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OMExplorer

Om Jaiswal — Founding Backend Engineer at Ravan.AI. Explorer view.

01The lab

Project map

30+ projects built outside work. These are the ones with write-ups, grouped by domain.

9 mapped · connected by shared domain

Each node is a project. Lines connect projects that share a domain. Hover or tab through them, or press R for a random one.

02Engineering universe

Engineering domains

Select a domain to see the tools used there and the systems and projects behind it.

Domain

Backend

API surfaces and service architecture. The bulk of the work.

Primitives
  • Django REST Framework
  • FastAPI
  • Go
  • Gin
  • Flask
  • gRPC
Evidence
03Projects

Projects

30+ built outside work. Systems, full-stack products, and research published in IEEE.

  • Raft Distributed Consensus Simulation

    Distributed systems

    A working Raft cluster with a live dashboard showing consensus as it happens.

    Why

    Built from scratch to understand the algorithm rather than use a library.

    Hard part

    Implementing leader election, log replication and node failure recovery correctly, then surfacing cluster state in real time without distorting the behaviour being observed.

    What Om built
    • Full simulation of the Raft consensus algorithm in Go.
    • Leader election, log replication and node failure recovery.
    • gRPC for peer-to-peer communication between nodes.
    • Gin for the HTTP surface and GORM for persistence.
    • A Next.js dashboard visualising cluster state transitions and replication events in real time.

    A working cluster whose elections, replication and recovery can be watched live.

    • Go
    • gRPC
    • Gin
    • GORM
    • Next.js
    GitHub ↗

  • Skippit

    aka Hyperlocal Freelance AppFull stack

    A real-time hyperlocal freelance marketplace — tasks, live location and geospatial matching.

    Hard part

    Serving geospatial queries and continuous live location updates over WebSockets, without query load growing faster than the user base.

    What Om built
    • Django and Django REST Framework backend with a React Native (Expo) client.
    • Real-time task marketplace built on Django Channels and WebSockets.
    • Live location tracking with geospatial queries via PostGIS.
    • Redis caching on the hot paths.
    • Deployed on a Google Cloud VM.
    • Django
    • Django REST Framework
    • Django Channels
    • React Native
    • Expo
    • WebSockets
    • Redis
    • PostGIS
    • Google Cloud VM

  • Redis Clone

    aka py-redisSystems

    A Redis-compatible key-value server in Python — RESP protocol, threaded connections, in-memory store.

    Why

    Built to understand the protocol and command model from the inside rather than treating Redis as a black box.

    Hard part

    Parsing RESP correctly and serving concurrent clients over TCP while keeping a small command surface (strings and lists) faithful to Redis semantics.

    What Om built
    • TCP server on port 6379 with one thread per client connection.
    • RESP parser for Redis Serialization Protocol requests.
    • Commands: SET, GET, DEL, LPUSH, KEYS, PING.
    • In-memory datastore with type checks (e.g. WRONGTYPE on LPUSH against a string key).
    • Simple CLI client for interactive use.

    A working Redis clone clients can talk to with RESP.

    • Python
    • TCP sockets
    • RESP
    • Threading
    GitHub ↗

  • GitHub PR Review MCP

    Tooling

    A FastMCP server that lets an LLM list, review, comment on and merge GitHub pull requests.

    Why

    Wire Claude Desktop (and other MCP clients) into a real PR review loop instead of copy-pasting diffs by hand.

    Hard part

    Exposing GitHub PR operations as MCP tools and prompts so an agent can fetch a diff, suggest review comments, push them, and merge — with auth via a GitHub token.

    What Om built
    • FastMCP server with tools: list_prs, get_diff, create_pr, review_pr, suggest_comments, push_comments, merge_pr.
    • Prompt templates for structured review comments and overall PR review.
    • GitHub REST API integration with token auth.
    • Claude Desktop config for connecting the server as an MCP tool host.
    • Python
    • FastMCP
    • MCP
    • GitHub API
    • requests
    GitHub ↗

  • Implementation of Deep Learning in Metro

    aka Metro MonitoringResearch

    Computer vision system that detects incorrect seat usage in metro carriages and raises alerts automatically.

    Why

    Reserved-seat compliance is otherwise enforced socially or not at all.

    Hard part

    No usable dataset existed. The training set was built by hand — 5,000 annotated images.

    What Om built
    • YOLO-based detection model for seat occupancy.
    • A 5,000-image annotated dataset built manually and managed in Roboflow.
    • Automated alerting when a seat is used incorrectly.

    Provisional patent filed.

    • YOLO
    • Computer Vision
    • Roboflow
    • Python

  • Traffic Prediction using V2X and Clustering

    Research

    Traffic hotspot detection from V2X vehicle data using unsupervised clustering.

    Hard part

    Identifying traffic hotspots in V2X / V2AI data without labelled ground truth, which rules out supervised approaches.

    What Om built
    • K-Means and Agglomerative Clustering applied to the V2X / V2AI dataset.
    • Traffic hotspot detection from the resulting clusters.

    Published in IEEE — document 10971508.

    • Python
    • Scikit-learn
    • K-Means
    • Agglomerative Clustering
    IEEE 10971508 ↗

  • PosterCo

    aka E-commerce SystemFull stack

    A full-stack e-commerce platform with a recommendation system.

    Hard part

    Running collaborative-filtering recommendations inside a Django request cycle fast enough to be usable — handled with Scikit-Surprise and Cython.

    What Om built
    • Django and Django Templates storefront covering product catalog, cart, coupons, order history and payments.
    • A lightweight recommendation system built with Scikit-Surprise and Cython.
    • Deployed on AWS EC2 with MySQL.
    • Django
    • Django Templates
    • Python
    • AWS EC2
    • MySQL
    • Scikit-Surprise
    • Cython

  • Librosa

    aka Library Management SystemFull stack

    A production-style library management platform that replaced a paper-based record system.

    Why

    Replaced a paper system — attendance, seat allocation and student records were kept by hand.

    What Om built
    • Django REST Framework backend with a React frontend.
    • Student tracking, attendance and seat allocation.
    • Digital records replacing the paper-based system.
    • Deployed on GCP Compute Engine with Cloud SQL.
    • Django REST Framework
    • React
    • Python
    • GCP Compute Engine
    • Cloud SQL

  • Command Store

    Tooling

    A Next.js SPA for saving, searching and organising CLI commands — local-first, with import/export.

    Why

    A personal vault for commands that are easy to forget and annoying to dig out of shell history.

    Hard part

    Keeping it useful as a daily tool: fast search, tag filters, one-click copy, and JSON backup without a backend.

    What Om built
    • Next.js + React SPA with localStorage persistence.
    • Search and tag filters across saved commands.
    • Add / edit / delete with one-click copy to clipboard.
    • Import and export commands as JSON.
    • Next.js
    • React
    • TypeScript
    • Tailwind CSS
    • localStorage
    GitHub ↗
04Deep dives

Deep dives

Problem, constraints, architecture, decisions, trade-offs, implementation, result. Select a diagram node to see what it does.

05Production systems

Systems in production

Built and run at Ravan.AI — architected, deployed and operated.

  1. 01

    Maya

    AI SecretaryRavan.AI
    The problem

    Maya has 50+ capabilities. Registering each as its own LLM tool inflates the prompt and degrades tool selection, so the capability set couldn't grow without making the agent worse.

    What Om owned

    Architected and implemented end to end: 7 microservices across Go, gRPC, Python, FastAPI, Asynq and LangGraph, including the tool-routing layer.

    • Go
    • gRPC
    • Python
    • FastAPI
    • Asynq
    • LangGraph
    Deep dive: Routing 50+ capabilities through 2 tools
    7
    microservices
    50+
    capabilities
    reachable through 2 tools
    <500ms
    latency
    for 80% of requests
  2. 02

    Agni

    Voice AI PlatformRavan.AI
    The problem

    A live call consumes an LLM slot, a WebRTC session, a telephony channel and infrastructure capacity at once. Each saturates at a different point, so one global concurrency limit either wastes capacity or overruns a layer.

    What Om owned

    Architected and implemented the core campaign execution and concurrency-management systems, including concurrency controls across every layer.

    • Python
    • WebRTC
    • SIP
    • RTP
    • Telephony
    • Queueing
    • Schedulers
    Deep dive: Four concurrency ceilings, limited independently
    1,000+
    concurrent calls
    4
    layers of concurrency control
    model · WebRTC · telephony · infra
  3. 03

    CloserX

    Outbound Calling PlatformRavan.AI
    The problem

    The outbound calling pipeline sustained 5 concurrent calls. The limit came from synchronous execution serialising work that had no ordering requirement, not from hardware capacity.

    What Om owned

    Architected and scaled the platform, and redesigned the asynchronous calling pipeline end to end.

    • Django REST Framework
    • Python
    • RabbitMQ
    • Celery
    • Redis
    Deep dive: 5 → 300 concurrent calls
    60×
    concurrency increase
    5 → 300 concurrent calls
    50K+
    AI calls / day
  4. 04

    LiveKit Fleet

    Real-Time Media InfrastructureRavan.AI
    The problem

    Real-time voice needs media servers near the user, capacity that tracks demand, and fast removal of failed nodes — without taking on Kubernetes as a dependency.

    What Om owned

    Designed and operated fleet-based LiveKit infrastructure, including geo-routing, manual overrides, self-healing and dynamic capacity management.

    • LiveKit
    • LiveKit Agents
    • WebRTC
    • Cloud infrastructure
    Deep dive: Running a media fleet without Kubernetes
    0
    Kubernetes dependency
    fleet managed directly
  5. 05

    Shopify AI Agent & Analytics

    0 → 1 PlatformsRavan.AI
    The problem

    Merchants needed an AI agent inside Shopify, and the team had no internal view of its own data. Both were proposed rather than assigned.

    What Om owned

    Proposed, architected and built both platforms from 0 → 1. Also migrated production services from Docker-based deployment to MicroK8s.

    • Python
    • Docker
    • MicroK8s
    • Shopify
    • OpenAI API
    2
    platforms from 0 → 1
    Docker → MicroK8s
    production migration
06Leadership

Leadership

Student leadership at BVCOE — teams, departments and events.

  1. EduMinerva

    Joint Secretary

    Jun 2023 — Jun 2024
    120
    people led
    6
    departments overseen
    8
    events curated
    300+ attendees each
    • Led a team of 120 people across six departments.
    • Worked directly with the President and Vice-Presidents, acting as the bridge between the Core Team and executives.
    • Planned and curated eight technical and non-technical events, regularly drawing 300+ attendees.
    • Responsible for overall functioning and cross-department coordination.
    • Guided and trained freshers.
  2. EduMinerva

    Event Management Head

    Jun 2022 — Jun 2023
    10
    executives led
    • Led a team of 10 Event Management Executives.
    • Planned and executed events alongside the Core Team.
    • Provided logistical support across departments.
  3. Alumni Cell BVCOE

    Head — Food and Beverages Team

    Jan 2024 — May 2024
    ~700
    people catered for
    500 alumni · 200 staff
    • Led a team of 6.
    • Managed food requirements for an alumni event.
    • Coordinated catering for approximately 500 alumni and 200 staff.
07Contact

Contact

If something here was interesting, get in touch.

work.omjaiswal@gmail.comResumeLinkedIn ↗GitHub ↗Upwork ↗
New Delhi, India · Press L to switch lenses.