Hyderabad has quietly become one of India's strongest product-engineering cities, and the work I see from Telangana reflects that — teams that have already shipped something and want the next version built properly, rather than a first MVP.
I have shipped over 70 projects, mostly for Indian founders and small product teams, plus clients in the UK and US. Everything is built and deployed by me directly — no team, nothing subcontracted. I am based in Bengaluru and work remotely across India.
Same timezone as Bengaluru and a similar working culture, which makes collaboration straightforward. Hyderabad teams often have in-house engineers, so my role is more often a specific capability — AI integration, a mobile app, a performance rescue — than the whole build.
Multi-tenant SaaS platforms with subscriptions, auth, and admin panels — production-ready from day one.
Custom AI agents, OpenAI integrations, and workflow automation built directly into your product.
High-performance iOS & Android apps built with React Native — standalone or alongside your SaaS.
Next.js listing platform for pre-launch and under-construction Bangalore developments — unit-level inventory with per-configuration pricing and availability, and budget filtering that reasons across configurations rather than projects.
Next.js site for a curated luxury property advisory — a single search across property, location, city and builder, and a schema model that describes a consultative service rather than a listings catalogue.
WordPress build for a luxury real-estate services company across four cities — Elementor Pro templates over a JetEngine content model, JetSearch property search, and a handover the client's own team could operate.
Hyderabad teams have usually shipped something already and want a specific capability added rather than a full build. Most often that is AI work — retrieval over their own data, an agent that completes a real task — bolted onto a product with existing users.
Adding AI to a live product is a different problem from a prototype. It needs guardrails, logging, a fallback when the model is wrong, and a cost model that survives scale. I scope those as first-class work rather than as polish.
An AI capability added to an existing product runs ₹5–12 lakh depending on how many systems the agent touches. Token cost is usually the smaller half; the retrieval infrastructure and the handling of wrong answers is the rest.
There is no team behind me and nothing is subcontracted. The person you scope the project with is the person writing the code, which removes an entire category of translation loss. The trade is capacity — I take a small number of projects at a time, and I will tell you if I cannot start when you need to.
The repository lives under your account and the cloud infrastructure is provisioned in yours. You should never be in a position where changing developer means losing your product. Handover documentation is part of the work rather than something negotiated at the end.
A deposit to start, then payments tied to milestones you can see working in a staging environment. Never the full amount upfront in either direction. If a project stops early, you keep everything built to that point.
Every project gets a written scope split into launch and later, with an explicit not-included list. That second list prevents most disputes, because disagreement almost always comes from an assumption nobody wrote down.
Yes, and it is common with Hyderabad companies. It works best when my scope is a clearly bounded piece — a service, an app, an integration — rather than shared ownership of the same codebase.
That is most of what I do now. Adding retrieval, agents or LLM features to a product that already has users is a different problem from building a demo, and it is the one I find most interesting.
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