How does pricing scale for a private credit fund?

Pricing scales with origination volume rather than seat count. Credit teams add unlimited users across funds and entities so originators, credit analysts, portfolio managers and risk teams all work on the same platform without per-seat licensing pressure.

How quickly can a private credit fund deploy Reuben AI?

Most private credit funds are operational within hours. Existing pipeline and borrower data is imported, strategy Blueprints encode the fund's direct lending, unitranche, mezzanine or venture debt thesis, and covenant and KPI monitoring is configured against existing reporting cadences.

What is Reuben AI's data residency and EU AI Act compliance posture for credit funds?

Data is organisation-isolated with encryption in transit and at rest, role-based access controls and exportable audit logs. The platform meets EU AI Act Article 26 deployer duties (Regulation (EU) 2024/1689), supports FCA SYSC 4 and SYSC 7 governance evidence, and retains records aligned to AIFMD Annex IV reporting and SEC Marketing Rule 17 CFR 275.206(4)-1.

Built for private credit, not adapted from equity

The covenant cadence, the NAV reconciliation, the waterfall complexity and the LP reports that credit funds actually need.

Equity-fund tools were not built for private credit. Covenant compliance is continuous, not quarterly. NAV facilities and subscription lines need their own waterfall logic. LPs in credit ask for yield, current pay vs PIK, weighted average life and concentration analytics, not equity-style return narratives. Most credit funds bridge the gap with spreadsheets and manual reconciliation.

Reuben AI is the operating layer credit funds actually need: covenant tracking encoded from the agreement, NAV reconciled to the day, waterfalls modelled per facility, and LP reporting tailored to credit. From origination through workout, one platform.

Why credit funds need a different OS

Covenants are continuous. Financial covenants, leverage tests and reporting covenants must be tracked on the cadence the credit agreement specifies. Quarterly spreadsheets miss compliance windows and waiver triggers.

NAV needs to reconcile to the day. Drawn, undrawn, accrued interest, PIK toggles, mandatory prepayments and recyclability all sit in different ledgers in most funds. Management fees and carry waterfalls computed against a Friday-night spreadsheet refresh produce errors that compound over a fund's life.

Workout situations need institutional memory. When a borrower trips a covenant the diligence done at origination, every consent and waiver since, and every conversation with the sponsor matters. Spreadsheets lose that history precisely when you need it.

LP reports look fundamentally different. Yield, loss given default, weighted average life, current pay vs PIK and concentration by industry and seniority. Equity LP report templates do not answer the questions credit LPs ask.

How Reuben AI serves credit

Covenant compliance engine

Covenants encoded once from the credit agreement. Borrower financials feed in via the founder portal or direct integrations. The system computes compliance against every covenant on the cadence the agreement specifies and surfaces breach risk before it happens. Waivers and amendments flow through structured approval with full provenance.

Real-time NAV and waterfall

Every facility, every drawdown, every accrual reconciled to the day. Management fees, performance fees and carry waterfalls computed against live NAV. Subscription lines, NAV facilities and back-leverage modelled with their own logic.

Origination and structured underwriting

Multi-dimensional credit diligence covering financial health, sponsor track record, sector dynamics, structure and downside scenarios. Evidence-backed findings flow directly into IC memos and credit committee workflows.

LP reporting tailored to credit

Yield-based reporting with current pay vs PIK breakdowns, weighted average life, loss given default and concentration analytics by sector, sponsor and seniority. Generated automatically from live portfolio data.