Working with an Agentic Product Architect — FAQ

The 4 offer tiers, credentials, and how engagements actually run.

✦FAQs

Frequently Asked
Questions

Everything you need to know about the product and billing. Can't find the answer you're looking for? Please chat to our team.

What are the four offer tiers?

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Four shapes, four price points, picked by your situation:
• Vibe-Built MVP — $15–30K, 3 weeks. Solo-founder lane.
• AI Product Sprint — $30–60K, 4–6 weeks. Fractional founder for the build.
• Production Audit + Rescue — $3.5K audit / $25–80K rescue. For agents that shipped and aren't behaving.
• Agentic Product Retainer — $15–30K/mo, 14-month average. Fractional CPO + Architect, embedded.

Pick based on whether you're validating, building, fixing, or scaling.

What does 'Agentic Product Architect' actually mean?

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It means I do the work that's normally spread across four people on an AI product team — research, product spec, system architecture, UX, AND the build itself. Most agentic AI projects fail in the seams between disciplines: the PM specs an agent the engineer can't ship; the engineer ships something the designer would never have approved. I went through all of those roles (8 years UX, then Principal PM, then Solutions Architect, now founder + builder) so I don't have those seams. One head, no handoff failures.

Do you work with non-technical founders?

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Yes. I spent 8 years in UI/UX and 5 years as Principal PM before becoming an Architect, so plain-English communication is the foundation, not the bolt-on. I explain AI in business terms and focus on the specific workflow we're shipping, not the framework. Most of my MVP and Sprint clients are non-technical founders.

What's your Fortune 500 credential?

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In 2022 I architected an AI contact center for a Fortune 500 retailer:
• CSAT +40%
• Average hold time −50%

Plus three other Fortune 500 wins as Solutions Architect:
• Fintech fraud reduction −60%
• Healthcare ML — 25% faster diagnostics
• Retail predictive inventory — 30% supply-chain efficiency

Can you help optimize our existing LLM API costs?

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Yes. Recent client: $14K monthly LLM bill cut to $5K in nine days with three lines of code. The three-layer stack: semantic caching, prompt compression, and intent-aware model routing — all eval-gated so quality doesn't silently regress. Standalone engagement starts at $3,500; often bundled inside a Sprint or Retainer.

What frameworks and tools do you use?

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Current production stack:
• Orchestration: LangGraph (default), CrewAI, custom Python
• Models: Claude Sonnet + Haiku, GPT-5o, self-hosted (Qwen, DeepSeek)
• Evals: LangSmith, LangFuse, Braintrust
• Integration: MCP-native — every new build is MCP-first
• RAG: pgvector + Cohere reranking
• Voice: Vapi, LiveKit + ElevenLabs Turbo
• App layer: Next.js, FastAPI, Supabase