Description: Fully local RAG application: PDF ingestion → 500-token semantic chunking → 73-chunk ChromaDB vector store → LangChain retrieval chain with source citations and multi-turn memory. Streamlit UI, no external API dependency.
I've built a new mobile AI assistant that brings chat, text and image generation, voice input and document analysis into one app.
It have Smart Chat, Text Creator, Image Create, Voice Input, plus PDF Scanner, Photo Analyze, Social Content and Prompt Ideas, filtered by category.
Almost everything AI can do, just in one place.
This project focused on building and testing a practical AI security assessment lab for evaluating LLM defenses against prompt injection and jailbreak attacks.
I integrated Spikee by Reversec with a locally hosted cybersecurity model running through LM Studio, then added NVIDIA NeMo Guardrails to compare model behavior under three conditions: no guardrails, input filtering, and combined input/output protection.
The work included configuring the local model environment, building a custom FastAPI gateway, integrating NeMo Guardrails, troubleshooting model latency and timeout issues, creating a reusable Spikee target, and analyzing attack results using Spikee’s built-in reporting tools.
The project also explored different adversarial testing approaches, including prompt injection datasets, obfuscation, encoded attacks, Best-of-N testing, synthetic canary leakage tests, and structured benchmark comparisons.
The objective was to measure how much the guardrails reduced successful attacks while keeping the model, dataset, and testing conditions consistent.
This project demonstrates a hands-on approach to LLM red teaming, AI safety testing, prompt-injection assessment, and guardrail validation for organizations deploying generative AI systems.
A visitor was qualified before sales even saw the chat.
Two quick questions in, the bot already knew this wasn't a browser it was a buyer. The chat routed straight to sales with the context attached, no waiting for someone to notice the lead.
→ Qualifying shouldn't wait for a human to have time to ask the first question.