Built Caylex Backoffice from scratch — the internal staff console
One place for the team to onboard MCP servers, watch background jobs, flip feature flags, find and clean up orphaned data, and see how the product's being used.
Code, design, infrastructure, and AI — from my first college projects to founding-engineer work.
2019 → today.
One place for the team to onboard MCP servers, watch background jobs, flip feature flags, find and clean up orphaned data, and see how the product's being used.
Checks each new MCP server, runs the whole onboarding on its own, and only publishes the ones that pass a health check. Shipped as a CLI and a skill, so any server that supports self-registration (DCR) can be onboarded hands-free.
The pipeline that collects logs, metrics, and traces from across the system, so we can see what production is actually doing.
Customers can route their own telemetry straight from Caylex to whatever monitoring tool they already run — plus the admin UI to set it up.
Tracing for every model call — prompts, tokens, latency, cost — so the AI's behaviour is visible instead of a black box.
A new design system, built so AI agents can read it too (JSON-LD, llms.txt), with analytics. Live.
Agents pull in a skill (its instructions and tools) only when a task actually needs it, instead of carrying every capability in context all the time. The platform can grow a big library of skills while each agent stays lean and focused on the job in front of it.
Surfaces security issues found on connected servers and their tools, with a detail panel and advisory fixes, plus notifications when something needs attention.
The UI to give an agent its identity and instructions in plain markdown — its name, description, and how it should behave.
A screen where an admin can see how risky each tool an agent can use is, sort and filter by severity, bulk-disable the dangerous ones, and override a rating — so teams stay in control of what their agents are allowed to do.
Gives an agent a memory of what lives in each connected tool (Linear, Notion, Slack…), so it knows which one to check instead of searching everything from scratch — like labels on the drawers of a filing cabinet. Built both the backend and the configuration UI.
Works out what each server and its tools actually do, scores how much each tool can affect things, and flags security risks.
The flow for browsing the library of available servers, filtering by category, seeing details, and connecting them to a project — plus session recovery and faster page loads.
Agents that reason through multi-step problems and call the right tools, wired into scalable systems for industry-specific workflows.
Feeds real-time observability context into every debugging prompt, so the model reasons over live logs and traces instead of guessing.
Improved root-cause-analysis speed by 60%, cutting incident resolution from hours to minutes for large-scale cloud environments.
Pinpoints where a failure started and proposes the code or config fix, instead of leaving an engineer to dig through dashboards.
Built the backend APIs and the UI for logs, the foundational pillar of an observability platform.
A Python framework that syncs third-party sources into BloomChat AI on a schedule, keeping its data fresh without manual imports.
Added schema validation to the API endpoints to stop bad data and schema drift, so microservices and downstream pipelines hand off cleanly.
Node.js, PostgreSQL, a vector DB, and AWS powering real-time image sharing for attendees at Laracon India 2023.
Kafka, FastAPI, and MongoDB on Azure, processing millions of live-stream events every hour.
Automated model training and data exchange across distributed nodes.
Deep-learning models that recognize furniture, trained and deployed on AWS.
Algorithms that rebuild full hyperspectral images from ordinary RGB input.
Integrated sensor data pipelines for live process monitoring on the factory floor, plus IoT-based permeability and viscosity tests using microcontrollers.
Automatic number-plate recognition in Python — detecting and reading vehicle plates from images with OpenCV.
Turning raw data into a clear narrative — a storytelling project (Alpha AI).
A digital-electronics project — one gadget with several practical applications.
A basics-of-electronics project — a working audio mixer.
An engineering-drawing and CAD project — modeled a handy laptop stand and cut it on a laser cutter.