Falkovia provides AI governance diligence for venture capital and private equity firms. We surface the human architecture risk that standard technical diligence never examines, pre-acquisition, and build the governance infrastructure that protects value creation post-close. Confidential, fixed-scope engagements scoped to deal timelines.
Venture capital and private equity firms evaluate AI investments across technology, market fit, and team. What they rarely evaluate is the human governance architecture that determines whether AI creates value or creates liability inside a portfolio company. This is not a compliance concern. It is a value creation variable.
The data from the last 24 months is unambiguous: AI does not fail because of code. It fails because the human systems surrounding it were never designed. Decision rights, accountability structures, workforce readiness, and governance architecture are the variables that determine whether AI investment produces value or produces liability.
For an investment firm, this means the AI value creation assumptions embedded in a deal model are functionally dependent on a variable, human governance architecture, that standard technical diligence does not examine.
1,208 AI bills were introduced across 50 states in 2025, with 145 enacted into law. Colorado's AI Act takes effect June 30, 2026. Texas TRAIGA is live. The EU AI Act classifies multiple sectors as high-risk. For portfolio companies deploying AI in any regulated context, compliance is no longer a future roadmap item.
Organizations with formal AI governance councils reach ROI in 7.5 months compared to 13.5 months without. Successful AI projects allocate 47% of budget to foundations (data, governance, change management) versus 18% in failed projects. Governance is not a cost center. It is the mechanism that converts AI investment into returns.
59% of employees use unapproved AI tools. Among executives, 93%. The average shadow AI breach costs $4.63M, 17% above standard. 86% of organizations are blind to their own AI data flows. This exposure exists inside your portfolio companies today, whether or not it appears in diligence materials.
Do you know which portfolio companies are using AI, how, and under what governance? Can you map AI adoption across your portfolio and identify where governance architecture is absent?
In each portfolio company, who holds authority over AI decisions: approval, restriction, override, and prohibition? Is that documented, or assumed?
Does your standard technical diligence examine human governance architecture (decision authority, oversight structures, accountability mapping), or only the technology stack?
Could your portfolio companies produce AI governance documentation if a regulator asked tomorrow? Would that documentation demonstrate the institutional oversight that state and federal regulators are now requiring?
Is AI creating value in your portfolio, or creating undocumented liability that will surface at exit? Can your portfolio companies demonstrate governance maturity to a future acquirer?
Falkovia's G.U.A.R.D. Framework™ surfaces the human architecture risk standard technical diligence does not price: shadow AI exposure, decision authority gaps, and regulatory defensibility, scoped to IC timelines. Built on forensic methodology and two decades of institutional governance experience. The diligence layer that completes the picture your IC needs. Architecture your portfolio company owns at close.
No dependency. That is the design.Accountable for portfolio-level risk and responsible for ensuring AI adoption across portfolio companies does not create regulatory, reputational, or valuation exposure that reaches the investment committee.
Responsible for operational value creation and accountable for ensuring AI-driven efficiency gains do not introduce governance gaps that undermine the value creation thesis.
Conducting technical and operational diligence on acquisition targets and responsible for identifying AI governance risk before the deal closes.
Leading organizations where AI adoption is accelerating and accountable for governance architecture that protects the company from regulatory, legal, and operational exposure.
Exercising oversight of investment decisions and responsible for understanding whether AI governance risk has been adequately examined and addressed.
The structural model applied to portfolio AI governance. Stress-tests whether governance holds when scrutinized.
Documented boundary for every high-risk AI workflow inside a portfolio company.
Fifty-question protocol for pre-acquisition assessment, organized across Foundation, Intermediate, Advanced, and Expert tiers.
Department-by-department audit of approved and unsanctioned AI tools with data exposure classification.
Identity threat assessment measuring the psychological dynamics that determine adoption success.
Cross-framework compliance register mapping controls to NIST AI RMF, ISO/IEC 42001, EU AI Act, and active state laws.
“The technical stack is 10%. The human architecture is 90%. Most never examine it.”
Technical diligence evaluates the model: does it perform, is the data pipeline sound, are there security gaps. Falkovia evaluates the human governance architecture surrounding the model: who has documented authority over its outputs, what happens when the AI is wrong, whether decisions the AI is now making were ever consciously delegated by a human. Technical diligence misses the layer that turns AI capability into post-acquisition liability.
Yes. Pre-acquisition diligence is typically scoped to 4-6 weeks and structured to align with IC timelines. The deliverable is built to be IC-presentable, with a clear governance risk assessment, exposure quantification, and integration considerations the IC needs to price the deal accurately.
Both. Pre-acquisition produces a governance risk assessment. Post-acquisition produces the governance architecture the portfolio company needs to operate without creating ongoing exposure. Many engagements span both phases, with continuity of context that a new vendor would not have.
Healthcare, higher education, and other regulated sectors where AI adoption is creating governance gaps faster than internal compliance can absorb them. The work is sector-specific because regulatory exposure varies meaningfully by industry, and a generic governance framework will miss the dimensions that actually drive risk.
Every engagement is confidential. Falkovia does not work simultaneously with direct competitors in the same sub-sector without explicit written consent from all parties. Engagement scoping includes a conflict review before any substantive work begins.
Both models are available. Some firms engage Falkovia for diligence only, then turn over the governance roadmap to the portfolio company's internal team. Others retain Falkovia to build the governance architecture post-close, often through a fractional Chief AI Officer arrangement. The structure is scoped to deal complexity and portfolio company readiness.
Directly, and in both directions. A portfolio company that cannot demonstrate who is responsible for its AI carries a liability that travels up the investment chain, and it is often the same company that paid for full AI licenses while capturing a fraction of the value, because adoption stalled at the human layer. Governance that names authority, monitoring, and accountability protects the compliance posture and the adoption a valuation depends on in one move. Missing governance compresses the multiple. Working governance expands it.
Whether what an organization claims publicly about its AI matches what the technology actually does. Websites, investor decks, and pitch materials are in scope. The SEC has flagged AI governance and disclosure as an examination priority, and the FTC continues to pursue enforcement against overstated AI claims. For a fund, that makes a portfolio company's ability to substantiate its AI claims a diligence question, because if it cannot, the valuation and the liability both land on the investment.
Every engagement begins with a confidential conversation about what your portfolio actually needs.
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