I build scalable backend systems, retrieval pipelines, and production AI infrastructure.

Every call reviewed, not one in ten.

AI Sales Coaching Platform

Every sales call transcribed, scored against a fixed rubric, and written back to Salesforce within minutes.

• Aircall webhook drops a call recording onto a Celery queue; transcription and scoring run as separate tasks so a slow LLM call never blocks ingestion.
• Every transcript is scored against one fixed rubric rather than a free-form prompt, so two calls a month apart are comparable.

• FastAPI
• Celery
• React 19
• LLM
• Aircall
• Salesforce

One story, every paper, one neutral summary.

Nirpekha Barta: AI Unbiased News Aggregation

Clusters one story across politically diverse Bangladeshi papers, scores each source's bias, publishes a neutral Bangla summary.

• A five-stage async pipeline — collect, cluster, bias-score, summarize, publish — where each stage is a separate Celery task, so a slow scrape never blocks publishing.
• Articles are clustered into stories before any summarisation happens; summarising first and grouping later loses the disagreement, which is the whole product.

• Next.js 14
• FastAPI
• Celery
• LangChain
• PostgreSQL
• Redis

Runners-up, Infinity AI BuildFest 2026 (InfoTech), CloudCamp BD

There is no write path to find.

MCP Servers Monorepo

Self-contained MCP servers, including a database-agnostic read-only SQL server that turns plain English into guarded queries.

• Read-only by construction — the server exposes no write path at all, so a prompt injection has nothing to reach for rather than being talked out of using it.
• Database-agnostic, so the same guarded surface works across engines instead of one bespoke integration per database.

• TypeScript
• Model Context Protocol
• Node.js

It checks its own answer before returning it.

LocalLens: Conversational AI Agent

A LangGraph multi-agent system that answers questions about places by searching, reading reviews, and fact-checking its own summary.

• A LangGraph orchestrator runs a five-stage graph — intent, geolocation, multi-source search, review and sentiment, grounded summary — rather than one prompt doing all five badly.
• Results stream to the client over SSE, so a multi-second graph traversal shows progress instead of a spinner.

• Python
• LangGraph
• LangChain
• FastAPI
• Playwright

Nothing leaves the machine.

RAG Docs Assistant

Documentation Q&A that crawls docs or ingests PDF/DOCX/HTML and answers from a local LLM. No external API, nothing leaves the machine.

• Ingests either a crawled docs site or uploaded PDF/DOCX/HTML, so the same index serves published docs and internal ones.
• Token-aware chunking rather than fixed character splits, so a chunk boundary does not land mid-sentence and strand the context a retrieved passage needs.

• Python
• FastAPI
• PostgreSQL
• pgvector
• Ollama
• Docker