Commerce systems
The platform is rarely the problem. It is where the problem becomes visible, usually as a catalogue, a checkout, or an architecture that has outgrown the decision behind it.
Exploring AI, digital commerce and technology systems that help modern businesses operate, sell and grow.

I build digital commerce systems from the technology layer up: engineering, architecture, performance, customer experience and growth, treated as one system rather than five projects.
Technology is valuable when it creates leverage: better decisions, stronger customer experiences, faster operations and sustainable growth. Not because it is possible, and not because it is new.
Since 2016 · 9+ yearsI don’t sell platforms. I make decisions, about architecture, about catalogue, about where the business actually loses money, and then I build the system that holds those decisions.
They overlap on purpose: a commerce decision is an architecture decision before it is a platform decision, and an AI decision is a process decision before it is a model decision.
The platform is rarely the problem. It is where the problem becomes visible, usually as a catalogue, a checkout, or an architecture that has outgrown the decision behind it.
Frontends over REST and GraphQL, caching that holds under traffic, and infrastructure sized to the order book rather than to the pitch deck.
A beautiful store means nothing if the customer journey is broken. Search, speed and checkout are the growth levers people keep looking past.
Where language models stop being a demo and start being infrastructure: grounded in real data, scoped to real tasks, checked by a person before anything consequential happens.
Six builds, and the decision inside each one. The interesting part of every one of these was the choice made before the code.
01Fashion · D2CA fast-growing fashion brand whose storefront had to keep getting faster while the catalogue and traffic behind it kept moving.
02Horticulture · D2CAn online plant store selling a product that is fragile, seasonal and hard to photograph, to customers who abandon at any friction in the buy.
03Health & fitnessSports nutrition is a category where authenticity is the purchase decision and delivery speed is the repeat one. The storefront has to carry both.
04B2B procurementB2B buying is not a cart. It is a request, a quote, an approval chain and a budget holder, and a consumer checkout models none of that.
05MarketplaceA wide multi-category catalogue, which is the point at which catalogue size stops being a number and starts being an architecture problem.
06ManufacturingAn engineering manufacturer whose buyers research for months and never fill in a form, on a site that search could not read.

A fast-growing fashion brand whose storefront had to keep getting faster while the catalogue and the traffic behind it kept moving.
A decoupled storefront over the commerce API, with caching pushed to the edge of every layer that could hold it.
Architecture direction and delivery: platform decisions, performance strategy and the engineering behind them.
Varnish, Redis, full-page cache and database tuning, the same stack that cut page load times by 60% on high-traffic B2B commerce.
A storefront that scales with the catalogue instead of against it. Detailed metrics on request rather than on a marketing page.
Not a logo grid. An architecture, read left to right, the way a request travels through it.
AI Search changes the discovery layer of commerce. Instead of matching a query to a keyword, the system interprets intent and returns products, categories or guidance based on what the customer is trying to accomplish. The rest of the value is unglamorous: retrieval that returns the right passage, evaluation you can trust, and grounding that stops a fluent answer from being a confident wrong one.
The four definitions that decide most first conversations, written plainly enough to quote.
AI, eCommerce and digital transformation. Practical work across Magento, Shopify, WooCommerce and headless commerce, the APIs behind mobile apps, cloud and performance architecture, AI search and automation.
AI commerce uses artificial intelligence across product discovery, search, recommendations, catalogue enrichment, customer support and purchasing workflows to create more relevant digital shopping experiences.
Headless commerce separates the customer-facing frontend from the commerce backend through APIs, so a business can build a custom experience while keeping the underlying commerce platform.
Generative Engine Optimization focuses on making information clear, authoritative and structurally understandable, so AI-powered search and answer systems can accurately interpret and reference a brand or topic.
Eight shifts, each one forced by a problem the previous way of working could not hold. The technology changed roughly every three years. What it was for did not.
Full experience ↗Six years of formal education in two stages: a three-year diploma in information technology from 2010 to 2013, then a three-year degree from 2013 to 2016. The point at which this stopped being a hobby and started being a discipline.
Started taking my own clients in Vadodara and never stopped. The engagements ran in parallel with the full-time roles below, which is why the dates overlap.
Two years building Magento stores and modules professionally. Magento is the platform that turns a catalogue into an architecture problem, and that reframing shaped everything after it.
Shopify development for global personal-care brands: fully customised storefronts with JavaScript enhancements, third-party tools and custom features.
Nearly three years building scalable, high-performance commerce for leading Saudi Arabian retail groups, across grocery, commercial kitchen equipment and fashion.
B2B procurement is not a cart. It is a request, a quote, an approval chain and a budget holder. I built the systems that model that, and the infrastructure to hold them.
The work stopped being code and became process: architecture guidance, code review and mentorship across a platform estate rather than a single store.
Where the work is now: agents with a deliberate tool boundary, retrieval grounded in a real catalogue, AI search, and automation pointed at the processes that absorb people who should be doing something else.
“Technology should not make business more complicated. It should make growth easier.”
Yuvraj RauljiLong-form technical writing, build notes and platform opinions, published where the conversation already happens.

Ten answers, in the words I would use on the call itself.
AI, eCommerce and digital transformation. The practical work spans commerce platforms, the APIs behind mobile apps, performance and cloud architecture, and AI systems such as agents, retrieval and automation.
LLM applications, agents with a deliberate tool boundary, retrieval augmented generation, MCP-based integrations, AI search, catalogue enrichment and process automation, with evaluation and human review around anything consequential.
Magento 2 and Adobe Commerce, Shopify and Shopify Plus, WooCommerce on WordPress, and headless storefronts built over REST and GraphQL APIs.
Four layers move first: discovery, where search interprets intent instead of matching keywords; merchandising, where recommendations use behaviour and catalogue context; catalogue operations, where enrichment is drafted by a model and reviewed by a person; and support, where grounded answers resolve order and product questions.
AI search interprets intent rather than matching keywords. A full sentence, such as a gift request under a budget for a specific occasion, can return products, categories or guidance, because the query is read as a goal instead of a string.
When the process is repetitive and already documented, the data behind it is reliable, the cost of a mistake is measurable, and a person can review the output. Automating an undefined process only makes the confusion faster.
Shopify is hosted and favours speed of launch and operational simplicity, which suits D2C brands with a focused catalogue. Magento 2 and Adobe Commerce are open architectures that carry complex catalogues, multi-store setups, B2B pricing and approval rules, at the cost of more engineering ownership.
Understand the business, map the process, choose technology against that process, build it, then measure. The technology decision comes fourth, not first.
The constraint you are actually hitting, the architecture or platform decision behind it, what AI should and should not touch in your operation, and the next practical step.
Start a conversation by email at [email protected], or book a 30-minute consultation. Replies usually arrive within 24 hours on IST business days.
Thirty minutes on your constraint, not on my slides. Bring a commerce, architecture or AI problem and we will name the decision behind it, what AI should and should not touch in your operation, and the next practical step.
Have a commerce, AI or technology problem that needs a clearer decision? Describe it in a few lines and I will reply within 24 hours, IST business days.