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Full-stack AI products, built end to end

AI products that actually ship.

Architecture and development for LLM-powered applications: eval-first pipelines, production APIs and polished front-ends, from first prototype to production.

ohmeow — zsh
$ ohmeow ship \
  --product "your-ai-product"
Wayde Gilliam · Founder, ohmeow · 25+ years shipping software
The gap

The demo works. Getting it to production is the hard part.

WHERE AI PROJECTS STALL evals · APIs · data · deployment · monitoring IDEA PROTOTYPE PRODUCTION
01 · Quality

Quality is a feeling

Without evals, nobody can say whether the last change made the model better or worse.

02 · Engineering

The prototype doesn't scale

A notebook and a chat box aren't a product. Auth, APIs, data, deployment and monitoring are.

03 · Ownership

The work is split up

ML, backend, frontend and DevOps hand off to each other, and the product stalls in between.

The approach

One engineer. The whole stack.

ohmeow designs, builds and deploys LLM-powered products end to end, so nothing gets lost between handoffs and every layer is built to work with the one next to it.

● One owner, interface to infrastructure

Interface

what users touch
VueQuasarReactNext.js

API

production services
FastAPINode.js

Intelligence

the AI core
LLMsRAGAgentsGuardrailsEvals

Platform

data & delivery
PostgreSQLDockerCloudCI/CD
Services

Three ways I can help.

Build the AI product, build the application around it, or help your team choose and learn. Most engagements start with one and grow into another.

AI & ML application development

  • LLM apps, RAG and agents
  • NLP workflows
  • Forecasting and classification models
  • Eval-first pipelines

Full-stack engineering

  • Vue, Quasar, React and Next.js front-ends
  • FastAPI, ASP.NET Core and Node.js back-ends
  • PostgreSQL, Docker and cloud deployments

AI strategy & enablement

  • Technical evaluations and benchmarking
  • AI-driven product architecture
  • Team training and workshops
How we work

Weeks, not quarters.

01

Discover

We scope the problem, the users, and what “good” looks like, then agree on a plan measured in weeks, not quarters.

02

Prototype

A working slice of the product, fast. Real data, real users, real feedback before big investments are made.

03

Evaluate

Evals and error analysis drive every iteration, so quality is something we can see and improve.

04

Ship & support

Deployed, monitored, documented and handed off cleanly, with ongoing support when you want it.

ohmeow — engagement.log
Eval-first AI

Quality you can see.

Evals and error analysis drive every iteration, so quality is something we can see and improve, not just feel.

✓

Measure before prompting. Success is defined and measured before the first prompt is written.

✓

Fix what the data shows. Error analysis on real traces decides what to work on next.

✓

Done this for the tools themselves. Redesigned the evals and observability experience for PydanticAI and Logfire.

The eval loop runs every iteration
Clients & employers

From startups to enterprise.

Education · Safety

Safer Schools Together

Architect and lead developer of DTAM, an AI-powered threat assessment platform for schools.

Evals · Observability

PydanticAI / Logfire

Redesigned the evals and observability experience.

LLM tooling

Answer.AI

Contributed to core libraries and tutorials for LLM tooling.

ML education

Weights & Biases

Educational content on fast.ai and blurr with Hugging Face transformers.

Agriculture

Agrilynk

A production AI platform for real-time agricultural insights.

Higher education

UC San Diego

Survey and analytics platform, a qualitative-data AI pipeline and HR forecasting models.

Wayde Gilliam at his desk
Founder · ohmeow.com
Who you're hiring

Wayde Gilliam

Founder of ohmeow and a full-stack and AI/ML engineer. Author of blurr, the library for training Hugging Face transformers with fast.ai, and a fast.ai community leader.

25+
years shipping
10+
companies served
1
owner, end to end
PythonPyTorchfast.aiHugging FaceLLMs & evalsFastAPIVue / QuasarReact / Next.jsPostgreSQLDocker
Next step

Let's ship your AI product.

Start with a discovery call. We'll scope the problem, define what good looks like and agree on a plan.

ohmeow.com ↗
github.com/ohmeow linkedin.com/in/wgilliam x.com/waydegilliam
ohmeow — zsh
$ ohmeow ship --product
  "your-ai-product"
✔ shipped  measured, monitored,
  improving
$
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