TECHNICAL INSIGHTS

Research Notes & Architecture Deep Dives

Documenting lessons learned from building enterprise multi-agent workflows, fine-tuning computer vision heads, and optimizing retrieval pipelines.

Enterprise RAG 8 Mins Read · 2026

Designing Production-Ready Enterprise RAG with Hybrid Retrieval and RBAC

Why naive vector search fails in corporate knowledge graphs, and how fusing dense semantic embeddings with BM25 sparse keyword indices and Reciprocal Rank Fusion (RRF) delivers bulletproof retrieval while enforcing zero-leakage role-based access control.

Hybrid Search pgvector FastAPI Reranking
Agentic Systems 6 Mins Read · 2026

Autonomous Multi-Agent Collaboration: Orchestrating Research, Writing & Critic Loops

A step-by-step breakdown of implementing a 4-agent collaborative ecosystem using LangChain and Google Gemini. Exploring how dedicated Critic agents auditing intermediate synthesis dramatically improve final report quality and citation fidelity.

LangChain Tavily API Critic Loops Autonomous Agents
Computer Vision 10 Mins Read · 2026

Benchmarking YOLOv11 & YOLOv26 on Medical Mammography Datasets

Empirical insights from training state-of-the-art vision models on the CBIS-DDSM and VinDr-Mammo datasets. Handling high-imbalance class distributions, transfer learning considerations, and optimizing bounding box recall for clinical screening.

PyTorch Ultralytics Medical Imaging Benchmarking
Explainable AI 7 Mins Read · 2026

Interpretable Machine Learning: Leveraging SHAP for High-Stakes Risk Prediction

Achieving 98.19% classification accuracy with XGBoost is only half the battle. How Shapley Additive exPlanations (SHAP) provides global and local feature importance transparency required by healthcare practitioners and stakeholders.

XGBoost SHAP Model Interpretability Scikit-learn
XGBoost SHAP Model Interpretability Scikit-learn