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I will build an intelligent ai knowledge base that chats with your data
Hassan Abbas
Informazioni su questo servizio
Stop Hallucinating. Ground Your AI in Private Reality. ️
Generic chatbots fail when they don't know your specific business data. They guess. They hallucinate. If you need an AI that provides accurate, verifiable answers based only on your internal documents, you need a custom RAG (Retrieval-Augmented Generation) ecosystem.
I architect intelligent data pipelines that turn messy PDFs, spreadsheets, and databases into a secure, searchable intelligence layer. No leaks, no guessesjust pure, actionable data retrieval.
️ Technical Stack
- Vector Infrastructure: PostgreSQL (pgvector), Pinecone, Weaviate, or ChromaDB.
- Orchestration: LangChain & LlamaIndex for advanced agentic logic.
- LLM Core: Integration with GPT-4o, Claude 3.5, or local Llama 3 models.
- Pipelines: Python-based ingestion with semantic chunking & re-ranking.
Key Deliverables
- Semantic Search: AI that understands the context of your query, not just keywords.
- Source Citations: Every response includes direct links/references to the source file.
- Privacy First: Optional local deployment (Ollama/vLLM) for total data security.
- Scalable API: A robust backend to power your website, app, or internal tool.
Scopri di più su Hassan Abbas
Hassan Abbas
AI Automation Engineer, LLMs, Voice AI and n8n Expert
- DaPakistan
- Membro daott 2021
- Tempo di risposta medio1 ora
Lingue
Inglese, Francese, Tedesco, Spagnolo
I’m an AI Engineer specializing in building production-grade systems using LLMs, voice AI, and workflow automation. I design end-to-end solutions, including RAG platforms, real-time voice agents (Twilio, Retell), and n8n-based automation. I’ve developed inbound/outbound AI calling systems for lead qualification, CRM integration (Simpro), and intelligent workflows. I focus on scalable architectures, robust APIs, and real-world business automation that delivers measurable results.
Il mio portfolio
FAQ
What exactly is RAG, and why do I need it?
Standard AI models rely on general knowledge and often "guess" when they don't have the answer. RAG allows the AI to "read" your private documents first. It retrieves the exact information from your files before generating an answer, ensuring 100% accuracy grounded in your specific data
Is my sensitive data secure and private?
Data privacy is my top priority. I use encrypted vector databases and secure API protocols. For clients with extreme privacy needs, I can deploy the entire system locally using open-source models (like Llama 3), ensuring your data never leaves your private server or environment.
What types of data or files can the AI process?
The system is highly flexible. I can ingest and vectorize: Documents: PDF, DOCX, TXT, Markdown. Structured Data: CSV, Excel, SQL Databases. Web Content: Live website URLs, Documentation pages, or Notion workspaces.
Will I have to pay for monthly API or Hosting fees?
Yes. You will typically need your own API keys for the LLM (OpenAI/Claude) and the Vector Database (Pinecone/Weaviate). I will help you set these up so you pay the providers directly at cost. If you prefer a "No Monthly Fee" model, we can discuss hosting open-source models on your own hardware.
Can you integrate this AI "Brain" into my existing website?
Absolutely. I provide a scalable Backend API that can be integrated into your custom website, mobile app, or internal tools. I can also use automation platforms like n8n to connect your AI knowledge base to Slack, Discord, or your CRM (HubSpot/Salesforce).
