Production AI Systems, Built to Hold Up
From knowledge provenance and Voice AI to backend orchestration and workflow automation — I build systems that survive real users, real load, and real edge cases.
Knowledge & Provenance
Self-maintaining knowledge bases, claim-level provenance, and trust layers for multi-agent systems. ISNAD — grading every agent and model in a claim's chain.
Voice AI
Real-time TTS/STT, telephony integrations, and voice agents for operations where latency and accuracy are non-negotiable.
Backend & Orchestration
LLM pipelines, workflow automation, and system architecture that holds up under real production constraints.
What the Work Actually Changed
Client-reported figures. Each tied to its case study.
client-reported · Islam & AI
client-reported · Islam & AI
self-reported · Islam & AI
client-reported · Entropic
client-reported · Entropic
self-reported · community
Three Pathways, All Focused on Production Outcomes
Full-time & Leadership
Senior ownership for teams with expensive technical problems.
Staff-level or founding AI engineer roles where you need someone to own architecture, execution, and production reliability end-to-end.
- Production AI system ownership
- Technical leadership and architecture direction
- Cross-functional execution with product and ops
Fractional CTO & Client Builds
Production AI systems for founders and teams with real budgets.
Selective client engagements and fractional CTO work — from voice AI and knowledge systems to backend orchestration and workflow replacement.
- Architecture through deployment ownership
- Voice AI, RAG, and backend system delivery
- Workflow automation that holds up in production
Advisory & Research Distribution
Positioning, strategy, and launch sequences for researchers and technical founders.
Premium advisory for engineers and founders navigating global tech markets, research distribution, and positioning at global standards.
- Global tech market strategy
- Positioning for high-trust roles
- Research launch and distribution
Production Case Studies
Across logistics, knowledge platforms, healthcare, fintech, and AI delivery.
ISNAD
Open-source, claim-level provenance framework for multi-agent AI systems — adapted from classical Islamic hadith transmission science. Published paper (arXiv:2607.24117), reference implementation with 157 tests, five pluggable strategy interfaces, PyPI package.
Stack Python, multi-agent provenance, narrator grading, Bayesian grading, LangChain integration
Explore ISNAD →Islam & AI
The ProblemGlobal users needed reliable, multilingual access to Islamic knowledge at scale.
BuiltGlobal knowledge platform with RAG over Qur'an + 600k+ Hadith, multilingual NLP, OCR, and retrieval systems. Began as a Microsoft Imagine Cup participant.
OutcomeServing 25k+ users across 150+ countries in production (client-reported). Secured seed funding; declined acquisition offers.
Stack RAG, NLP, OCR, retrieval infrastructure
Visit Islam & AI →Entropic Technologies
The ProblemTeams needed production AI execution, not fragile one-off automations.
BuiltGPT-powered email generation, document parsing with structured extraction, and memory-safe therapy platform APIs.
OutcomeProduction systems shipped across industries. 10k+ therapy platform users (client-reported). 3× faster campaign creation (client-reported).
Stack LLM orchestration, backend APIs, structured extraction, safety systems
Visit Entropic →Glacis — Voice AI
The ProblemLogistics teams were bottlenecked by manual dispatcher calls and follow-ups.
BuiltVoice AI agents for outbound calls, scheduling, ETA updates, and compliance-critical communication.
OutcomeDispatcher-heavy operations moved to live voice automation in production logistics flows.
Stack Voice AI, telephony integrations, workflow orchestration
MDVoice
Linkmdvoice.ai →
The ProblemClinicians were losing time on manual documentation after patient conversations.
BuiltReal-time doctor-patient transcription with SOAP note generation workflows.
OutcomeProduction-ready transcription and SOAP support for high-volume clinical documentation.
Stack Real-time transcription, clinical NLP, structured note generation
The Tooling Family
Four tools, one thesis: AI systems need the same testing, linting, and CI discipline we already demand of code.
ISNAD
Grades the chain a claim travelled through. Claim-level provenance for multi-agent knowledge systems.
Explore →Agent-Surface
Makes your website usable by AI agents. Converts web apps into agent-friendly interfaces with machine-readable manifests.
View all tools →knowledge-ci
Repository-native RAG with built-in evaluation. Fails your build when answer quality degrades.
View all tools →RAGLint
CLI-first tool that scans arbitrary data and emits an AI Data Readiness Report for RAG systems.
View all tools →How Multi-Agent Systems Earn Trust
ISNAD is an open-source framework for claim-level provenance in multi-agent AI systems. It adapts 1,200 years of hadith transmission science to grade every agent, scraper, and model in a claim's chain — identifying the weakest link before you serve the result.
The Paper
Published July 2026. arXiv:2607.24117. 25 pages. Full framework specification, worked example, and §8 evaluation.
Read the Paper (arXiv) →The Framework
Reference implementation — 157 tests, five pluggable strategy interfaces, PyPI package v2.0.5, LangChain integration.
Explore ISNAD →Trusted by Those I've Built With
"Ali is an exceptionally talented software engineer with deep expertise in end-to-end system design and a passion for advancing AI and ML technologies. His ability to design and build AI systems is outstanding. Ali is also a highly professional teammate, making it a pleasure to work alongside him."
Noah Marra
Sr. Machine Learning Engineer, AMD · Former teammate
"Ali Raja is an exceptional and talented data scientist that possesses great entrepreneurial and technical abilities. Working with him is always a pleasure. His positive spirit when working part of a team or a leadership capacity propel any project with great energy and enthusiasm."
Ali Almussa
Entrepreneur & Project Management Consultant
The Pattern
Truth over optics
"Every real jump in my career came right after I admitted I was wrong about something." The ISNAD paper reports its own inconclusive results in the same weight as its successes. This site reports metrics as client-reported where they can't be independently verified. The pattern: if you can't say what doesn't work, you don't understand what does.
Tradition to technology
Classical Islamic hadith science spent 1,200 years solving how to verify knowledge transmitted through chains of narrators. Multi-agent AI has the exact same problem. ISNAD transfers that methodology. The pattern: the best new ideas aren't new — they're old wisdom applied to new problems.
Craft over credentials
A 2.69 GPA and failed startups followed by global top 0.1% in competitive programming. Not a contradiction — the traditional path measures the wrong things. The pattern: judgment and systems thinking are visible in what you ship, not where you studied.
Talent is global. Opportunity is not.
From Pakistan to Silicon Valley to Estonia to Saudi Arabia — built production systems across four countries, three continents, and multiple cultures. The pattern: the best engineering happens at the intersection of disciplines, not inside them.
Ready to Build Something That Holds Up?
I'm open to serious conversations with founders, recruiters, and teams with real budgets and expensive technical problems.