Train
Python, PyTorch, Hugging Face, Transformers, embeddings, fine-tuning.
AI / ML2026Gurugram
Building interfaces
with a mind of their own. Schedule
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Selected work01 — 08
Stack
Python, PyTorch, Hugging Face, Transformers, embeddings, fine-tuning.
LangChain, LlamaIndex, LangGraph, MCP.
RAG, vector search, SQL, NoSQL.
TypeScript, React, Node, Three.js, WebGL, AWS, Azure, Docker.
What I focus on
01 — Legistify — 2026
Codex is the agentic copilot I built for Legistify. It sits on contracts, notices, litigation, and trademarks: Python and LangGraph beside the Node API and the React app, on AWS.
A research agent calls tools in a loop, then a separate writer answers. There is an intent gateway, a point where it can ask the person, and a guard on how long it may think. Answers stream from Claude on Bedrock and from OpenAI. Tools are isolated per tenant: search, signed copies, org metadata, live web search, and OAuth connectors for Gmail, Calendar, and Outlook.
It writes PowerPoint, Excel, HTML reports, and redlines: a Word file with real track changes and clause citations. Projects, reusable skills, and a document vault sit around that. A query guard and grounding rules keep it from inventing an id or a link.
Check live: Codex guide
Python · LangGraph · Bedrock · MCP
02 — Telephony bot — 2026
1.2s
perceived response
This is the autonomous sales bot. LangGraph does the reasoning and the tool calls. Speech to text, voice activity, and text to speech run on the same loop, with Groq for the inference. The person on the line hears a reply in about 1.2 seconds. Bot-qualified leads converted at 1.83 percent, against 2.28 percent for human reps, at a fraction of the team’s cost. About ten thousand calls a month.
Each line is its own container. They register with one orchestrator over Redis, take leads from a queue, and recover when one of them dies. Adding capacity is starting another container. Voice, model, and the rest of the config live in Postgres and are read from Redis before every call, so a change does not need a deploy.
LiveKit carries the media. Exotel calls come in over SIP, and audio and video can share one room, so the same pipeline can leave voice-only and take a video call.
LangGraph · Groq · LiveKit · Redis · Exotel
03 — Hero Vired — 2025
10,000
recordings a day
Built for Hero Vired. Every sales call is transcribed, then read by a model with a schema so the result can be written straight to the database. That replaced the previous vendor and cut the cost by 83 percent.
Queues sit between the pipeline and the outside services, so a busy hour is paced against their rate limits instead of hitting them raw. The same desk scores leads, reads classroom meetings, and writes the weekly report.
Node · Python · PostgreSQL · AWS
04 — Cloud IDE — 2025
A backend service, not a hosted product page. An agent clones the repository, reads it, changes it, commits, and opens the pull request. Each session gets an isolated container. More than twenty can run at once.
Three specialists share the session: bugs, tests, and features. Contracting the memory made token use about five times leaner. Caching the repository cut the cold start by seventy percent.
Python · LangGraph · Docker · AWS
05 — RAG · ReAct — 2024
Two pipelines, one front door. A classifier decides whether the question needs search or a tool. Search is hybrid, dense and sparse, with contextual chunks and a cross-encoder to rerank.
The other path is a ReAct agent with more than fifteen tools, on a LangGraph state machine, with the slow work carried in the background.
Python · LangChain · LlamaIndex · LangGraph
06 — WhatsApp bot — 2025
A WhatsApp bot. A job agent, a sales agent, and a general agent share the chat, and LangGraph can hand it from one to another without starting over.
The model parses the message. The thread is held so a long task can sit and come back. More than one language.
Python · LangGraph · Twilio
07 — Open source — 2026
pip install wacp
npm install wacpjs
A backend's own routes become a layer an agent can read. MCP-like, without standing up a separate MCP server. One core, six frameworks: FastAPI, Flask, Django, Express, Nest, Next.
The search inside it is not a borrowed library. BM25, a dictionary of twenty-one thousand terms, fuzzy match, stemming.
08 — Personal project — 2026
Entangle is a personal project. It is how I drive a Mac from a phone or a browser. Screen, keyboard, mouse, files, and chat go peer to peer. The server sets the connection up and then stays out of that traffic.
On the same network the two sides talk directly. Otherwise a public address is found, and a relay is the fallback. If a tab refreshes or the network drops, the same session comes back. Each device keeps its own key, so a new IP is not a new device.
The Mac side is a menu-bar app. It captures the screen and injects the mouse and keyboard. I put the whole service on one Linux host: the API, nginx in front of it, and the relay. It is in real use.
WebRTC · Electron · TypeScript
Experience
I build production AI for Legistify and Hero Vired. On several products I was the only AI engineer, from the brief to the server. Employee of the Month, October 2025. Rockstar Rookie, December 2024. Three production AI products in the first forty-five days.
Study
Engineering College Bikaner. A paper on plant-based zinc oxide nanoparticles.
Maharaja Ganga Singh University, Bikaner.
State rank 17. All India rank 257. Before that, Interactive 3D Graphics with Udacity.
India time
Bring a problem, a system, or a question. I'll bring how I'd build it.
Or write arihant2001jain@gmail.com
India time
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