Ayah · آية
An offline-first “Shazam for the Quran” that identifies the exact Surah and Ayah from recitation using a Whisper-class model running entirely on-device.
AI Engineer · Founder, Nexasity AI
Production experience across RAG pipelines, multi-LLM orchestration, multi-agent systems, and on-device inference. Shipped 10+ AI/ML systems to 500+ users; code merged into Grafana and Crawl4AI. BSc Computer Science, BRAC University.

Why me

Inference that never leaves the phone. Ayah runs a full-precision ONNX model on-device via Transformers.js — audio is transcribed locally and never uploaded.

RAG for students priced out of coaching. LesLearn and RootLearn ingest the national curriculum and teach from each learner's actual level — offline and zero-cost by design.

Evals, calibration, and grounded retrieval. whyLayer, CiteXai, and the World Cup 2026 calibration loop check answers before trusting them.
World Cup 2026 → whyLayer → Ayah → skeletree
Ayah captures ~7s of audio and runs a ~145MB ONNX model on-device via Transformers.js. Arabic recitation is transcribed and fuzzy-matched to the exact Surah and Ayah — audio never leaves the phone.
whyLayer interrogates a decision through 3–9 adaptive questions, hunts web evidence (Serper / Jina / DuckDuckGo), and returns a GO / NO-GO verdict with a confidence score — reasoning routed across a 5-provider LLM cascade.
World Cup 2026 predicts every match with a 5-model stacking ensemble over 40+ engineered features — evaluated on log-loss, RPS, Brier, and ECE, with post-hoc calibration (temperature + isotonic) and a daily retrain-and-monitor loop.
skeletree scans a repo once and emits a compact map — the directory tree plus every class and function signature, no bodies — so AI coding agents grasp a codebase for ~90% fewer tokens. Published on PyPI with MCP integration.Four themes run through the work: private on-device AI, decision intelligence you can trust, calibrated quant-ML, and developer tooling.

Private inference that runs entirely on the phone — audio and data never leave the device. Ayah, RootLearn, RainXai.

Multi-LLM systems that interrogate and verify before they answer. whyLayer, False9.

Calibrated models with proper scoring, time-split validation, and monitoring. WC2026, StockIND, HR Attrition.

Tools that make AI agents cheaper and sharper. skeletree, oss-hunter, GitChrono.
Everything I’ve shipped or am building — 32 projects across on-device AI, decision intelligence, quant-ML, RAG, and developer tooling.
An offline-first “Shazam for the Quran” that identifies the exact Surah and Ayah from recitation using a Whisper-class model running entirely on-device.
A decision engine that interrogates a choice before you make it — multi-LLM profiling plus web research, returning a GO / NO-GO verdict with a confidence score.
An AI stock scanner that screens 500+ symbols with per-ticker XGBoost models and a FinBERT sentiment veto. Research only.
An autonomous system that audits, fixes, and completes projects with 12 domain-expert agents running in parallel inside any AI coding environment.
A gamified platform for learning prompt engineering across 100 challenges with 3-layer AI grading (validation → regex → LLM judge), XP, and certificates.
A CLI that maps any repo into a token-cheap skeleton — tree plus every signature, no bodies — cutting AI-agent context cost ~90%. Published on PyPI.
Detects fake and AI-hallucinated citations by cross-referencing them against 200M+ papers across CrossRef, OpenAlex, Google Books, and arXiv.
A 5-model stacking ensemble over 40+ features with strict time-split validation and post-hoc calibration — evaluated on log-loss, RPS, Brier, and ECE.
A Bangla-first adaptive math tutor (Class 5–8) that diagnoses learning gaps and teaches from the student’s actual level — fully offline via local Gemma.
A RAG adaptive-learning platform for Bangladeshi students (Classes 1–10) priced out of coaching — zero-cost by design.
Ingests messy legal documents, retrieves evidence via vector search, and generates grounded case summaries with a citation per claim.
A deterministic, rules-based CRM ticket classifier — problem type, severity, department, plus phishing detection in English and Bangla. No LLM.
A CLI that finds real good-first-issues, ranks them by ease of merging, and drives the workflow from discovery to pull request.
An Android app that turns raw call history into ranked leaderboards and communication analytics — 100% on-device.
An Android screen-content auditor and app-locker that uses Gemini to label what’s on screen Useful or Wasteful and lock out lazy scrolling.
A Chrome extension that auto-categorizes bookmarks with a three-tier engine of domain matching, regex rules, and Groq LLaMA 70B.
Estimates coding time from GitHub language stats. Published as a CLI on npm with a web dashboard.
An end-to-end pipeline converting PDF content into narrated, animated videos via CrewAI → Manim → EdgeTTS, with auto-upload.
An academic marketplace connecting Bangladesh students with vetted experts, with AI price estimation and bKash payments.
An anonymous messaging platform tied to Instagram handles — no signup, private bookmark-only inbox.
A Jira app that analyzes project data and generates predictive “future retrospectives” so teams anticipate risk early.
Scans Reddit for buying-intent posts, scores them, and drafts compliant outreach — draft-only by default.
A direct-to-consumer storefront with geo-adaptive pricing and an admin panel, shipped as a single-file build.
A client-side expense tracker with a clean dashboard, multi-currency support, and a Zod-validated entry form.
A privacy-first expense tracker where you log spending by voice in English or Bengali, stored entirely on device.
A supervisor-student matching engine that pairs thesis supervisors with students by research-interest overlap.
An offline-first dashboard that ranks students by mean score and exports an LLM-ready evaluation prompt in one click.
A CRISP-DM attrition model on the 1,470-record IBM set — XGBoost at 92% accuracy / 0.92 ROC-AUC with a Tableau dashboard.
A linear-regression notebook predicting vehicle CO₂ emissions from engine size, cylinders, and fuel consumption.
A web app for tracking criminal profiles, investigations, and evidence, and finding crime patterns — built on raw SQL.
An agriculture and organic-farming marketplace PHP template with transporter and delivery-management pages.
An Android app that tracks where you go using free OSS APIs and shows where your time went — raw GPS never leaves the device.
The stack behind the work, and the loop it runs on: Data → Model → Eval → Deploy → Monitor.
Credentials in agent tooling, applied AI, and data analytics — from Anthropic, Google, and industry programs.
MCP fundamentals — exposing tools & resources and wiring agents to live data.
Building effectively with AI — prompting, agent workflows, and responsible use.
5-course specialization — AI tools, prompting, and responsible AI use.
SQL, Excel, Tableau, Power BI, and exploratory data analysis.
“Testimonial coming soon — this space is reserved for feedback from collaborators and the people who use what I build.”