Production Systems
Systems
Selected production systems — each with a real problem, a real build, and a real outcome. Where a metric is client-reported, it says so.
ISNAD
The ProblemWhen a multi-agent AI system answers you, that claim has passed through many hands — a source, a scraper, an ingestion model, a synthesis model. Each hand can drop, distort, or invent. Provenance tools record what happened. Nothing grades who did it.
BuiltOpen-source, claim-level provenance framework adapted from classical Islamic hadith transmission science. Reference implementation with 157 tests, five pluggable strategy interfaces, end-to-end worked example. Published paper (arXiv:2607.24117), PyPI package, LangChain integration.
OutcomeThe first framework to apply narrator grading and chain-level trust assessment to multi-agent AI pipelines. Validated on 20,000 claims; 4,057 quarantined claims traceable to specific narrator grades.
Stack Python, multi-agent provenance, narrator grading, Bayesian grading, LangChain integration
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 across logistics, healthcare, marketing, and data platforms.
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
Maverick AI
The ProblemPublic safety and compliance needed automated computer vision during COVID-era operations.
BuiltComputer vision for public safety — mask detection, people counting, social-distance checking. Led AI R&D as Co-founder & Chief AI Officer.
OutcomeGrew engineering team from 3 to 12. Shipped CV systems deployed across multiple client sites.
Stack Computer vision, PyTorch, edge deployment, real-time inference
Genie AI / Albis
The ProblemTeams needed reliable speech and vision systems for real-world use cases.
BuiltBuilt the first MVP of Albis, a personalised 3D avatar assistant with TTS/STT voice interaction. Head of Machine Learning.
OutcomeShipped TTS/STT voice assistant systems and computer vision workflows for production use.
Stack TTS/STT, computer vision, OCR pipelines, 3D avatar rendering
Motive (KeepTruckin)
The ProblemFleet management needed automated AI testing pipelines for safety-critical systems.
BuiltProduction AI testing pipelines and systems behind thousands of vehicles and drivers. Landed via a $200 LinkedIn post that drew 726 reactions and six interviews.
OutcomeNegotiated the package up. Worked on production systems serving thousands of drivers.
Stack Production AI testing, fleet management systems, safety pipelines
Isharay
The ProblemAccessibility for the deaf and hard-of-hearing community in Pakistan.
BuiltPakistani Sign Language translator — computer vision + NLP. Finalist at the Pakistan Science Fair Award (Ulster University), January 2020.
Outcome50% tuition scholarship toward Ulster MSc in Data Science, AI, or IoT. Judged by panel chaired by Ulster's Associate Dean for Global Engagement.
Stack Computer vision, NLP, accessibility
GitHub → Ulster University →Research Distribution as a System
The ProblemIndependent research dies in obscurity. No lab, no press office, no coauthor network.
BuiltA repeatable launch sequence — preprint and DOI, archived software release, package distribution, aggregator submission, community seeding, long-form technical writing, conference submission — each stage instrumented.
OutcomeIndexed on arXiv and Hugging Face Daily Papers within days. An unsolicited comparison document from the co-creator of Selenium. The launch sequence produced third-party coverage within three weeks.
Stack arXiv · Zenodo · PyPI · Hugging Face · Reddit · Medium · LinkedIn
It is the receipt.
Selective engagements for teams with real budgets and expensive workflow problems.