About Datificial
Build the data layer before asking the model to compensate for it.
Thesis
The missing product is often not another model.
It is the data the system can actually rely on. AI teams can access capable models quickly. The harder part is turning scattered files, databases, APIs, and documents into a coherent product with stable identifiers, semantic structure, provenance, quality gates, delivery, and refresh. Datificial focuses on that layer.
The company combines data engineering, machine learning, retrieval, and controlled language-model use without treating any one method as the answer. The objective is a reproducible data product that can be consumed by software, analysts, or agents.
Founder-led execution from source to production handoff.
Datificial combines production data engineering, machine learning, retrieval, and reliable system design in one accountable practice. Responsibility for the product contract stays with the person who scopes it, from schema design through delivery and handoff.
The same engineering discipline applies throughout: explicit state, bounded interfaces, measured quality, and reproducible operation.
Small, technical, and accountable.
Datificial begins as a founder-led practice. The person scoping the product remains close to schema design, transformation choices, quality evaluation, and delivery. Specialized collaborators or infrastructure may be added when a project requires them, but responsibility for the product contract remains explicit.
01
Use-case first.
The source does not define the product. The consumer does.
02
Evidence over abstraction.
Every claim about quality or enrichment must be tied to tests, samples, or provenance.
03
Reusable delivery.
Each engagement should produce a maintained asset, not a one-time pile of scripts.
Datificial focuses on data products. Related work in model post-training and broader application engineering is operated separately so each engagement has a clear technical owner and outcome.