JUNE 2026 – PRESENT
INTELLIGENT CRIME DATABASE CONVERSATIONAL AI
AI-Powered Multilingual RAG Search & Predictive Analytics
01 // PROJECT_SUMMARY
Led AI development by building LLM-powered conversational search, NLP intent classification, Retrieval-Augmented Generation (RAG) pipelines, and predictive analytics for multilingual crime intelligence.
PythonNLTKTableauLLMsRAGFAISSNLP
02 // STRIDE_THREAT_MODELING_LOGS
| THREAT_CATEGORY | EXPLOIT_VECTOR | MITIGATION_STRATEGY |
|---|---|---|
| Prompt Injection (AI Specific) | Malicious users input adversarial prompts to force the LLM to output classified investigation case details. | Implemented LLM Guardrails (input-output validation) and system-level prompt isolation. |
03 // ARCHITECTURAL_SANDBOX_SCHEMAS
FILE_DUMP // DIAGRAM_NODES.LOG
- Data Pipeline: CSV/JSON crime log ingest parsed, cleaned, and normalized with NLTK.
- Embedding Vector Database: Documents chunked and stored in a local FAISS database using text-embedding models.
04 // ARCHITECTURE_LESSONS
- Grounding LLMs strictly to verified penal code datasets prevents legal hallucinations.
05 // TARGET_OUTCOMES
- Optimized crime case search times, transforming keyword-based filtering into semantic conversations.