Finance Automation Software
Agentic, not RPA — it won't break when the layout changes.
Most finance automation software scripts the screen — and breaks the day a layout changes. NashOS automates at the data layer instead: AI agents with 45 typed tools draft forecasts and plan changes and rank variance movers by financial impact on one 10-dimension foundation, and a human posts every write the agents propose.
What finance work is actually automatable
Finance automation software runs the recurring work of a finance team — close, consolidation, variance analysis, reporting, planning — as software instead of late nights. The useful question is no longer whether to automate but at which layer. RPA bots and spreadsheet macros automate the screen: they replay clicks and cell references, and they break the day a layout changes. Agentic automation works at the data layer: an AI agent calls typed tools against a governed data model, drafts the result, and a human decides what posts.
More of the close is automatable than most teams assume. Connector pulls from REST APIs and SFTP run on a schedule — hourly, nightly or weekly — so trial balance lands without anyone exporting it by hand, and Excel and CSV uploads land in the same cube. The SAP S/4HANA, NetSuite, QuickBooks Online and Stripe engines are built and covered by automated tests, and we set those up with you. Consolidation runs as computation — intercompany matching, non-controlling interests, dated ownership, period-average FX translation under IAS 21 — with a consolidation bridge and board pack as outputs. Variance movers are ranked by financial impact rather than eyeballed in a pivot. Reports export to CSV, Excel, or PDF from any view. Forecasting compares 15 algorithms on holdout windows using R², RMSE, MAE, and MAPE, and saves the winner's forecast into a forecast scenario.
The agentic difference shows up when something changes. An RPA bot that scripts your ERP's export screen fails the day the export moves; a macro embeds its logic in a workbook nobody dares reopen. A NashOS agent reads and writes through the same computation path as the rest of the system — 45 tools covering the day-to-day work, across Read, Plan, Forecast, and Decide categories — so there is no screen to break. Change a driver and member formulas recompute the downstream lines through OPEX, EBITDA, and net income. Nothing gets re-recorded or re-scripted.
Automation that writes to financial data has to be safe by construction, not by policy. Every write Nash proposes produces a draft card a human must Post, and the backend re-validates before commit — the model never auto-mutates the database. Tool calls run with the calling user's permissions, enforced at the API layer; the LLM never holds a session token or a stored connector secret. Edits committed from a draft card land in the audit trail with actor and timestamp, most with before/after JSON, and grid saves and bulk loads are logged per save. And the agent streams its work live, step by step, with a per-agent kill switch and an audit mode above that.
All of it runs on one data foundation — the 10-dimension cube described at /system — so automations compound instead of fragmenting into bots that each hold their own copy of the truth. That is how NashOS collapses an 11-day close cycle to 1 day and a 40-hour variance pack to 90 minutes. For the close specifically, see /financial-close-software; for the agent layer, /inside-nash; for the platform design, /agentic-fpa-platform. A pilot takes days, not months — and a walkthrough on your data happens within one business day.
What to automate first
Sequence by payback and risk: scheduled syncs and on-demand exports first, then variance, then the close itself. Everything below ships today — the one roadmap item is marked.
Connector syncs
Scheduled, not remembered
Pulls from REST APIs and SFTP run on a schedule — hourly, nightly or weekly — or start with a CSV/Excel upload. Trial balance lands in the cube without anyone exporting it by hand. The SAP S/4HANA, NetSuite, QuickBooks Online and Stripe engines are built and covered by automated tests, and we set those up with you.
Report exports
CSV · Excel · PDF
Any view exports to CSV, Excel, or PDF.
Variance movers
40 hours → 90 minutes
Top movers ranked by financial impact, not raw percentage. The pack assembles from the cube instead of being rebuilt in slides each period. Auto-drafted paragraph commentary is on the near-term roadmap.
Close tasks
11 days → 1 day
Continuous reconciliation replaces the month-end sprint, and consolidation — intercompany matching, NCI, period-average FX under IAS 21 — runs as computation.
Agentic automation vs RPA
RPA replays what someone clicked. An agent works out what you meant, does it with tools, and shows you a draft before anything posts.
Tools, not screen scripts
45 typed tools
RPA breaks when a layout, export format, or field name changes. NashOS agents call 45 typed tools against the data model, with permissions enforced at the API layer — so there is no screen to break.
Drafts, not blind commits
Human-in-the-loop
A macro posts whatever it computes. Every write Nash proposes here produces a draft card a human must Post, and the backend re-validates on commit. The model never auto-mutates the database.
Your permissions, not a bot login
Server-side auth
RPA bots often run under a shared service account. NashOS tool calls run with the calling user's permissions, enforced server-side at the API layer — the LLM never holds a session token or a stored connector secret.
Audit trail and kill switch
Every figure saved is logged
Edits committed from a draft card are logged with actor and timestamp, most with before/after JSON; grid saves and bulk loads are logged per save. The agent streams its steps live over SSE and sits behind a per-agent kill switch and an audit mode.
Common questions
What is finance automation software?▾
Finance automation software runs recurring finance work — close, consolidation, variance analysis, reporting, and planning — as software rather than manual effort. Legacy approaches automate the screen with RPA bots and macros; agentic automation works at the data layer, where AI agents call typed tools against a governed data model. NashOS is the agentic kind: 45 tools cover the day-to-day work, and every write the agent proposes is drafted for human review.
How is agentic automation different from RPA?▾
RPA records and replays UI interactions, so it breaks when a screen layout, export format, or field name changes — and the logic lives in scripts nobody wants to touch. An agentic system gives an AI model a set of typed, server-side tools: the agent plans the steps, calls the tools, and produces a reviewable draft. In NashOS the agent reads and writes through the same computation path as the rest of the system, with permissions enforced server-side, so there is no screen to break and no shared bot credential.
Which finance processes should we automate first?▾
Start with scheduled connector syncs and on-demand report exports — low-risk, immediate payback. Then variance: NashOS ranks top movers by financial impact, collapsing a 40-hour variance pack to 90 minutes. Then the close itself — scheduled full-close runs that reconcile every new load, plus a consolidation engine with intercompany matching, NCI, and period-average FX under IAS 21 takes the close cycle from 11 days to 1. Planning automation — driver-based recomputes and forecast comparison across 15 algorithms — compounds from there.
Is it safe to automate writes to financial data?▾
Only with the right primitives, and they have to be structural rather than policy. In NashOS every write Nash proposes produces a draft a human must Post, and the backend re-validates on commit — the model never auto-mutates the database. Tool calls run with the calling user's permissions, the LLM never holds a session token or a stored connector secret, and every edit committed from a draft is logged with actor and timestamp, most with before/after JSON. The agent streams its work live, step by step, and sits behind a per-agent kill switch.
How long does finance automation software take to implement?▾
A pilot takes days, not months. Upload a P&L or GL through the onboarding wizard and it auto-maps your accounts and lands you in a live workspace; scheduled REST and SFTP connectors come next. Ask at /try and we'll run a walkthrough on your own data within one business day.
Automate the work. Keep the judgment.
Watch the agent draft against your own trial balance — walkthrough on your data within one business day.