Conviction is an AI-focused venture capital firm founded by Sarah Guo.
When Conviction moved to Hanover Park, we had to migrate the firm’s financial history: over $1 billion of assets, a meaningful and material amount of investments and limited partners, and years of underlying accounting data.
It took less than seven days.
The 7 Day Migration
Moving fund administrators has historically been painful. A migration can take months of passing files back and forth, mapping historical records, rebuilding workbooks, and reconciling years of activity before the new administrator is fully up to speed.
Hanover Park built a different migration process.
Our AI agents ingest and structure historical accounting data, map investments and entities across different source files, and cross-check the results against the firm’s books and records. Our fund accountants review the output and resolve exceptions rather than rebuilding the history by hand.
“Hanover Park migrated our entire firm in under seven days - faster than we thought possible - providing far better and faster visibility into our financial data than before. We want Conviction to be on the frontier of AI, and this was one of those “wow” moments.”
— Josh, Head of Finance of Conviction
The Power of Data Hygiene
Speed matters, but there’s another benefit to rebuilding migration around AI: data hygiene. Financial data accumulates over the life of a firm. An investment might appear under different names across different periods. Transactions come from multiple banks and entities. Investment activity lives across workbooks, financial statements, administrator systems, and supporting documents. None of this is unusual. It’s a consequence of running an investment firm for years.
It also wasn’t designed for a world where software can reason across the firm’s entire financial history. As part of a migration, Hanover Park takes those historical records and creates a consistent data model underneath them. Investments are mapped across periods. Transactions are tied to entities and accounts. LP records are connected to their underlying activity. Historical balances are reconciled.
This is data hygiene that used to be prohibitively manual.
AI makes it possible to do it as part of the migration itself.
Making Financial Data Legible to AI
This matters because the next generation of financial software won’t just produce reports. It will need to answer questions.
How much have we invested in AI infrastructure since inception?
How has our ownership in a company changed across financing rounds?
Why did NAV change last quarter?
Those questions are only useful if you can trust the answers. And trustworthy answers depend on what sits underneath the model.
For Conviction, migrating to Hanover Park meant that in less than seven days, years of financial history were available in a structured system built for both fund accountants and AI.
The future of AI in finance starts with getting your source of truth right first.
