Local or cloud, retrieval or tuning, agent or script. Each is a fork with a cost, picked by a team that has not walked either road, with no budget for a hire and risk that shows up late.
Local model or cloud. Retrieval or fine-tuning. Agent or a script. Each is a fork with a cost, and the team has to pick one without having walked either road.
The project does not justify hiring an AI engineer, and it cannot afford to learn everything the slow way either.
A permission gap, an invented answer in front of a customer, a bill that doubles. Each was visible in the design to someone who had seen it before.
We review your designs, answer the questions as they come up, and sit in the meetings where the choices are made. Your team does the building. The mistakes we have already made stay made.
Three parts: know your situation, be there for the decisions, leave the knowledge behind.
Your project, your stack, your team, in one week.
Decisions answered when they come up.
Your team keeps what it learned.
Four phases, sized to your project.
One week to know the ground.
The decisions ahead and when they fall.
In the build, at the cadence agreed.
The knowledge stays.
Better decisions, sooner, with a record of why.
Model, runtime, retrieval versus tuning: chosen with someone who has seen the alternatives fail. No rebuild in month four.
Hours or days a month from an engineer who does this daily. The project gets the expertise it needs at the size it can afford.
Every decision written with its reason. Every pattern left as a checklist. The next project starts from what this one learned.
| Side by side | On your own | With guidance |
|---|---|---|
| Speed | Each decision researched from scratch | Answered within the week, often the day |
| Technical decisions | Best guess | Chosen from alternatives already tried |
| Risk | Discovered at release | Named in the design review |
| What the team learns | From incidents | From reviews, written down |
| Cost | A senior hire or a slow project | Hours a month, fixed |
Any AI build your own team is doing: retrieval over documents, agents, fine-tuning, local models, forecasting, churn. It fits best when the team can build but has not built this before, and when the project is too small to justify a hire.
Every decision is written with its reason and kept in your repository. Every pattern used leaves a checklist. Before the engagement ends we run a session per pattern with the engineers who will maintain the system, and review their runbook and tests.
It is sized per project: a fixed number of hours or days a month, with a weekly review, a channel for daily questions and attendance at the decision meetings. Blockers are answered within a day. The scope and the price are set in the planning phase and revisited monthly.
By the decisions: each one recorded with its reason and, later, with whether it held. By the risks: those named in assessment and whether they were retired before release. And by the team: whether they run the next decision without us.
We are a small team of senior specialists. We pick the right model and the right layer, and we build the least machinery that does the job. You get a call with an engineer, not a sales deck.
