I don't build demos. I build systems that go to production.

Most AI engineers stop at the model. I start there.

I'm Taimoor Khan — an AI Engineer with deep expertise in architecting enterprise-grade machine learning pipelines, retrieval-augmented generation (RAG) systems, and deploying LLMs at scale.

My work sits at the intersection of ML research and production engineering — taking models from notebooks to reliable, observable, cost-efficient systems that handle real users and real data.

I believe great AI engineering means obsessing over:

• Reliability — systems that don't break at 3am
• Observability — you can't fix what you can't measure
• Scalability — designed for 10x from day one
• Precision — the right model, the right context, the right output

Currently co-building Stonepath Labs — an AI automation and enterprise systems company.

Production-grade Retrieval-Augmented Generation at scale

A battle-tested RAG architecture built for enterprise deployments, supporting multi-tenant document ingestion, hybrid dense-sparse retrieval, reranking pipelines, and LLM-agnostic query synthesis. Handles 160K+ document corpora with sub-second P95 latency.

Real-world Use Case → Deployed as core intelligence layer for clinical decision support and enterprise knowledge bases.

End-to-end ML pipeline orchestration platform

An MLOps platform enabling automated model training, versioning, evaluation, and deployment across cloud and on-prem environments. Features experiment tracking, data lineage, model registry, drift detection, and CI/CD integration, reducing time-to-production by 70%.

Real-world Use Case → Enables teams to ship ML models to production in hours, not weeks.

Intelligent document understanding and information extraction

A modular NLP system for extracting structured intelligence from unstructured documents at scale — combining entity recognition, relation extraction, document classification, and semantic clustering. Powers automated reporting workflows across high-stakes domains.

Real-world Use Case → Processes clinical notes, legal documents, and financial reports — extracting structured facts with 94%+ accuracy.

Unified hub for fine-tuned and production-ready ML models

A curated collection of production-optimized models spanning computer vision, NLP, and multimodal tasks — each with benchmarks, deployment configs, quantization support, and inference endpoints. Designed for teams who need reliable models without the research overhead.

Real-world Use Case → Accelerates AI adoption in enterprise settings by providing plug-and-play, production-ready model pipelines.

We help businesses adopt practical AI — not hype. Stonepath Labs builds intelligent systems and automation infrastructure for startups, clinics, agencies, and enterprises that want AI to actually work inside their operations.

"We don't sell AI. We build the systems that make it work."

What we implement:

Who we work with: Startups · Clinics · Agencies · Enterprises · Operations teams that know AI matters but don't know where to start.