Transforming contact center support operations with AI. Processes that are governed, measurable and built to scale.
Jonathan sits at the rare intersection of advanced AI use and tools scaling, with heavy domain expertise in contact center operations, workforce management, quality, training, reports, process automation and tools optimization — more than 20 years of global-scale business process transformation.
Stack & platforms / always shipping
Core competencies / not buried in bullets
Six disciplines (Shared Services, WFM, Finance, Data, AI and Automation). One operator who connects them all.
Built to ship, governed to last
The engineering discipline and enterprise platforms that take AI from prototype to durable, compliant production.
AI Governance
Policy, evaluation, and oversight frameworks that make AI adoption durable rather than performative.
CI/CD Pipelines
Automated build, test, and deploy pipelines that move models and apps safely from dev to production.
Python
Production Python — FastAPI services, Streamlit apps, and data tooling built for scale and maintainability.
SQL
Advanced SQL across Snowflake and PostgreSQL — from raw ingestion to self-serve executive analytics.
Azure
Cloud-native deployment on Azure — containers, hosting, and AI services with cost and security controls.
Power Platform · M365 · Snowflake
Enterprise platform fluency across the Microsoft ecosystem and Snowflake for scalable data and automation.
Operations that scale, end to end
Two decades inside large global contact centers — the systems that keep thousands of seats running profitably.
Scalable WFM Tools
Omnichannel forecasting, intraday management, and concurrency modeling aligned to SLA and unit economics.
Training Tools
Scalable onboarding and upskilling pipelines — now augmented with AI coaching and just-in-time learning.
Quality Assurance Tools
QA frameworks and calibration workflows, extended by automated evaluation and sentiment-driven sampling.
Operations Leadership
Senior management of multi-site, multi-channel operations with ITFM cost governance at enterprise scale.
Production GenAI, not demos
Applied generative AI engineered for enterprise reliability — retrieval, autonomy, conversation, and cost control.
RAG
Retrieval-augmented generation over private knowledge bases — grounded answers with source lineage and zero data egress.
Agentic AI
Multi-step agents that plan, call tools, and execute operational workflows with human-in-the-loop guardrails.
Chatbots
Conversational assistants tuned for contact-center intents — escalation-aware, context-aware, audit-ready.
Tokenomics
Cost-bounded inference through local LLMs and LiteLLM routing — modeling token spend, latency, and quality trade-offs.
Tools / how the pieces connect
AI tools meet contact-center needs. Outcomes follow.
Hover any node to see how retrieval, agents, and chatbots connect to governance, quality, and workforce management — producing measurable, scalable results.
AI Tools
Contact Center Needs
About
The rare intersection
Jonathan is a Senior Leader with more than 20 years of experience in Workforce Management inside large global contact centers — the kind of operations where thousands of seats, dozens of channels, and tight unit economics meet every day.
He is also pursuing a PhD researching Generative AI adoption in knowledge work — measuring the structural drivers, productivity outcomes, and governance conditions that make AI stick rather than stall.
That combination is the point. Jonathan does not build AI in a vacuum, and he does not run operations the way they were run a decade ago. He connects retrieval, agents, and governance to forecasting, quality, and staffing — and turns the result into measurable, scalable advantage.
Doctoral research
Generative AI adoption in knowledge work — measured with structural equation modeling.