— WHO WE ARE

An engineering agency, not a strategy deck

HEILC builds AI products, machine learning models and the enterprise software they live inside. We work from Chennai, India, with clients worldwide, and we measure ourselves on what reaches production.

The HEILC engineering workspace

FOUNDED BY

Pardhasaradhi Menda

Varshith Dondamuri

Why HEILC exists

Most AI work dies in the gap between a notebook that produces an impressive output once and a system a business can depend on daily. The interesting engineering is almost entirely in that gap: evaluation, retrieval quality, access control, latency budgets, cost per query, monitoring, and what happens when a model provider is degraded at two in the morning.

HEILC was founded to work in that gap. We take a problem from the decision it has to support through to a deployed, monitored, documented system running in the client's own cloud account — and we prove it works against an evaluation set built before the first line of model code.

Our own products are the reference. GeneRisk AI classifies DNA sequences at 97.37% accuracy on held-out data. CineGenome holds structured analysis of 3,477 films across four independent dimensions and serves recommendations from cached vectors rather than live model calls. ResumeAI went from concept to working build inside a 24-hour hackathon. Different problems, same discipline.

THE FOUNDERS

Two people, named, who are on the calls and on the commits.

Pardhasaradhi Menda

Co-Founder — AI & Machine Learning Engineering

Pardhasaradhi Menda co-founded HEILC to close the gap between machine learning that works in a notebook and machine learning that works in production. He leads model development and evaluation across HEILC's engagements: feature engineering and validation strategy on classical ML problems, retrieval architecture and groundedness evaluation on LLM systems, and the evaluation harnesses that decide when a model is actually ready to ship. He built GeneRisk AI, the DNA-sequence cancer risk classifier that reaches 97.37% accuracy on its held-out test set, and led the cinematic-DNA enrichment pipeline behind CineGenome's 3,477-film index. He is direct about what models cannot do, which is usually the more useful half of the conversation.

Focus areas

  • Machine learning engineering and model evaluation
  • Retrieval-augmented generation architecture
  • Applied AI product development

Varshith Dondamuri

Co-Founder — Product & Platform Engineering

Varshith Dondamuri co-founded HEILC and leads product and platform engineering: the application architecture, API design, cloud infrastructure and delivery pipelines that turn a model into something a business can depend on. His work covers full-stack product development in Next.js and TypeScript, backend services in Node.js and Python, and the deployment and observability layer underneath. He built the CineGenome platform — the FastAPI retrieval service, the Pinecone and MySQL data layer, and the Next.js interface over it — and delivered ResumeAI end to end inside the 24-hour AppXcelerate 1.0 hackathon window, including the three-strategy JSON parser that made its structured model output reliable enough to demo.

Focus areas

  • Full-stack product engineering
  • Cloud infrastructure and delivery
  • API and platform architecture

HOW WE WORK

Evaluation before implementation

We build the evaluation set — real questions, real records, an agreed definition of correct — before we write model code. Without it, 'better' is an opinion and nobody can say when the project is finished.

You own everything at the end

Repository, model weights, training data, documentation, cloud account. No licence-back, no proprietary runtime you have to keep paying for. If you wanted to continue without us, you could.

We will tell you when AI is the wrong tool

A rules engine, a better query, or leaving a working legacy system alone are all legitimate outcomes of a discovery week. Saying so early is cheaper for you than discovering it in week five.

Numbers come with their source

Every figure on this site names the dataset or the constraint it was measured under. Where we do not have a number, we say so rather than reaching for a plausible one.

WHAT WE TAKE ON

Work with us

Tell us what you are building and what is in the way. We will be straight about whether we are the right team for it.

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