You will get a review of your LLM pipeline architecture: evals, routing, cost control
Project details
Your LLM pipeline works, mostly. But you are not sure it will hold up as volume grows, costs are harder to predict than they should be, and prompt changes ship on hope because nothing measures whether they helped.
This is a focused two-week review of the architecture: model routing, eval coverage, fallback paths, queue design, and cost per unit of output. You get a written review doc, a gap list for eval coverage, and a 45-minute call to walk through it.
Reference point: my own production pipeline routes extraction and prose to different models, gates every output with an eval suite before anything persists, fails over automatically when the primary worker dies, and runs at catalog scale for under a dollar a month in model spend. Day to day I work with Claude, OpenAI, Llama, Whisper, and Apple Intelligence.
Background: 25 years of design-led product work, Apple Design Award winner, a native macOS app live in the Mac App Store.
This is a focused two-week review of the architecture: model routing, eval coverage, fallback paths, queue design, and cost per unit of output. You get a written review doc, a gap list for eval coverage, and a 45-minute call to walk through it.
Reference point: my own production pipeline routes extraction and prose to different models, gates every output with an eval suite before anything persists, fails over automatically when the primary worker dies, and runs at catalog scale for under a dollar a month in model spend. Day to day I work with Claude, OpenAI, Llama, Whisper, and Apple Intelligence.
Background: 25 years of design-led product work, Apple Design Award winner, a native macOS app live in the Mac App Store.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Content Creation, Automatic Speech Recognition, Conversational AI, Natural Language GenerationAI Development Language
PythonAI Tools
Hugging FaceAI Models
ChatGPT, GPT-4, LLaMA, WhisperWhat's included $1,500
These options are included with the project scope.
$1,500
- Delivery Time 14 days
- Number of Revisions 1
- Model Documentation
- Model Testing & Optimization
About Aaron
Product Designer + Builder | Production AI | Apple Design Award
Chattanooga, United States - 7:47 am local time
Most recent build is a production AI pipeline, the Steadcast Knowledge Engine. Queue-based Whisper transcription on a self-hosted GPU, an eval suite that gates every model output before it persists, mixture-of-models routing, cost ceilings, monitoring. It runs autonomously at catalog scale for under a dollar a month in model spend. That is the difference between a demo and production, and it is the standard I hold client work to.
What I do for clients:
- AI features that have to work in front of real users (eval gates, fallbacks, cost control)
- Native macOS and iOS product design and build (SwiftUI, App Store review passed)
- Product strategy from someone who has run a P&L, not just a design file
- Rescue work: "we built it with AI tools and it's broken" is a brief I like
I have been the non-technical founder trying to evaluate a technical quote. I will not do that to you. Plain language, working software, no jargon walls.
Steps for completing your project
After purchasing the project, send requirements so Aaron can start the project.
Delivery time starts when Aaron receives requirements from you.
Aaron works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff call
30 minutes. You walk me through the pipeline and where it hurts; I confirm scope and access.
Architecture review
I work through routing, eval coverage, fallbacks, queue design, and cost per output against production standards.