Aashir co-founded Afnexis after watching too many AI projects die between proof of concept and production. The demo works. The data pipeline breaks. The model drifts. Nobody told the client it would cost three times as much to actually ship.
He built Afnexis around one constraint: everything ships to production. No demos that sit in staging. No models that get rebuilt when real data shows up. The team has delivered 50+ projects for 30+ clients across healthcare, fintech, and real estate. The work spans RAG pipelines, credit scoring engines, document AI for health records, and computer vision systems for property inspection.
His technical focus is on the parts most teams skip. Data quality before model selection. Monitoring before launch. Compliance architecture on day one, not as a post-launch fix. My Medical Records AI, a health records platform, is one of the products built that way.
He writes about what actually works in production AI. Not theory. Not benchmarks. What it takes to go from a working notebook to a system your clients trust with real data.
Areas of Expertise
- RAG pipeline architecture
- LLM integration (OpenAI, Claude, Llama)
- Healthcare AI systems
- Production ML and MLOps
- Computer vision (YOLOv8, OpenCV)
- AI agent development
- LangChain and LangGraph
- Vector databases (Pinecone, pgvector)
Published Articles
- Why AI Projects Fail (And How to Fix It)
- How to Build a RAG System That Actually Works
- AI Development Cost in 2026: Real Numbers
- Fine-Tuning vs RAG: Which Should You Use?
- How to Choose an AI Development Company
- AI Agent Frameworks Compared: 2026
- Vector Databases Compared: Pinecone, Weaviate, pgvector
- The Agentic AI Revolution
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