SAFT Recalibration
Recurring valuation of SAFTs is an operational challenge we often see in practice.
Recurring valuation is an operational challenge we often see in practice. SAFTs were often held at cost because teams lacked a practical framework to true them up between signing and token launch.
In our work, we used agentic AI tools to help build an SAFT valuation template that analyzes legal terms, identifies economic assumptions, assesses scenario outcomes, and incorporates calibration logic. The result is a structured operating model that web3 finance teams can use to update SAFT valuations in response to changes in the economic environment and the entity’s facts and circumstances.
A calibrated scenario model can help management estimate changes in fair value after acquisition. The process starts with the deal terms at inception, calibrates to the transaction price, then updates probabilities and outcomes at each reporting date. This produces a more defensible fair value estimate for SAFTs, even when token delivery remains contingent.
By anchoring the model to the original transaction and updating assumptions at each measurement date, companies can build a more consistent and auditable fair value process. The approach gives finance teams a repeatable process where they previously had a valuation gap. It also applies calibration discipline to instruments that were often left at cost until a major event forced a reset.
Used properly, with appropriate governance, expert review, and quality control, agentic AI tools can help create structured data that captures contractual terms, improve documentation, and support more accurate financial reporting.