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.
Igor Bogdanov is an AI systems researcher and software builder with more than 15 years of production software and systems experience. He has built data-intensive products, experimental AI infrastructure, agent harnesses, evaluation systems, retrieval pipelines, and reproducible research artifacts across software, machine learning, and distributed systems.
His research on compound LLM agents studies how context, system structure, adaptation, and evaluation affect reliability and cost. Datificial applies the same engineering discipline to the data layer: 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.