You will get a private AI chatbot trained on your documents, website or knowledge base
Project details
Imagine your team or customers asking complex questions about a 500-page manual and getting instant, accurate answers. That is exactly what a Retrieval-Augmented Generation (RAG) chatbot does—it connects an AI directly to your proprietary documents so it answers strictly based on your actual content.
The biggest issue with standard AI is hallucination—making things up. I build custom RAG systems with strict guardrails to solve this. If the answer isn't in your documents, the bot clearly states it doesn't know. Furthermore, every single response includes a direct citation to the source document, allowing users to instantly verify the facts.
I utilize hybrid search (semantic + keyword) to ensure high retrieval accuracy, and I build robust ingestion pipelines that handle messy, real-world documents. Before handing over the project, I thoroughly test the system against 20+ real questions from your specific domain. You receive a production-ready, fully tested tool that you can trust with your data, whether deployed via a local, cost-free LLM like Ollama or a powerful cloud model like OpenAI.
The biggest issue with standard AI is hallucination—making things up. I build custom RAG systems with strict guardrails to solve this. If the answer isn't in your documents, the bot clearly states it doesn't know. Furthermore, every single response includes a direct citation to the source document, allowing users to instantly verify the facts.
I utilize hybrid search (semantic + keyword) to ensure high retrieval accuracy, and I build robust ingestion pipelines that handle messy, real-world documents. Before handing over the project, I thoroughly test the system against 20+ real questions from your specific domain. You receive a production-ready, fully tested tool that you can trust with your data, whether deployed via a local, cost-free LLM like Ollama or a powerful cloud model like OpenAI.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Gradio, Hugging Face, PyTorch, StreamlitAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$299
|
Standard
$599
|
Advanced
$999
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | |||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | |||
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Optional add-ons
You can add these on the next page.
Notion / Drive / Confluence sync
(+ 2 Days)
+$150
Voice input & output (STT/TTS)
(+ 2 Days)
+$200
Slack or MS Teams integration
(+ 1 Day)
+$150Frequently asked questions
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HK
Huda K.
Jun 29, 2026
Research Survey Distribution Across Arab Countries
"I strongly advise against working with this freelancer. During our project, I discovered that the survey responses I received were generated by automated bots rather than real human participants. The submission intervals were suspiciously consistent (exactly 28 seconds apart), and the influx of responses ceased immediately the moment I added a logic verification question to the form—a clear indicator of scripted automation. When confronted with this technical evidence, the freelancer denied using any automated tools and failed to provide a logical explanation for the activity patterns. I am extremely disappointed by the lack of professional integrity and the attempt to mislead me with fraudulent data. Proceed with caution."
About Anas
DevOps & Full-Stack Engineer | AI Agents, LLMs & Automation
Casablanca, Morocco - 10:16 pm local time
I'm Anas, a software engineer from Morocco specializing in full-stack web development, Linux server administration, and AI automation. I've spent years building and deploying real products, from the first line of code to a live, secured, production-ready environment.
Web Development
I build responsive, fast, and maintainable web applications using React, JavaScript, HTML, CSS, and Bootstrap. I design REST APIs, connect databases, and integrate third-party services, everything needed to go from idea to working product.
Linux Server Administration
I set up and manage Linux servers end to end: Docker containerization, Nginx configuration, SSL/HTTPS, Cloudflare Tunnels, DNS management, firewall rules, and security hardening. I handle what most developers hand off to someone else.
AI Agents & LLM Engineering
I build AI agents that take actions, not just answer questions — browsing the web, calling APIs, managing files, and running multi-step workflows autonomously. I work with LLMs (OpenAI, Claude, LLaMA, Mistral), set up RAG pipelines, fine-tune models for specific use cases, and deploy everything locally or in the cloud. If you need an AI that actually does something useful inside your product or business, this is where I operate.
Automation
I build workflows with n8n that connect services, cut manual work, and run reliably in the background. I combine LLMs, APIs, and custom logic to automate processes that used to require a human, reliably, at scale.
My background in C and C++ gives me a strong foundation in how systems work at a low level, which makes me a better debugger and a sharper engineer overall.
I work with clarity: I understand the scope before I commit, I communicate throughout, and I deliver what was agreed, on time, without surprises. I'm not the cheapest option, but I'm the one who gets it right.
Let's build something solid.
Steps for completing your project
After purchasing the project, send requirements so Anas can start the project.
Delivery time starts when Anas receives requirements from you.
Anas works on your project following the steps below.
Revisions may occur after the delivery date.
Document Intake & Review
I review your uploaded files, assess document quality and formatting, and determine the expected question types to optimize the setup.
Ingestion & Vector Pipeline
I build the document processing pipeline to chunk your files, generate semantic embeddings, and securely load them into the ChromaDB vector database.