternatives but never named lance-format/lance. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking an open lakehouse format with data versioning for machine learning pipelines.
you: not recommended
AI recommended (in order):
1. Delta Lake (delta-io/delta)
2. Apache Iceberg (apache/iceberg)
3. Apache Hudi (apache/hudi)
4. LakeFS (treeverse/lakefs)
5. DVC (iterative/dvc)
AI recommended 5 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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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite