REPOGEO REPORT · LITE

NVIDIA-AI-Blueprints/aiq

Default branch develop · commit 9f573a25 · scanned 5/30/2026, 4:21:18 AM

GitHub: 689 stars · 193 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 NVIDIA-AI-Blueprints/aiq, 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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

ai-agents, rag, llm, enterprise-ai, nvidia-nemo, langchain, ai-blueprint, reference-architecture, data-analysis, business-intelligence

mediumreadme#2

Reposition the core description in the README's opening

Why:

CURRENT

> **🏆 BENCHMARK NOTE 🏆**
>
> To obtain results consistent with the **nvidia-aiq** DeepResearch Bench leaderboard and DeepResearch Bench II benchmark repository results, please use the `drb1` and `drb2` branches, respectively.

COPY-PASTE FIX

The NVIDIA AI-Q Blueprint is an open reference example for building intelligent AI agents that connect to your enterprise data, reason using state-of-the-art models, and deliver trusted business insights. It serves as an enterprise-grade research agent built on the NVIDIA NeMo Agent Toolkit and uses LangChain Deep Agents, providing both quick, cited answers and in-depth reports.

> **🏆 BENCHMARK NOTE 🏆**
> To obtain results consistent with the **nvidia-aiq** DeepResearch Bench leaderboard and DeepResearch Bench II benchmark repository results, please use the `drb1` and `drb2` branches, respectively.

lowcomparison#3

Add a 'Comparison to other frameworks' section in README

Why:

COPY-PASTE FIX

Add a new section to the README, perhaps titled 'How AI-Q Compares' or 'Relationship to other Frameworks', explaining that AI-Q is an enterprise blueprint built *using* frameworks like LangChain and NeMo Agent Toolkit, rather than a direct competitor to general-purpose LLM orchestration libraries.

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 NVIDIA-AI-Blueprints/aiq

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LangChain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LangChain · recommended 2×
LlamaIndex · recommended 2×
Haystack · recommended 2×
Microsoft Semantic Kernel · recommended 1×
Dataiku · recommended 1×

CATEGORY QUERY
How can I build intelligent AI agents to analyze enterprise data for business insights?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Microsoft Semantic Kernel
4. Haystack
5. Dataiku
6. Databricks Lakehouse Platform
7. MLflow
8. Unity Catalog
9. Google Cloud Vertex AI
AI recommended 9 alternatives but never named NVIDIA-AI-Blueprints/aiq. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What frameworks exist for developing and evaluating AI agents that perform complex reasoning?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Hugging Face Transformers Agents
4. AutoGen
5. DSPy
6. Haystack
7. CrewAI
AI recommended 7 alternatives but never named NVIDIA-AI-Blueprints/aiq. 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 NVIDIA-AI-Blueprints/aiq?
pass
AI named NVIDIA-AI-Blueprints/aiq explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts NVIDIA-AI-Blueprints/aiq in production, what risks or prerequisites should they evaluate first?
pass
AI named NVIDIA-AI-Blueprints/aiq 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 NVIDIA-AI-Blueprints/aiq solve, and who is the primary audience?
pass
AI named NVIDIA-AI-Blueprints/aiq 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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NVIDIA-AI-Blueprints/aiq — 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