Request analysis #1
🧠 AI analysis
Your request: Automation of sending email responses to inquiries about products.
AI summary: Automation of sending email responses to product inquiries
Type: automation
Complexity: medium
Confidence: 85%
🤖 Proposed solution
The system recommends an AI agent–based workflow.
🔍 Experts with similar projects
From our candidate database — ranking starts with project topic fit, not profile popularity.
🌍 Local experts from poland
We did not find local projects strictly related to your request topic. Below are local experts recommended for the strongest public experience (followers, stars, activity).
👤 Expert profile
📦 the-book-of-secret-knowledgeRecommended — most experience
About the project: A collection of inspiring lists, manuals, cheatsheets, blogs, hacks, one-liners, cli/web tools and more.
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🧠 We do not have a local delivery that directly matches your topic — this profile is recommended for the strongest public experience, not because the project description matches your request.
👤 Expert profile
📦 PublishRecommended — most experience
About the project: A static site generator for Swift developers
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🧠 We do not have a local delivery that directly matches your topic — this profile is recommended for the strongest public experience, not because the project description matches your request.
👤 Expert profile
📦 pe-sieveRecommended — most experience
About the project: Scans a given process. Recognizes and dumps a variety of potentially malicious implants (replaced/injected PEs, shellcodes, hooks, in-memory patches).
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🧠 We do not have a local delivery that directly matches your topic — this profile is recommended for the strongest public experience, not because the project description matches your request.
👨💻 Experts matched to your request
👤 Expert profile
📦 ComplaintBotRelated topic
About the project: AI-powered customer support assistant designed to handle food delivery complaints efficiently. Built using LangChain, RAG (Retrieval-Augmented Generation), PostgreSQL, and email automation, the chatbot provides…
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🧠 Project “ComplaintBot” is thematically related to your request. Topic match: 32%.
👤 Expert profile
📦 X-CustomerSupport-ChatbotRelated topic
About the project: X-Customer Support Chatbot is an NLP-powered AI tool built with LangChain, FAISS, and OpenAI’s GPT models. It efficiently retrieves context-aware responses to customer queries for Twitter/X support. The chatbot is…
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🧠 Project “X-CustomerSupport-Chatbot” is thematically related to your request. Topic match: 26%.
👤 Expert profile
📦 AI-Voice-and-LLM-Powered-HelpDesk-PlatformRelated topic
About the project: Developed a robust ticket management system enabling users to create, update, and search tickets, enhancing organization and efficiency. Integrated an AI-powered chatbot using Langchain (LLM) and Python for automated…
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🧠 Project “AI-Voice-and-LLM-Powered-HelpDesk-Platform” is thematically related to your request. Topic match: 25%.
🏆 Experts on MakerAI
Matched by portfolio deliverables on MakerAI (projects and profile).
🧠 Portfolio item "Grzegorz" is related to your request. Topic match: 31%.
Why does the match percentage look “low”?
We compare your request topic to portfolio project descriptions (a technical AI scale, not “100% = turnkey product”).
- A typical “related” score is around 20–30% — that is often the best hit in our database, not a system error.
- Green label “Delivered a similar project” = the repo/portfolio description contains concrete words from your request (e.g. chatbot, store) — our experience signal, not AI similarity alone.
- External links (profile, project) unlock after you sign in as a registered client — until then you only see the project name and match details in MakerAI.
Experts are suggested automatically from your problem description, technologies, and public projects.
The percentage on each card is topic match — higher on the list means closer to your request versus other candidates.
Sign in as a client to open profile and project links from cards; until then, use match labels and the in-app expert summary before sending an inquiry.
