Rexfin vs Jirav: The Trust Layer Under FP&A

These two tools get filed under the same search, but they solve different problems. Jirav is an all-in-one budgeting, forecasting, reporting, and dashboarding platform built for accounting firms and SMB finance teams. Rexfin is the reconciled data and modeling layer that makes AI trustworthy on financial numbers. If you’re weighing them against each other, the real question is whether you need a planning UI or a way to get exact, traceable figures into the hands of an AI.

Here’s the honest version of who each one is for.

Jirav fits accounting and CFO advisory firms building 3-statement plans for clients, and SMB finance teams that want to retire the V50 Excel model with driver-based forecasting out of the box. Rexfin fits teams who want AI to answer financial questions without inventing numbers. It connects your ERP, accounting system, and spreadsheets, builds one reconciled model, and lets AI pull the exact figure instead of guessing.

At a glance

	Jirav	Rexfin
Core job	All-in-one budgeting, forecasting, and reporting	Reconciled financial model that AI can query
Best for	Accounting firms and SMBs doing FP&A	Teams putting AI on top of financial data
AI approach	Minimal: “Analyze Intelligently,” no AI agent	AI retrieves exact figures and calculates deterministically
Data reconciliation	Depends on clean upstream GL mapping	Built in; every figure ties to the source
Where numbers come from	Driver-based models built on GL/ERP feeds	Reconciled model linked to ERP and accounting
Setup	Fast GL integration, template-driven	Connect sources, reconcile, query
Pricing model	Public tiers, from $50/mo	Demo-led: contact for pricing

Where Jirav is strong

Jirav owns a defensible niche. It’s purpose-built for accounting firms and CFO advisory practices, backed by an AICPA strategic partnership, and it shows in the product. The driver-based engine forecasts the P&L, balance sheet, and cash flow as a full 3-statement pro forma, so teams get a genuine model rather than a stack of disconnected tabs. Its Report Packages turn a monthly close into a client-ready financial package in a few clicks, which is exactly the workflow an advisory firm repeats across dozens of clients.

Time-to-value is a real strength. Jirav integrates quickly with QuickBooks Online, Xero, Sage Intacct, and NetSuite, then leans on industry-specific templates, KPI dashboards, and metrics libraries so a new engagement doesn’t start from a blank model. Workforce and headcount planning, rolling forecasts, and multi-scenario modeling on leadership input are all there. Clone lets a firm spin up a fresh company model fast, which matters when you’re onboarding client after client.

And the pricing is refreshingly transparent for this category. Entry starts at $50/mo, with a CFO Enterprise tier from $150/mo unlocking custom budgets, long-range forecasts, and bottoms-up scenario modeling. In a market full of “request a quote,” that clarity is a feature in itself.

Where Jirav leaves gaps

The gaps show up around AI and the accuracy foundation an AI layer depends on.

Jirav’s AI story is thin. The marketing centers on dynamic, driver-based planning and “Analyze Intelligently,” but there’s no flagship AI agent, no conversational assistant, and no AI-native framing. For a firm that plans to add AI-driven advisory to its service line, that’s an exposed flank. The moment you point a language model at Jirav’s outputs, you’re responsible for making sure it reads the right figure and doesn’t approximate the rest.

The deeper issue is where the numbers come from. Jirav’s model accuracy depends on clean upstream bookkeeping and correct GL mapping. That’s fine for a human analyst who knows the client’s books, but an AI has no such judgment. If you want AI to retrieve last quarter’s gross margin or reconcile a forecast line to the ledger, you need a layer that guarantees every figure ties to source. Jirav produces the plan; it doesn’t provide the deterministic reconciliation and calculation guarantees an AI needs to answer safely.

Jirav is also, by design, an SMB and accounting-firm tool. That focus is a strength for its ICP, but it means limited fit for large multi-entity complexity and a narrower integration surface than warehouse-backed platforms. If your data lives across a warehouse, several ERPs, and uploaded statements, the planning UI isn’t the constraint: the reconciled data underneath it is.

Where Rexfin is different

Rexfin starts from the data, not the planning screen. It connects your accounting, banking, and warehouse sources (or ingests uploaded statements), then builds one reconciled model where every figure ties back to its origin in the ledger. That reconciliation is the product, not a setting you configure later. A deterministic engine does the math, so calculations are repeatable rather than estimated.

The payoff shows up the moment AI touches the numbers. Instead of a language model guessing revenue from surrounding context, Rexfin lets the AI retrieve the exact figure, run the calculation deterministically, and test scenarios against real data. Ask for a margin by entity and you get the reconciled number, traceable to source, not “roughly.” That’s the layer that makes AI safe to point at financial data. If you’re evaluating the broader category, our take on the reliable financial-modeling layer for AI explains why this sits underneath a planning tool rather than replacing it.

Rexfin is not trying to be a full FP&A suite. It doesn’t do Report Packages or client dashboards. It’s the trustworthy data and modeling foundation those tools sit on top of.

Which should you pick

Pick Jirav if you’re an accounting firm or SMB finance team that needs an affordable, template-driven planning tool with real 3-statement forecasting and client-ready reporting, and you’re comfortable owning data accuracy yourself.

Pick Rexfin if your priority is getting AI to work on financial data with numbers that tie out and trace back to the ledger. The two aren’t mutually exclusive. A firm can keep building plans in Jirav and use Rexfin as the reconciled layer that feeds reliable figures to any AI sitting on top, which is precisely the trust infrastructure advisory firms will need the day they add AI to their service.

Want to see exact figures retrieved live from a reconciled model? Book a demo and bring your own numbers.

FAQ

Is Rexfin a replacement for Jirav? No. Jirav is a planning, budgeting, and reporting suite for accounting firms and SMBs. Rexfin is the reconciled data and modeling layer that makes AI reliable on financial numbers. Many teams run both: Jirav for the plan, Rexfin as the trust layer under any AI.

Does Jirav have an AI agent? Not a prominent one. Jirav’s pitch centers on driver-based 3-statement planning and “Analyze Intelligently,” with no flagship AI assistant or agent. Rexfin exists to give whatever AI you use exact, traceable figures instead of estimates.

Why does reconciliation matter for AI specifically? An AI has no judgment about whether a figure is right. If the underlying numbers depend on manual GL mapping, an AI can confidently return the wrong one. Rexfin builds one model where every figure ties to source, so AI queries return numbers that reconcile to the ledger rather than approximations.