REPOGEO REPORT · LITE

otwld/ollama-helm

Default branch main · commit 3f1e1a08 · scanned 6/2/2026, 6:32:36 AM

GitHub: 581 stars · 91 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 otwld/ollama-helm, 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 opening to emphasize Helm chart, LLM inference, and GPU support

Why:

CURRENT

Ollama, get up and running with large language models, locally. This Community Chart is for deploying Ollama.

COPY-PASTE FIX

This Community Helm Chart provides a robust and easy way to deploy Ollama, the local large language model inference server, on Kubernetes. It enables users to quickly get up and running with open-source LLMs within their cluster, offering comprehensive and explicit support for GPU acceleration (NVIDIA and AMD ROCm), which is crucial for efficient LLM inference.

mediumabout#2
Enhance repository description with 'LLM inference server'
Why:
CURRENT
```
Helm chart for Ollama on Kubernetes
```
COPY-PASTE FIX
```
Helm chart for deploying Ollama, the local LLM inference server, on Kubernetes.
```
lowtopics#3
Expand topics to include LLM-related keywords
Why:
CURRENT
```
helm, kubernetes, ollama
```
COPY-PASTE FIX
```
helm, kubernetes, ollama, llm, large-language-model, inference
```

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 otwld/ollama-helm

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Kubeflow

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Kubeflow · recommended 1×
NVIDIA Triton Inference Server · recommended 1×
Hugging Face Text Generation Inference (TGI) · recommended 1×
Ray Serve · recommended 1×
OpenLLM · recommended 1×

CATEGORY QUERY
How to deploy a local large language model inference server on Kubernetes?
you: not recommended
AI recommended (in order):
1. Kubeflow
2. NVIDIA Triton Inference Server
3. Hugging Face Text Generation Inference (TGI)
4. Ray Serve
5. OpenLLM
6. BentoML
7. FastAPI
8. Flask
AI recommended 8 alternatives but never named otwld/ollama-helm. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best Helm charts for deploying open-source LLMs on a cluster?
you: not recommended
AI recommended (in order):
1. KubeFlow
2. KServe
3. Hugging Face Text Generation Inference (TGI) Helm Chart
4. OpenLLM Helm Chart
5. BentoML Helm Chart
6. Ray (KubeRay) Helm Chart
AI recommended 6 alternatives but never named otwld/ollama-helm. 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 otwld/ollama-helm?
pass
AI named otwld/ollama-helm explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts otwld/ollama-helm in production, what risks or prerequisites should they evaluate first?
pass
AI named otwld/ollama-helm 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 otwld/ollama-helm solve, and who is the primary audience?
pass
AI named otwld/ollama-helm explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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Live preview

MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/otwld/ollama-helm"><img src="https://repogeo.com/badge/otwld/ollama-helm.svg" alt="RepoGEO" /></a>

Pro

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otwld/ollama-helm — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite