REPOGEO REPORT · LITE

lablup/backend.ai

Default branch main · commit 143b84a9 · scanned 6/4/2026, 5:32:08 AM

GitHub: 643 stars · 176 forks

AI VISIBILITY SCORE

33 /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

2 / 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 lablup/backend.ai, 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 the README's opening paragraph to emphasize AI/ML specialization

Why:

CURRENT

Backend.AI is a streamlined, container-based computing cluster platform that hosts popular computing/ML frameworks and diverse programming languages, with pluggable heterogeneous accelerator support including CUDA GPU, ROCm GPU, Rebellions, FuriosaAI, HyperAccel, Intel Gaudi, Tenstorrent, Google TPU, Graphcore IPU and other NPUs.

COPY-PASTE FIX

Backend.AI is a specialized, multi-tenant computing cluster platform designed for AI/ML workloads, offering streamlined container-based execution across diverse programming languages and pluggable heterogeneous accelerators including CUDA GPU, ROCm GPU, Gaudi NPU, Google TPU, GraphCore IPU, and more.

mediumtopics#2

Expand the repository's topics list with AI/ML-specific keywords

Why:

CURRENT

api, backendai, cloud-computing, containers, distributed-computing, docker, documentation, hpc, monitoring, paas, python

COPY-PASTE FIX

api, backendai, cloud-computing, containers, distributed-computing, docker, documentation, hpc, monitoring, paas, python, machine-learning, deep-learning, gpu-computing, npu, ai-platform, mlops

lowreadme#3

Add a 'Comparison to Alternatives' section in the README

Why:

COPY-PASTE FIX

Add a new section titled '## Comparison to Alternatives' or '## Why Backend.AI?' that briefly explains how Backend.AI differs from general-purpose orchestrators like Kubernetes or specific ML platforms like Kubeflow, focusing on its strengths in fine-grained resource management for heterogeneous AI/ML accelerators and multi-tenancy.

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 lablup/backend.ai

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Kubernetes

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Kubernetes · recommended 2×
OpenShift · recommended 2×
Open Data Hub · recommended 2×
Kubeflow · recommended 1×
NVIDIA AI Enterprise · recommended 1×

CATEGORY QUERY
What platform manages containerized AI/ML workloads across heterogeneous accelerators and diverse languages?
you: not recommended
AI recommended (in order):
1. Kubernetes
2. Kubeflow
3. OpenShift
4. Open Data Hub
5. NVIDIA AI Enterprise
6. Google Cloud Vertex AI
7. Azure Machine Learning
8. Amazon SageMaker
AI recommended 8 alternatives but never named lablup/backend.ai. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to set up a multi-tenant computing cluster for distributed jobs with GPU/NPU support?
you: not recommended
AI recommended (in order):
1. Kubernetes
2. NVIDIA GPU Operator
3. KubeFlow
4. OpenShift
5. Open Data Hub
6. HPE GreenLake for ML Operations
7. Determined AI
8. Slurm
9. Singularity/Apptainer
10. Docker
11. Ray
12. KubeRay
13. Apache Mesos
14. Marathon
15. Aurora
AI recommended 15 alternatives but never named lablup/backend.ai. 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 lablup/backend.ai?
pass
AI named lablup/backend.ai explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts lablup/backend.ai in production, what risks or prerequisites should they evaluate first?
pass
AI named lablup/backend.ai 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 lablup/backend.ai solve, and who is the primary audience?
pass
AI did not name lablup/backend.ai — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of lablup/backend.ai. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/lablup/backend.ai.svg)](https://repogeo.com/en/r/lablup/backend.ai)

HTML

<a href="https://repogeo.com/en/r/lablup/backend.ai"><img src="https://repogeo.com/badge/lablup/backend.ai.svg" alt="RepoGEO" /></a>

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