1 week
AI Opportunity Audit
Map the workflow, assess data readiness, estimate ROI, and decide whether AI is worth building into the process.
AI engineering
Production AI systems for teams ready to move past prototypes.
I embed with founders, operators, and technical teams to design, build, and deploy AI agents, RAG systems, and workflow automation around real data, real users, and measurable business outcomes.
10+ years in data science and AI engineering RAG, agents, MLOps, full-stack analytics North America-based, remote globally
Best fit: AI-ready teams with messy workflows, scattered knowledge, support or research bottlenecks, and internal tools that need automation.
Production workflow AI deployment path
01Map workflow
Owner, task, inputs, success metric.
02Connect data
Docs, tools, permissions, retrieval.
03Build system
Agent, app, review loop, handoff.
04Operate
Evals, monitoring, cost, iteration.
ROI before build Evals from day one Human review Observable launch
The gap
Most AI efforts stall between the impressive demo and the operational system.
The hard parts are workflow fit, data access, evaluation, reliability, adoption, and integration with the tools people already use.
AI pilots without measurable ROI or a workflow owner.
Knowledge trapped in documents, tickets, CRM notes, Slack, PDFs, and spreadsheets.
Support, research, and operations teams repeating manual knowledge work.
Prototypes without evals, monitoring, permissions, fallback behavior, or adoption plans.
What I build
AI deployment work that connects strategy, code, and operations.
Each engagement starts with the smallest useful next step, then builds toward a reliable production workflow when the business case is clear.
2 weeks
AI Prototype Sprint
Turn one high-value workflow into a working prototype connected to real inputs and evaluated against useful examples.
4 to 8 weeks
Production AI Workflow Build
Ship a reliable AI workflow with integrations, monitoring, review loops, deployment, and handoff documentation.
Selected work
Live AI systems mapped to business workflows.
These projects demonstrate grounded knowledge systems, safe agents, workflow automation, evaluation thinking, and production-ready operating tools.
Workflow assessment
AI Workflow Readiness Scorecard
Teams know AI matters but cannot rank which workflows are worth building first.
- Buyer
- Founders, COOs, CTOs, and operators
- Business problem
- Teams know AI matters but cannot rank which workflows are worth building first.
- Solution pattern
- Interactive diagnostic that scores workflow fit, data readiness, risk, guardrails, and ROI potential.
- Evidence
- Live assessment app with explainable scores, deterministic fallback, tailored recommendations, and protected submissions.
Live case study
Frontline AI Business Assistant
A client-facing assistant that helps service businesses answer routine questions, capture requests, book appointments, and keep staff focused on higher-value work.
- Buyer
- Business owners, office managers, brokers, practice managers, front desk leads, and service teams.
- Business problem
- Many teams lose time and opportunities because calls, messages, bookings, follow-ups, and policy questions are handled manually across too many places.
- Solution pattern
- A guided assistant that gives approved answers, collects the right details, creates follow-up items, and gives the team a clear view of client conversations and requests.
- Evidence
- Reviewable demo with multiple office types, public demo access, client chat, appointment capture, request review, business activity dashboard, and readiness assessment handoff.
- Outcome
- Faster replies, cleaner intake, fewer missed inquiries, better follow-up, and less repetitive admin work.
Working model
Diagnose, prioritize, prototype, evaluate, deploy, improve.
01
Diagnose workflow and data reality
02
Prioritize by ROI and risk
03
Prototype against real inputs
04
Evaluate quality and failure modes
05
Deploy with review and observability
06
Improve from usage signals
Readiness scorecard
Not sure where AI can create value?
Take the AI Workflow Readiness Scorecard to assess workflow fit, data readiness, risk, guardrails, and ROI potential before you commit to a build.
Scorecard outputlive
Workflow fit Data readiness Guardrail needs Build priority
Writing preview
Practical notes for operators and technical teams.
AI Deployment
What a Production AI Engineer Does for Mid-Sized Companies
Learn what a production AI engineer does, when to hire one, and how they turn AI workflows, RAG systems, and agents into reliable business systems.
AI Deployment
How to Pick an AI Workflow Automation Project Worth Building
A practical framework for choosing AI workflow automation projects with clear ROI, usable data, manageable risk, and realistic implementation paths.
AI Deployment
AI Prototype to Production: A Practical Checklist for Business Workflows
Use this AI prototype to production checklist to plan data access, evaluation, permissions, monitoring, cost controls, rollout, and workflow ownership.
RAG and Knowledge Systems
RAG Evaluation: How to Test AI Knowledge Assistants Before You Trust Them
A practical RAG evaluation framework for testing retrieval quality, answer faithfulness, citations, refusal behavior, latency, cost, and business usefulness.
Agents and Workflow Automation
How to Build an AI Agent That Does Not Break Your Operations
A practical guide to AI agent implementation with tool permissions, human approvals, evals, logging, rollout controls, and operational risk management.
AI Deployment
The AI Opportunity Scorecard: A Practical Framework for Operators
Use this AI opportunity scorecard framework to rank AI workflow ideas by pain, data readiness, risk, integration effort, ROI, and implementation path.