Kunal Ganglani
Senior Staff Software Engineer
Toronto, Canada
About Me
I'm Kunal — a Senior Staff Engineer who's spent 14 years turning "what if we could..." into production systems serving millions. Right now I'm deep in AI agents, RAG pipelines, and LLM orchestration in Toronto. I've shipped everything from a Walmart chatbot that 4x'd engagement to crypto accounting systems processing 200K transactions in 5 minutes.
When I close my laptop, I'm either hunting for the perfect late-night icecreams (Indore still wins), debating whether Vinland Saga or Attack on Titan has the better character arc, or planning my next trip — 10 countries deep and Japan is next. Born in India's undisputed snacking capital, I believe the best ideas come from mixing things that shouldn't work together.
I'm the kind of person who'll help you debug a model at midnight and then send you a ramen recommendation. If that sounds like someone you'd want to work with — or just grab coffee with — let's talk.
Quick Facts
Expertise
What I've shipped
Concrete artifacts I built end-to-end, with the metric or scope that matters. If you'd want to know "how do they know what they're talking about?" — this is the answer.
- Walmart conversational commerce chatbot 2023
Lead engineer. Production deployment serving North-America retail. Drove 4× engagement lift on the product surface vs prior search-only UX.
- Crypto accounting + tax pipeline 2022
Distributed system processing 200K transactions in 5 minutes. Multi-chain ingestion, cost-basis lots, FIFO/LIFO reconciliation.
- RAG / GraphRAG production pipelines 2024
Built end-to-end RAG systems with hybrid retrieval (BM25 + dense), agentic re-ranking, and observability for hallucination drift.
- AI Hardware Decision Hub (this site) 2026
4 free tools + an open dataset for LLM hardware decisions. 80+ measured benchmarks across Apple Silicon M1–M5, NVIDIA RTX 30/40/50, AMD, A100, H100. Public JSON API, CC BY 4.0.
- kunalganglani.com — AI engineering blog ongoing
150+ in-depth posts on AI engineering, local LLMs, RAG, agent patterns, and the hardware running it all. ~129 clicks/day from organic search at last count.
How I write about hardware + AI
Editorial standards for this site. The reason every number here carries a confidence label and the dataset is open.
- Every number carries a confidence label
- Benchmark tables on this site label each row as ● measured / ◐ interpolated / ○ estimated so you can tell what's a real community measurement vs a bandwidth-derived estimate. No fake precision.
- Data is public, attributed, and openly licensed
- The benchmark dataset (80+ rows) and the API pricing dataset (70+ models) are CC BY 4.0 with JSON endpoints. Original sources (r/LocalLLaMA threads, Ollama discussions, MLX docs, llama.cpp PRs) are credited where applicable.
- Posts have a freshness commitment
- Posts on rapidly-changing topics (model releases, pricing, hardware) carry a "Last reviewed" date and are systematically refreshed when underlying facts change.
- Corrections are surfaced, not buried
- If a number is wrong, tell me ([email protected]) and I'll fix the page + log the correction at the bottom rather than silently editing.
- No undisclosed sponsorships
- No vendor pays for placement on this site. Affiliate links (Amazon, etc.) are disclosed inline where they appear. Lead magnets are first-party PDFs only.
Where the work shows up
Page last reviewed . Found something out-of-date or wrong? Tell me.