I build AI systems that solve human problems.
I think harder about why than how.
Aarav Kashyap Singh (byaarav) is an AI engineer building agentic RAG systems, backend workflows, and automation tools.
I do my best work where a workflow feels broken, a product needs sharper thinking, or an AI feature has to become reliable enough for real users.
Right now I'm finishing my undergrad and shipping production-grade AI systems. Not demos. Not tutorials. Things that actually run.
A selection of product-focused builds, from AI tools to fullstack platforms and real-time dashboards.
Internal liveAI platform that converts bank statements into audit-ready summaries for lending teams.
Public liveAI recruiting copilot that ranks candidates and delivers evidence-backed hiring recommendations.
A focused stack built around shipping real products fast and correctly.
The languages I reach for when I need performance, product speed, or a backend that stays readable under pressure.
Python anchors most of the AI and backend work. TypeScript and SQL carry product and data contracts cleanly.
Interfaces that feel considered, move cleanly, and stay connected to the backend contract instead of floating above it.
I care about the final surface as much as the system underneath it, especially when product clarity and motion matter.
APIs, queues, databases, and infra choices built around reliability, speed, and not painting the product into a corner.
This is the layer that makes shipping fast possible: clean data models, sane service boundaries, and deployment that behaves.
Model selection, agent orchestration, NLP, retrieval, and the tooling layer that turns AI features into working products.
This is where I connect model capability to product usefulness: orchestration, embeddings, evaluation, and inference patterns that behave in production.
I only take on work I can own end to end. These are the systems I know how to ship, harden, and make useful.
From messy inputs to reliable AI workflows with clear system boundaries.
Production backends where models do real work: retrieval, reasoning, validation, and structured output.
Products that ship cleanly from schema to surface without losing coherence on the way up.
I own the full path: backend contracts, frontend interaction, deployment, and the final feel of the product.
Raw PDFs, scans, and statements turned into clean, auditable structured output.
Vision models, extraction pipelines, and validation layers built for operational use instead of demos.
Language models woven into existing products so the workflow improves without the product breaking.
I integrate AI where it belongs: search, copilots, automation, and internal tools that respect the rest of the stack.
I build production grade AI systems, sharp product surfaces, and workflows that still feel good when real people start leaning on them every day.
