How We Work Together
Three engagement models depending on where you are and what you need. Every engagement starts with the same question: does AI actually make sense for this problem?
AI Assessment
Before you build anything, figure out if you should. An assessment gives you clarity on whether AI is the right solution, what it would take to implement, and what the realistic ROI looks like.
Sometimes the answer is "don't build this with AI." That's valuable information that saves you from wasting months and money on the wrong approach.
- Use case evaluation and prioritization
- Technical feasibility analysis
- Build vs. buy recommendation
- Implementation roadmap
- Honest assessment of what won't work
Good fit if:
- • You're exploring AI but not sure where to start
- • You have a specific idea but want validation
- • You've been burned by AI hype before
- • You want an honest second opinion
Timeline: 1-2 weeks
Deliverable: Written report with clear recommendations
Implementation Oversight
Your team builds, I guide. Expert oversight keeps your AI project on track and catches problems before they become expensive mistakes.
This works well when you have developers who can execute but need AI-specific expertise to make the right architectural decisions.
- Architecture review and guidance
- Technical QA on AI components
- Vendor evaluation and coordination
- Weekly check-ins and async support
- Course correction when things go sideways
Good fit if:
- • You have developers but lack AI expertise
- • You're working with a vendor and need oversight
- • You want expert guidance without full outsourcing
- • Your project is already underway but stalling
Timeline: Ongoing monthly engagement
Commitment: Typically 3+ months
Full Build
End-to-end AI implementation. I build it, you use it. This is the right choice when you need production AI and don't have the team to build it yourself.
Focus is on shipping something that works, not on impressing anyone with complexity. Clean, maintainable, production-ready.
- Voice AI systems (cloud or self-hosted on GPU)
- Full pipeline: STT → LLM → TTS with sub-300ms latency
- Multi-tenant architecture with per-client customization
- Training and handoff
Good fit if:
- • You need production AI, not a prototype
- • You don't have the team to build it in-house
- • You want direct access to the builder
- • You value shipping over complexity
Timeline: Typically 4-12 weeks depending on scope
Includes: Post-launch support and documentation
Not Sure Which Fits?
Start with a conversation. 30 minutes to understand your situation and figure out the best path forward. No commitment.