AI-Native, RAG, Vector Store & Model Utility Valuation
AI investment is justified on narrative because the underlying assets are rarely valued. KRYOS values them - by measured marginal contribution, not assumed strategic worth.
AI-era valuation for the assets that actually drive model utility, reliability, and licensing posture - training corpora, eval sets, embeddings, retrieval indexes, telemetry, and governance artifacts.
Most AI ROI cases attribute value to the model rather than to the data assets that determine its utility. Training corpora, eval sets, RAG indexes, embeddings, and telemetry are treated as plumbing. The result is overpriced model investment, mispriced licensing, governance artifacts that never become assets, and AI valuations that collapse under diligence.
KRYOS catalogs every AI-native asset, attributes marginal utility per corpus and index, recognizes eval and telemetry as first-class governance assets, and elevates model cards, datasheets, and red-team reports from compliance burden to recognized artifact. AI assets enter EARM recognition and KRDS valuation on equal footing with the rest of the estate.
Data and assets reviewed
What the engagement delivers
Training corpus valuation
Marginal-utility attribution across pretraining, fine-tuning, and post-training data.
RAG and vector store valuation
Value retrieval indexes by recall lift, freshness, and rights posture.
Eval and telemetry valuation
Recognize evaluation sets, telemetry, and red-team datasets as first-class governance assets.
AI governance artifact register
Catalog and value model cards, datasheets, red-team reports, and lineage records.
Rights-cleared AI estate
Rights posture per corpus, per use - training, fine-tuning, inference, productization.
Model utility scorecard
Composite scorecard linking AI assets to measured lift, reliability, and licensing optionality.
Artifacts produced
Decisions you can defend
AI investment decisions grounded in measured utility, not narrative.
Defensible value for the data that determines model performance and risk.
Governance artifacts elevated from compliance burden to recognized asset.
The capital, audit, and AI consequences.
AI strategy is now a capital allocation discipline. Boards, investors, and acquirers will increasingly require asset-level evidence for AI claims. Without AI-native asset valuation, every AI investment thesis sits on assumption - and the upside is captured by counterparties who can prove their AI estate, not just describe it.
The data assets that govern model utility and risk.
AI-Native Asset Map
The data assets that govern model utility and risk - and the value drivers they connect to. Hover any node to trace its contribution.
Questions clients ask before engaging.
Do you value the model itself?+
We value the data assets that determine model utility - corpora, eval sets, RAG indexes, embeddings, telemetry, and governance artifacts. Model value is treated separately and reconciled to AI-asset contribution.
How is marginal utility measured?+
Through ablation against documented evaluation sets, telemetry-tagged lift, and KRDS utility-in-use attribution. We refuse to attribute utility on narrative.
Are training corpora always recognizable as assets?+
Only when EARM thresholds are met - including rights to use the data for training and inference. Corpora that fail are explicitly withheld or excluded.
How do you treat hallucination and red-team data?+
Both are first-class governance assets. They reduce risk discount in KRDS valuation and increase reliability score in the model utility scorecard.
Can vector stores be valued separately from their underlying corpus?+
Yes. The index, freshness, recall lift, and rights posture are valued independently of the source corpus - and reconciled in the composite.
Begin with a forensic data asset assessment.
A focused engagement that maps your data estate, scores assets against KRYOS frameworks, and produces a board-ready brief on what to recognize, value, productize, and capitalize.