ENGAGEMENT · MODE 02 · BUILD
Build your AI infrastructure properly. Once.
Most companies bolt AI onto their stack the way they bolt analytics onto a v1 product: fast, fragile, and impossible to evolve. Six months later they are stuck debugging hallucinations, rewriting evals, and explaining to the board why the AI roadmap is behind schedule. The fix is to build the infrastructure properly from the start, and then keep it maintainable.
The shape relationships move to once your team is operating well. Build the system. Properly. Once.
Book a call◆ Who this is for
AI-native startups past the demo phase. Established companies with an AI mandate from leadership and an engineering team that has never shipped production LLM systems. Founders who have shipped a prototype and now need to scale it without burning down.
- ◆You have a working AI prototype and no path to production hardening
- ◆You are hitting latency, cost, or quality walls in your current implementation
- ◆You need to add new AI features and the existing architecture will not bend
- ◆Your team is strong on app development but inexperienced with LLM systems at scale
◆ Where this sits in the relationship
This shape often comes second. A team that has been trained well will quickly identify the systems that need to be built properly — the RAG layer that will not survive scale, the eval framework that does not exist yet, the agent orchestration that was hacked together for the demo. The build engagement is where the relationship moves from skill transfer to infrastructure ownership. For some companies it is also where the relationship starts; the training shape happens later, or not at all.
◆ What we'd do
We start with architecture. Not slides: a working design document that maps your product requirements to a concrete system. Which models, which infra, which orchestration patterns, which evaluation framework, which observability stack. Defensible choices with the tradeoffs spelled out.
Then I build it. Whether it is a RAG system over your customer data, a multi-agent workflow for a complex domain, an evaluation framework that keeps quality from drifting in production, or full MLOps infrastructure for continuous deployment: the implementation is mine. Built end-to-end, production-grade, no handwaving.
Throughout, your team is in the loop. Weekly demos. Architecture sessions. Code that is documented well enough for them to own it after I leave. The goal is a system your engineers can maintain, extend, and be proud of.
◆ What you get
Architecture document
The kind technical investors and engineering leads actually read. Defensible, opinionated, annotated.
Production AI infrastructure
Shipped end-to-end. RAG, agents, evals, MLOps: whatever the product requires.
Evaluation framework
Quality is measured, not hoped for. Automated evals built in from the start.
Observability and cost monitoring
You see what the system is doing and what it costs before it surprises you.
Clean handoff
Documentation, knowledge-transfer sessions, and a team that can own the system.
◆ What this is not
This is not a prototype factory. It is not a slide-ware engagement. It is not me writing a strategy document and disappearing. If you want a deck about AI strategy, that is a different engagement entirely. If you want working infrastructure that ships to production and your team can own, this is the engagement.
◆ Relevant work
TraceLayer: a compliance automation SaaS built in 21 days, shipped to paying customers, still running.
Read the case studyLet's talk about this.