02 / Exit Economics / Current owner
AI Re-underwriting assesses software viability and its position in a market changed by AI. It determines whether an AI-obsolescence discount exists, whether it is rational, and whether additional time and capital can recover value.
AI Re-underwriting
What will the next buyer underwrite?
AI Re-underwriting applies Big Lever AI's proprietary code-intelligence capability to the current owner's question. The assessment establishes the software's current condition, tests whether the market is applying an AI-obsolescence discount, and determines whether that discount reflects real product risk.
The assessment identifies the strongest economic path for the asset.
One assessment / Three economic paths
The assessment establishes the software's condition, tests the existence and rationality of an AI-obsolescence discount, and returns one of three actionable verdicts.
The software remains viable and supports the current investment thesis. Where the market is applying a discount, the evidence establishes whether that concern is unsupported or overstated. The verdict identifies the value the asset can credibly defend and the evidence that makes its position underwritable.
Defend the value.The assessment identifies the product wedge most likely to change revenue, margin, defensibility, exit value, or hold period. AI Reengineering then builds the successor capability around that economic target.
Reengineer the product. Reprice the exit.Additional time and capital lack a credible path to a stronger multiple or shorter hold. Taking the asset to market now preserves value before holding costs, product risk, or market repricing erode the available outcome.
Preserve the value.Software first
The assessment begins with the software itself. Code, architecture, data, product, market position, and transformation economics are reconciled into one asset-level view.
Architecture, security, reliability, scalability, provenance, maintainability, model dependencies, code history, and team concentration.
What the existing architecture and data make possible—and where an AI-native successor can create an advantage beyond the current product.
Embedded workflows, proprietary data, integration depth, customer trust, switching costs, and the sources of differentiation a future buyer can underwrite.
What the market appears to be penalizing, which concerns the evidence supports, and which concerns exceed the asset's actual exposure.
The time and capital required for product change measured against the plausible effect on revenue, margin, exit value, multiple, and hold period.
Assessment before allocation
We begin before the conversation with an outside-in point of view. AI Re-underwriting produces the verdict. Reengineering follows where product change can credibly improve the exit economics.
Before the engagement
Fixed-scope assessment
Verdict-dependent execution
Named-asset thesis
We approach a named asset with a specific view of the market pressure, technical position, and economic opportunity worth testing. The engagement replaces that outside-in thesis with evidence from the software itself.
Discuss a portfolio asset