Full-stack, mobile & AI engineer · Melbourne, Australia

I turn messy information into products people rely on.

I'm Ali Abbas — a full-stack, mobile, and AI engineer drawn to the whole of a hard problem: the models and retrieval at the core, the data pipelines that feed them, and the product people actually use. I learned it by building and operating Shia Library, a production search platform — on the web and as iOS and Android apps — over a large, multilingual corpus. I'm building toward AI products that ship at real scale.

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~315k: hadith records ingested
4,900+: automated tests, web + mobile
91%: claims grounded (30-q eval)
Web · iOS · Android: one product, live on every surface

Selected work

Systems tested at the boundary

Production web and mobile products, real model training, grounded retrieval, and evaluation work. Each case study shows the measured result and where it stops holding.

All work

Flagship · Search & retrieval platform01

Shia LibraryLive

Web · iOS · Android

A production digital library that grew from search into answers — and from one client into two. Diacritic-insensitive Arabic search, English full-text, and semantic vector search over a corpus of over 315,000 ingested records; a grounded "Ask" (in beta) that retrieves from the live corpus and answers with Claude's native citations, so every claim links to the exact source span or the system abstains; and one Supabase/Postgres backend serving both the web platform and offline-first iOS and Android apps. Built with row-level access control, an in-app CMS, and a reading experience tuned for dense scholarly text.

Next.js 16React 19TypeScriptSupabase / PostgrespgvectorClaude (native citations)

~315k: hadith records ingested
91%: claims grounded (30-q eval)
1,000+: web tests in CI
0.94: faithfulness (Claude judge)

Read case study

Machine learning & evaluation02

PolaritySource release

A 184M-parameter cache-equivalence model, and the scientific debugging trail that established both where it works and where it fails.

PythonPyTorchHugging Face TransformersDeBERTa-v3scikit-learn

184M: trainable parameters
2/44: Veritas false accepts

Case study

Mobile engineering03

Shia Library — Mobile AppLive

iOS · Android

The same library, offline-first on iOS and Android — hand-written Swift and Kotlin keep downloads alive under each OS's rules, and the offline store makes a half-visible book impossible. Live on the App Store and Google Play.

React Native (Expo)TypeScriptSwiftKotlinObjective-C++

3,900+: automated tests (Vitest)
iOS + Android: public, one codebase

Case study

AI / retrieval systems04

Citation-Grounded RAGLive

A retrieval-augmented answer engine where every claim is grounded in a specific source — hybrid retrieval, verifiable citations, an abstain path, all measured by an eval harness.

Next.js 16TypeScriptClaude (native citations)OpenAI embeddingsBM25 + RRF

0.91: recall@5 (hybrid)
1.00: faithfulness (Claude, 22-q)

Case study

AI infrastructure05

LLM GatewayLive

A provider-agnostic LLM gateway whose semantic cache is proven correct — precision, false-positive rate, and a CI gate most managed gateways don't expose.

Next.js 16TypeScriptClaude Haiku (intent judge)OpenAI embeddingsOpenTelemetry gen_ai.*

1.00: cache precision (guarded)
0%: false-positive rate

Case study

AI evaluation & methodology06

JudgelabLive

A reproducible lab that measures how reliable LLM-as-a-judge systems actually are — chance-corrected agreement with confidence intervals and a keyless, license-clean benchmark, not a single agreement score.

Pythonuv · Ruff · mypy (strict)pytestNumPy / SciPyPydantic · Typer