REPOGEO REPORT · LITE

Conchylicultor/DeepQA

Default branch master · commit 886ec77a · scanned 6/22/2026, 7:17:43 PM

GitHub: 2,912 stars · 1,157 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

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 Conchylicultor/DeepQA, 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 H1 and opening paragraph for clarity

Why:

CURRENT

# Deep Q&A

COPY-PASTE FIX

# DeepQA: A TensorFlow Implementation of "A Neural Conversational Model" (Google Chatbot)

This repository provides a research-oriented implementation of the "A Neural Conversational Model" paper, often referred to as the Google chatbot. It uses a seq2seq RNN model built with Python and TensorFlow to reproduce the paper's results for conversational AI.

mediumhomepage#2

Add a homepage URL to the repository metadata

Why:

COPY-PASTE FIX

[Insert relevant URL, e.g., a GitHub Pages site, project website, or even a direct link to the README if no other site exists]

lowreadme#3

Add a "Comparison to other tools" section in the README

Why:

COPY-PASTE FIX

Add a new section to the README, for example, under the "Presentation" section, titled "Comparison to other tools". This section should briefly explain how DeepQA differs from general-purpose NLP libraries (like Hugging Face Transformers) or full conversational AI frameworks (like Rasa) by focusing specifically on reproducing and exploring the "A Neural Conversational Model" paper.

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 Conchylicultor/DeepQA

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 2×
RasaHQ/rasa · recommended 1×
BERT · recommended 1×
GPT-2 · recommended 1×
DistilBERT · recommended 1×

CATEGORY QUERY
How to build a conversational AI agent using Python and deep learning?
you: not recommended
AI recommended (in order):
1. Rasa Open Source (RasaHQ/rasa)
2. Hugging Face Transformers (huggingface/transformers)
3. BERT
4. GPT-2
5. DistilBERT
6. T5
7. BART
8. Flask (pallets/flask)
9. FastAPI (tiangolo/fastapi)
10. DeepPavlov (deepmipt/DeepPavlov)
11. PyTorch (pytorch/pytorch)
12. TensorFlow (tensorflow/tensorflow)
13. NLTK (nltk/nltk)
14. spaCy (explosion/spaCy)
AI recommended 14 alternatives but never named Conchylicultor/DeepQA. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a TensorFlow-based seq2seq model for developing a neural chatbot.
you: not recommended
AI recommended (in order):
1. TensorFlow's Official Seq2Seq Tutorial/Examples
2. TensorFlow Addons (tensorflow/addons)
3. Keras-Applications (keras-team/keras-applications)
4. Hugging Face Transformers (huggingface/transformers)
5. TensorFlow Text (tensorflow/text)
6. OpenNMT-tf (OpenNMT/OpenNMT-tf)
AI recommended 6 alternatives but never named Conchylicultor/DeepQA. 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 Conchylicultor/DeepQA?
pass
AI named Conchylicultor/DeepQA explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Conchylicultor/DeepQA in production, what risks or prerequisites should they evaluate first?
pass
AI named Conchylicultor/DeepQA 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 Conchylicultor/DeepQA solve, and who is the primary audience?
pass
AI named Conchylicultor/DeepQA 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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MARKDOWN (README)

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HTML

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

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Conchylicultor/DeepQA — 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