How I Work
Senior Product Judgment. Hands-On Delivery. One Accountable Partner.
Most AI builds end with a demo that never reaches production. Mine end with a shipped product, clean architecture, and code your next hire can read.
Choosing the Right Delivery Model
The right model depends on the shape of the work. Here's how three common paths compare on timing, delivery ownership, and what you're left with at the end.
Independent Freelancer
- Timeline to value
- Often days to weeks
- Who delivers
- The individual you select
- Commitment
- Usually hourly or task-based
- What you get
- Flexible implementation capacity
- Ideal when
- You have narrow, well-defined work and can direct it
Product Studio or Agency
- Timeline to value
- Weeks to kickoff
- Who delivers
- A multidisciplinary delivery team
- Commitment
- Usually project or team based
- What you get
- Design, engineering, and project delivery capacity
- Ideal when
- You need several disciplines operating in parallel
Direct AI Product Build
(What I Do)
- Timeline to value
- Week 1
- Who delivers
- Ken leads the critical product and engineering work
- Commitment
- Fixed-price sprint or outcome-based retainer
- What you get
- A shipped AI product, modern-stack architecture, docs you can actually use
- Ideal when
- You need an AI product shipped, not managed
How I Operate
Four non-negotiables that shape every build. These aren't aspirations — they're the working agreements I put into the written proposal from day one.
I Build for Production, Not the Demo
Your product has to work when real users hit it. I build with that from day one: grounded AI, sensible architecture, tests that matter, and docs your team can use.
Modern Stack, Chosen to Ship
TypeScript, Next.js, Supabase, Vercel AI SDK, pgvector. A tight, modern toolkit picked to move fast and stay maintainable, not to pad a diagram.
I Leave Your Codebase Better Than I Found It
If you bring me into an existing product, you shouldn't inherit more mess. I refactor as I go, document what I touch, and leave patterns your team can follow.
Measured on Outcomes, Not Hours
In scoping we agree on what "done" looks like. That's how I know when to hand off, and how you know you got what you paid for. Priced on outcomes and retainers, never hourly timesheets.
Ways to Engage
Start with clarity when the product is still taking shape, move into a focused build, and continue with ongoing product engineering when the work earns a longer life. You can enter at the stage that matches what you already know.
Product Discovery & Architecture Sprint
Start here when the right build is not clear
Best For
You have an idea, an early prototype, or an existing system, but the right build is not clear yet.
Commitment
Fixed-fee · normally five business days
What You Get
A standalone decision document covering the current state, user and workflow, smallest valuable product slice, architecture, risks, prioritized delivery plan, and fixed-scope build proposal.
AI Product Build Sprint
The primary build engagement
Best For
You need a working AI product or feature shipped in weeks.
Commitment
Fixed scope or milestone-based · typically four to eight weeks
What You Get
A deployed working product or high-value product slice, delivered against milestones with maintainable code, infrastructure, documentation, and a clean handoff.
Ongoing Product Engineering
Continue after launch or enter with a defined product
Best For
You need ongoing principal-led delivery for an existing product without making a full-time senior hire.
Commitment
Outcome-based monthly engagement · async-first
What You Get
A defined monthly product workstream with principal-level architecture, critical engineering, shipped features, and additional delivery capacity introduced transparently when the scope supports it.
What Does an AI Product Engineer Actually Do?
Most engineers do one part well. I build the whole product — frontend, backend, and the AI pipeline — which means fewer handoffs, fewer gaps, and a product that actually works for your users.
As Your Product Engineer, I:
Validate the “Why” Before Writing Code
- •Work with you to turn a fuzzy idea into a scoped, buildable product
- •Challenge features that don't map to a real user problem
- •Define what “done” looks like up front, so there's no scope creep at the end
Build End-to-End, Front to Back
- •Frontend: TypeScript, React, Next.js (App Router, Server Components)
- •Backend: Node.js and Python APIs, Supabase (Postgres, Auth, RLS, Storage)
- •Integrate third-party APIs, webhooks, and data pipelines without duct tape
- •Deploy on Vercel with preview environments and async orchestration (Trigger.dev/Inngest)
Build AI/RAG Features That Actually Work
- •Design the ingestion pipeline: chunking, embeddings, vector storage (pgvector)
- •Build the retrieval and ranking layer so answers are grounded, not hallucinated
- •Orchestrate models with the Vercel AI SDK (Claude/GPT), model-agnostic
- •Add cost guardrails and observability (LangSmith/Braintrust) so you know what AI costs and when it misbehaves
Iterate on Metrics, Not Gut Feel
- •Instrument features with usage telemetry so we know if they work
- •Set up feature flags and A/B testing to ship safely to a subset of users
- •Bring you data after launch, not just a “shipped” ticket
Bridge Technical and Business Goals
- •Translate budget, timeline, and constraints into tradeoffs you can decide on
- •Flag when a feature will create technical debt, and whether it's worth it for your stage
- •Write code your next engineer can maintain
How This Compares to Other Options
| What You Need | Typical Freelancer | Dev Agency | Ken (AI Product Engineer) |
|---|---|---|---|
| Full-stack AI product | Often focused on one part of the stack | A coordinated team of specialists | One accountable lead across the full product |
| AI/RAG that works in production | Depends on the specialist's experience | A broader team and delivery plan | Grounded, deployed, fixed price |
| Shipped MVP in weeks | Depends on scope and availability | Kickoff in weeks, ship in months | Working product in weeks for a focused scope |
| Architecture decisions | Varies by individual experience | Technical lead works through the delivery team | Decades of judgment, in the room |
| Docs your team can use | Maybe | Formal handoff doc | Runbooks and inline docs, built as I go |
| Who's actually building | The individual you select | The studio's assigned delivery team | Ken leads the critical work and remains accountable |
Ready to ship your AI product?
30-minute intro call. No pitch, a real product conversation about your idea, timeline, and whether I'm the right builder.