Technology · HOLD.co portfolio
data.co — the data behind the model.
data.co prepares the data AI systems are trained, tuned and tested on. One team sources and cleans training data, labels it with human annotators, and builds the pipelines and evaluation sets that keep a model accurate once it is in use.
A HOLD.co company
What the models learn from.
data.co sits in the HOLD.co technology segment alongside LLM.co, hardware.co, DEV.co and ERP.io. LLM.co builds private language models and hardware.co supplies the machines they run on; data.co supplies what those models are trained and measured on.
Most AI projects that disappoint do so because of their data, not their model: the wrong examples, inconsistent labels, or no way to tell whether a change made things better. data.co treats the dataset as the product — specified, versioned and tested — so the model built on it has something solid underneath.
What it does
Three parts, one dataset.
Sourcing, labeling and evaluation are usually handled by different vendors. data.co does all three, so the people who label the data also know what the model needs from it.
Datasets built for the model, not just collected.
Sourcing, cleaning, de-duplication and formatting of the text, documents, images and records a model learns from — with licensing and provenance tracked for every source, so the dataset can stand up to review.
Human annotation with written guidelines.
Classification, extraction, ranking and preference labeling by trained annotators working from written instructions, with review passes and agreement checks so quality is measured rather than assumed.
Data that keeps flowing after launch.
Repeatable pipelines that move an organization's own data into fine-tuning and retrieval, plus evaluation sets that show whether a model is getting better or worse as it changes.
Where it's used
Data work for every stage of an AI system.
From the first fine-tune to the evaluation set that gates every release afterwards.
Our thesis
As models become interchangeable, the lasting advantage is the data an organization trains and tests them on — and that data is worth building properly.
Scope a dataset with data.co.
Tell data.co what the model needs to do, what data you already have and how you will judge the result. The team replies with a proposed dataset and the questions that decide the scope.
Interested in the platform or the portfolio?
Whether you're preparing data for an AI project or you're an owner, operator, or investor exploring a deal with HOLD.co, start a confidential conversation.