category

AI Projects

Complete AI products — from problem framing to deployment and adoption.

Model Training & Fine-tuningAI Projects
research

Tool-Selection LLM — LoRA Fine-tune on xLAM

Teaching a small LLM to pick the right tool/API for an agent — and say how confident it is. LoRA-tuned Qwen3.5-2B with Unsloth on xLAM function-calling data: 50.6% → 100% accuracy, ECE 0.220 → 0.002.

50.6% → 100%
tool-selection accuracy
0.220 → 0.002
ECE (calibration)
UnslothQwen3.5-2BLoRA / PEFTPyTorchHugging Face Datasets+1
Architecture & SystemsRAG & Retrieval

Agentic AI Chatbot Platform

Production multi-agent assistant for cybersecurity & case management — a router agent dispatching to five specialist agents across hybrid RAG, text-to-SQL and action execution, on self-hosted vLLM.

400+
enterprise clients
200
concurrent users
LangGraphvLLMSGLangChromaDBRedis+5
LLM Security & Red-teamingAI Projects

RedForge — AI Red-teaming & GenAI Eval Platform

Target-agnostic platform that attacks any LLM chatbot with 200+ OWASP-LLM-Top-10 test cases and an adversarial mutation engine. Built on personal time, adopted as a company product.

200+
attack cases mapped to OWASP LLM Top 10
10
evasion / mutation techniques
PythonPlaywrightLLM-as-JudgeFastAPIYAML configs+1
RAG & RetrievalAI Projects

Self-learning Few-shot Text-to-SQL

A retrieval-driven few-shot engine that harvests successful queries as exemplars and injects them dynamically — lifting text-to-SQL accuracy from 60% to 95% with no retraining.

60% → 95%
text-to-SQL accuracy
$0
fine-tuning cost
RedisEmbeddingsvLLMLangGraphPostgreSQL
AI ProjectsArchitecture & Systems

Abuse Reporting Automation Platform

End-to-end automation of trademark and abuse complaints across multiple platforms — FastAPI services, async workers and resilient browser automation.

multi-platform
complaint submission
0
manual submissions for analysts
FastAPIPlaywrightSeleniumPostgreSQLAzure Blob+3
Model Training & Fine-tuningAI Projects

Predictive Forecasting for a Banking Client

Forecasting models and automated reconciliation for a leading Australian bank — better accuracy than baseline and 40% less manual effort.

40%
less manual effort
↑
forecast accuracy vs baseline
PythonSQLVBAPower BITableau