Products
AI product development
Use-case discovery, model selection, and a production interface wired to the workflow your team already runs. The product is the screen, the eval, and the owner — not a notebook.
AI & Data
Artificial Intelligence.
Custom models, generative assistants, computer vision, and data pipelines — built for live workflows, not a slide that dies after the demo.
The practice
AI a Monday desk can actually run.
United States teams hire this practice when an AI pilot has to become a product an ops desk can run. We pick the job, lock the eval, wire the model to data they already trust, and leave a named owner on the release. Source, prompts, and evals transfer with delivery — we do not hold the model hostage.
The work starts in the workflow, not in a model zoo. Property media, document queues, support threads, listing reviews — if the model cannot sit inside the screen they already open on Monday, it is a demo. We write the job, the score, and the owner before anyone fine-tunes a weight. Then the interface, the permissions, and the review loop follow that sentence.
If the model cannot sit inside the screen they already open on Monday, it is a demo.
- Use-case discovery written as a job an operator can name.
- Eval locked before the first fine-tune.
- RAG, tools, and permissions against systems you already trust.
- Source, prompts, and ownership transfer with the release.
OpenAI
Azure OpenAI
Python
RAG
Vision
Agents
On this desk.
Stills from the practice. The work changes. The review does not.
Field media
What it ships.
Ops, product, and data leads who need a model in a live review — property media, documents, support queues — not a slide.
Generative
Generative AI & assistants
Custom copilots, multilingual support agents, and content systems connected to your documents and tools. Grounded answers, named permissions, a desk that can correct the model.
Vision
Machine learning & vision
Forecasting, fraud signals, and computer vision for operations that still run on photos, PDFs, and field media. Detection in the review, not a separate portal.
Data
Data engineering
Pipelines, warehouses, and governance so the model has something true to read — and an audit trail a compliance desk can follow.
How this desk runs.
Name the job. Lock the score. Name the owner.
Name the job
The workflow, the owner, and the constraint — written before anyone fine-tunes a model.
Lock the eval
A score the desk can read. If it cannot be measured, it is not a product.
Wire live data
RAG, tools, and permissions against the systems you already trust.
Ship and watch
A release, a named lead, and a review loop after the demo is over.
After the release.
What a stakeholder can point to once the practice is live.
Detection sits in the live review, not a leftover slide.
Source, prompts, and evals transfer with the product.
A named desk runs the product in US business hours.
What you keep.
The work transfers with the release. Source, files, and a named owner stay with the stakeholder.
A named job and a written eval
Production interface on live data
Prompts, source, and ownership transfer
US-hour coverage after go-live
Next
Brief Artificial Intelligence.
Name the goal, the date, and the stack. A producer replies the same business day.
Start a brief