One operating system. Eight decision-makers.

You cannot quantify the data asset on the balance sheet conversation. Single-point valuations collapse under diligence, intangibles get impaired, and investor questions go unanswered.

Capital decisions, M&A premiums, and impairment positions made on indefensible data values.

KRDS composes cost, market, income, replacement, and option layers into P10·P50·P90 bands with full audit trail. EARM governs what is recognized; capital readiness scoring assembles the disclosure pack.

Valuation memo with method weights, sensitivities, and a defensible band.

Recognize at base, hold at floor, defer, or impair - with evidence.

Catalog maturity is low, assets are invisible to the business, data products are unknown, and governance is reactive.

Strategy depends on a data estate no one can describe end-to-end. Investments in catalog, lineage, and AI fail to translate into board-visible value.

Command-grade registry with passporting, evidence tags, lineage, fusion matrix, and product pathways. Each asset gets an identity, a steward, and a route to revenue or capital.

Asset registry with passports, evidence tags, and product candidates.

Promote, repair, pilot, or shelve every candidate asset.

Rights are assumed rather than evidenced. Cross-border transfers, consent chains, and contract restrictions are unclear. Monetization plans run ahead of legal posture.

Regulatory exposure, contract breach, consent failure, and reputational risk from data used outside its lawful envelope.

Rights gate enforces existence, ownership, consent, provenance, quality, transferability, buyer demand, and valuation support. WITHHELD and EXCLUDED ledgers are explicit and auditable.

Rights posture per asset and per use case with WITHHELD / EXCLUDED ledger.

Cleared for internal use, monetization, quarantine, or destruction.

Untracked access, weak lineage, leaky AI/RAG pipelines, and exhaust streams that bypass security review.

Cyber exposure, AI model leakage, breach scope ambiguity, and audit findings on data lineage and access.

Provenance chains, access evidence, clean-room and enclave pathways, RAG risk review, and a red-team ledger that attacks claims before adversaries do.

Control map per asset with provenance, access evidence, and adversarial findings.

Harden, isolate, restrict use, or retire.

Under-used data, unclear competitive moat, partnership potential left on the table, and no map of monetizable assets.

Competitors and partners monetize the same exhaust first. Strategic narratives - AI, platform, ecosystem - lack a defensible asset base.

Forensic exhaust discovery, fusion of internal datasets, external enrichment options, and a monetization studio that routes assets to buyers, partners, and indices.

Revenue map of hidden, fused, and enrichable assets with buyer pathways.

Build, partner, license, or hold back.

Diligence reports lean on management claims. Data product revenue is unaudited, rights are unclear, and the asset base behind AI strategy is impossible to verify.

Overpaid premiums, post-close impairments, hidden rights liabilities, and AI moats that do not survive integration.

Investor-ready data asset pack: registry, evidence file, rights posture, valuation band, stress-test outputs, and red-team assurance - assembled to diligence standard.

Investor-ready data asset pack with full audit trail and stress outputs.

Bid, re-price, condition, or walk.

Cyber and warranty exposure rests on insured claims about data quality, controls, and lineage that are rarely independently evidenced.

Mispriced premiums, surprise losses, contested claims, and reserves set against fog.

Insurance and warranty readiness reviews: risk registers, control evidence, red-team testing, and data quality scoring against underwriting criteria.

Risk register with evidence, control map, and adversarial findings.

Quote, decline, condition, or re-price.

Training data, eval sets, RAG indexes, and drift logs are uncounted, ungoverned, and unvalued - even though they drive model lift.

Model fragility, eval drift, ungoverned RAG sources, and AI strategy that cannot point to the asset base it depends on.

AI-native valuation of training sets, eval sets, vector stores, and RAG logs. Model utility scoring links data assets to measured lift and governance posture.

AI asset map linking training, eval, and RAG assets to model lift and governance.

Invest, govern, retire, or replace per asset.