I work where knowledge graphs, retrieval-augmented generation, and enterprise data converge — turning scattered, inconsistent systems into something an AI can reason over correctly, and prove it did. Full-stack engineering background, systems-analyst rigor, now going deep on what I call the Knowledge Layer.
This is the architecture pattern I've built hands-on, in production — the same core shape you'll find under most serious enterprise AI-grounding platforms today.
Competency questions define the domain's ontology before a line of extraction code is written. SHACL constraints validate it; it's versioned and git-tagged like any other production artifact.
OWL · SHACLLanguage models extract entities and relationships against that ontology's schema — not into an open vocabulary. The model can't invent a relationship type the domain doesn't define.
Structured LLM OutputExact-match normalization first, then embedding-cosine similarity. Auto-merge above ~0.92, human review between 0.80–0.92. Nothing gets silently duplicated or silently merged.
Two-Tier ResolutionA templated query layer sits between the model and the graph. The AI can request evidence — it can never author an arbitrary raw query against production data.
Grounded, Not GuessedI track this space closely — not as a trend, but because real, funded companies are already commercializing the exact architecture pattern above, at two different scales.
A modular GenAI agent platform: knowledge graphs and semantic search as core infrastructure beneath a catalog of pre-built agents. Philosophy — "connect once, govern once, reuse everywhere." Land on one high-friction use case, expand from there.
Brands itself "The Knowledge Layer Company." Builds an RDF/OWL semantic layer purpose-built to ground AI agents in premium industrial and automotive engineering environments.
"The Knowledge Layer thesis isn't a bet I'm making alone — it's already being commercialized at enterprise scale and at vertical depth. I've built the same core pattern hands-on, in a live engagement."
Full-stack delivery, enterprise integration, and now AI-grounded knowledge systems — for named clients, with measurable outcomes.
Full production systems, built solo — the same rigor as client work, applied to problems I picked myself.
A logistics OS for Pakistan's transport/"bilty" ecosystem — event-driven parcel state, offline-first sync, admin dashboard, and a driver mobile app.
A bid-based delivery marketplace for the Japanese market — order → bid → accept → payment → delivery → rating, built toward APPI and Freelancers Act compliance.
Contributed institute multi-tenancy, an XP/leaderboard engine, and custom quiz authoring to an Arabic-morphology learning platform.
SIR/SEIR disease-spread modeling with Bayesian and evolutionary optimization, streamed live to a scientific visualization dashboard.
A Kitchen Display System and table-side POS on ERPNext, with real-time sync and independent per-split bill settlement.
Scans a product barcode, cross-references Open Food Facts, and runs a rule-based ingredient engine to classify Halal status on the spot.
The Knowledge Layer work doesn't sit on nothing — it sits on a decade-adjacent range across full-stack engineering, enterprise systems, and delivery leadership.