I build production systems businesses actually run on, a live ERP, WooCommerce storefronts, an AI marketing agent, structured for how search works in 2026: human readers and AI answer engines at the same time. All shipped, deployed and in daily use.
I'm Raghuram, a full-stack engineer based in India, working remotely with founders and teams worldwide. I don't just write code and hand it over. Before I ever specialised in engineering, I spent years on the other side of the table, doing business development, helping startups find traction, and figuring out what a product actually needs to earn revenue.
That background changes how I build. A feature isn't done when it compiles, it's done when it moves the number the business cares about. I spent five years building custom applications on contract for a company, ERPs, CRMs, WooCommerce platforms and internal tools, then took that experience independent. For the last two years I've worked directly with clients, owning the full build end to end: tools that real operations run on every day, not demos that impress in a pitch and break in production.
Today my focus is the intersection of two things most people treat separately: engineering that's genuinely fast and well-structured, and being discoverable in a web where AI answer engines, not just Google, decide what gets recommended. I build for both, because they share the same foundation.
Over a decade helping startups find product-market fit, open channels and turn early traction into revenue. I understand what a build is actually for.
Five years building bespoke applications on contract for a company, ERPs, CRMs, storefronts and internal tools, then two years working directly with clients, owning each build end to end.
Fast, well-structured builds that are also discoverable by AI answer engines, the newer half of the web most teams haven't adjusted for yet.
Google is still enormous. But a growing share of discovery now happens inside conversational AI tools, ChatGPT, Perplexity, Gemini, Copilot and Google's own AI Overviews, that read the web differently than a search engine from 2015 did. Most businesses, and most agencies selling “SEO packages,” haven't adjusted for either half of this new reality.
None of this replaces good engineering, it adds a new requirement on top of it. A slow, poorly structured site was always a problem. Now it is also invisible to the fastest-growing discovery channel on the web.
Meta titles, a sitemap, a handful of backlinks, that was the job in 2018. Necessary, not sufficient. Most freelancers still pitch it as the whole solution.
Plenty explains what AEO and GEO are. Far fewer have actually shipped llms.txt files, validated them against Google's Agentic Browsing checks, and rebuilt schema to match.
One person fixes Core Web Vitals, another does SEO, a third “does AI stuff.” They rarely talk, and the site ends up optimised for none of it consistently.
Competitors who get their structure right now will be the ones AI engines learn to cite by default. That advantage compounds the longer it goes unaddressed.
Each layer builds on the one before it. Skipping to “AI SEO” without a fast, well-structured foundation doesn't hold up, and a beautifully engineered site invisible to AI discovery leaves real reach on the table. Hover a layer to trace the stack.
Real platforms, built end to end and running live. Expand any system to trace the architecture and my role.
Five things, done properly: build, optimise, get you discovered by AI search, make commerce AI-ready, and build the backend systems a business actually runs on.
I look at the actual site, performance, structure, schema, AI-readability, before proposing anything. Real findings, not a generic checklist.
Findings get mapped into a clear priority order: highest impact first, scoped honestly against what's actually needed.
Engineering happens in the open, staged, tested and communicated as it progresses, not delivered as a surprise at the end.
Performance, schema and AI-readability are tuned together, since they share the same underlying technical foundation.
Search and AI discovery keep moving. The relationship doesn't end at launch, it adjusts as the landscape does.
A polished case study can make any project sound easy. This is the actual debug log from the Cocomera engagement, six real problems, each with the diff that fixed it. Click an entry to expand the trace.
Any one of these is easy to find. The value is in the intersection, one person who has all four, without the handoffs and overhead.
Every case study and claim is verifiable. No fabricated portfolios, no inflated experience. If something is new ground, I say so directly.
No handoffs between a “technical” person and a “strategy” person. The same person who writes the code makes the structural and search decisions.
Findings and decisions are explained in language you can act on, not buried in jargon that exists to sound impressive.
Based in Chennai, India, working flexible hours to overlap with clients across time zones, without the overhead of a full agency.