Turn AI ideas into working systems
I help teams implement practical AI solutions that integrate cleanly into existing workflows.
Most companies don't fail with AI because of models — they fail because of poor integration, unclear scope, and unrealistic expectations.
Typical delivery: 3–10 business days depending on scope and data readiness.
This website provides general information only and does not constitute a binding offer, guarantee, or professional advice. All services are governed by signed agreements.
Engineer-Led Execution
You work directly with the person designing and building the system.
Business-First AI
Every build starts with a clear problem, success metric, and definition of "done".
Production Mindset
Logging, guardrails, documentation, and handover are built-in — not afterthoughts.
How I Help
Clear scope, predictable delivery, and measurable outcomes.
AI Use-Case Audit
Identify realistic, high-ROI AI opportunities across workflows and data.
Done-for-You AI Systems
Design and deployment of production-ready AI workflows.
Ongoing Optimization
Improve accuracy, reliability, and extend features over time.
Is This a Good Fit?
✓ Good Fit If
You have a real operational problem, not just curiosity about AI
You're ready to integrate AI into existing workflows
You value clarity, speed, and practical outcomes
✗ Not a Good Fit If
You want "AI for AI's sake"
You're looking for a generic chatbot or demo
You expect magic without data, process, or effort
Representative Work
Visual references of real-world AI system patterns (anonymized).
Common Patterns Delivered
- Document AI (RAG over PDFs & knowledge bases)
- Internal AI assistants for ops & support
- Workflow automation & reporting
- Decision-support systems
How I Think About AI
I don't see AI as a replacement for people or a shortcut to results. I see it as a force multiplier for clear processes and good judgment.
Most AI failures come from unclear problem definition, poor integration, or unrealistic expectations — not from model quality.
My focus is always on systems that teams trust, understand, and actually use.
Case Notes
Representative examples of real AI systems. Details are anonymized.
Document AI for Operations
Context: High-volume document review slowing operations.
Approach: Retrieval-based AI layered on existing tools with confidence thresholds.
Outcome: Reduced manual effort and faster turnaround.
Focus: integration, trust, and adoption.
Internal AI Assistant
Context: Teams depended on a few people for operational knowledge.
Approach: AI assistant trained on internal docs with guardrails.
Outcome: Faster answers and reduced internal support load.
Focus: reliability over flashy UI.
Workflow Automation
Context: Manual reporting and handoffs caused delays.
Approach: AI-assisted classification + rule-based automation.
Outcome: Cleaner workflows and fewer errors.
Focus: predictable behavior.
Let's Discuss Your Use Case
Share your workflow or challenge. I'll help you evaluate whether AI is the right approach.