Custom AI-driven applications, taken from concept through to deployment by the team that labelled the data.
We build the application on top of the data, and often we built the data too. That order is deliberate. Most projects that stall do not stall on the model architecture; they stall because the training data does not describe the problem the product actually has.
A team that has read a few thousand of your records knows where the labels are thin, which classes collide, and which part of the pipeline is quietly guessing. That knowledge is difficult to hand over in a specification, so we would rather not have to.
Four kinds of engagement. Most real projects are a mix, and the strategy work is often the one that saves the most money.
A model is a compression of its training data, so every ambiguity in the labels becomes a behaviour in the product. A taxonomy with two classes that annotators could not reliably tell apart will produce a model that cannot tell them apart either, and no amount of architecture work fixes it.
Because we do the labelling as a service in its own right, we tend to find these problems while looking at the data rather than after the first evaluation run. When the honest answer is that the dataset cannot support the feature, that is worth hearing before the build rather than after it.
Scoping is a real phase with a real output, not a sales call. If the audit says the project should not proceed as specified, that is a successful audit.
What the application has to do, who uses it, and what would count as working. Written down, because 'improve accuracy' cannot be delivered against.
We look at what you already have before proposing anything: coverage, label quality, leakage between splits, and whether the historical data actually contains the signal the feature needs.
The measurement is built before the model is tuned. Without a held-out set and a metric you agree with, every subsequent result is an anecdote.
The smallest thing that exercises the whole path, end to end, on your data. It is meant to be shown and argued with, not demoed.
Into your infrastructure, with the monitoring that tells you when inputs drift away from what the model was trained on.
Code, datasets, the evaluation harness and the written record of what was tried and rejected. The rejected list saves the next person months.
Who owns the code, the datasets and the trained weights at the end is a contract question, so settle it in the contract rather than assuming it. Raise it in the first conversation; it is a short discussion early and an expensive one late.
Where a project depends on a third-party model or cloud service, we will say so and name it, so that dependency is a decision you made rather than one you discover during renewal.
Yes, and the first thing we will do is audit it. Inherited datasets are normal. What is not normal is trusting one without checking splits, duplicates and label consistency first, because those problems produce evaluation numbers that look good and do not hold.
Yes. That case starts with deciding what data would have to exist, then creating a small amount of it and testing whether the signal is really there before committing to a full dataset. It is slower to start and much cheaper than discovering the same thing after a full labelling run.
Into yours by preference, because it keeps the data where your own compliance regime already covers it. Where that is not possible we will describe exactly what would run where before anything is agreed.
You get told, with the reasoning and what would have to change. An audit that can only ever recommend proceeding is not an audit, and a model shipped on data that cannot support it costs more than the conversation does.
Most projects touch more than one of these. One partner across all of them means the labelling, the preparation and the application are not three vendors blaming each other.
Human-labelled training data across text, image, video, audio and 3D, and the feedback layer for language models: preference data, written demonstrations, rubric scoring, red-teaming.
The work before training: cleaning, structuring and checking datasets so a model has something honest to learn from.
Rendered training data built in 3D instead of collected and labelled: for classes that are rare, expensive to photograph, or cannot leave the building.
