AI Systems Builder · Bangalore, India
I build and ship production AI systems, end-to-end.
Eighteen years as an operator and founder; the last two building production AI SaaS solo — retrieval, LLM orchestration, search, data, billing, and deployment. I don't stop at prototypes — I ship systems that run under load, and I can explain every decision in them.
Flagship — live in production
Vidhi
A domain-specific AI research & drafting platform — RAG over a very large, citation-heavy corpus. Soft-launched with real users and live subscription billing. Sole architect and engineer.
Production RAG platform
Fast, source-cited answers over 750,000+ document chunks — with the full product built around the model, not just an API wrapper.
The platform — five more, one codebase
Built solo on a shared multi-tenant core
Five additional production-grade AI SaaS products run on one shared Elixir platform, each a thin shell over hardened infrastructure. They're pre-launch, so the products stay dark — but the engineering doesn't. These are the primitives, described without the market. Full walkthroughs under NDA.
Deterministic classification & scoring engine
Rule-based, with a full auditable decision trail — chosen over an LLM where traceability and defensibility matter more than flexibility.
Multi-stage generative-media pipeline
Brief → storyboard → asset generation → voiceover → render, orchestrated through resilient background workers across several media providers.
Entity & relationship knowledge graph
A graph store layered over Postgres for entity-and-relationship intelligence, kept in sync from the primary system of record.
Resilient ingestion & extraction
Browser-automation ingestion for dynamic, JavaScript-heavy sources, plus multi-format document extraction/OCR and normalization.
Shared multi-tenant core
Unified auth, billing, RAG, chat, search, and content — so each new product ships as a thin shell, not a rebuild. Improvements compound across every app.
Architecture walkthroughs under NDA
Under mutual NDA I walk my own proprietary systems — real screenshots, workflows, architecture. I never use client work as public proof.
Where I can help
Engagements I take on
Fixed-scope pilots or weekly retainers, not open-ended hourly. I lower the risk of a narrow, high-value outcome.
Production RAG & document intelligence
Reliable retrieval, source citations, evaluation harnesses, anti-hallucination controls — audits through full builds.
AI feature integration for existing SaaS
Add an AI capability without breaking your auth, data, search, or UX. I build the whole system around the model.
Elixir / Phoenix build & rescue
Backend, LiveView, Ash, Postgres, OTP, deployment — for teams on the stack or moving to it.
Fractional technical partner / CTO
Architecture, delivery, and product judgment for founders who need someone who has been the PM, engineer, and operator.
Fixed-scope starters — low commitment, fast proof
RAG / retrieval-quality audit
A scored review of your retrieval, chunking, citations, cost, and failure modes — with a concrete fix roadmap. Fixed fee, days not weeks.
AI-generated code review & hardening
If your MVP was built fast with AI assistance, I review it for correctness, security, maintainability, and production readiness — and harden what needs it.
LLM API / AI-feature integration sprint
One reliable AI feature into your existing product — retrieval, structured outputs, or tool-calling — wired into your data, auth, and UX.
AI SaaS launch-readiness audit
A pre-launch pass over your product — reliability, security, cost, and data — closing the gap between “code-complete” and “ready for real users.”
How I work
Judgment first, tools second
My edge isn't typing speed. It's taking an ambiguous product problem, designing the system, building the full stack, integrating AI responsibly, deploying it, measuring cost and usage, and iterating — the whole arc, owned by one person who has also carried a P&L.
I use AI-assisted development as leverage, with human ownership of architecture, review, testing, and production quality. The AI writes code; I own whether it's correct, fast, secure, and maintainable under load. That distinction is the entire job.
Several of my own products are pre-launch, so everything here is deliberately abstracted — that's the same discretion I'll apply to your work. Deeper technical walkthroughs happen under mutual NDA, never in public.
Before the code — 18 years operating
Operator & founder track record
MITx MicroMasters, Statistics & Data Science (MIT) · Independent Directors Certification, IIM Bangalore · MBA · B.Tech, Computer Science
Let's talk
Have a system that needs building — or rescuing?
Best fit for production RAG, AI features in real products, and Elixir/Phoenix work. Tell me the outcome you need; I'll tell you the narrowest first step to prove I can deliver it.