AI for FP&A
Built for AI agents. Not retrofitted with a chatbot.
AI for FP&A, done as a platform rather than a plug-in: NashOS was designed around agents from day one. 44 agent tools cover the day-to-day work. Every write is drafted, every action audit-trailed. The agentic alternative to Anaplan, Adaptive, and Excel.
What AI for FP&A means in practice
Two very different products share the label. One describes the numbers you already have. The other operates the system and hands you the result to approve.
AI for FP&A splits into two products. The first is a chatbot attached to a planning tool: it can describe what is already in the model. The second is an agent that operates the system — reads the cube, updates a driver, runs a forecast, drafts the change — and hands a person the result to approve. NashOS is the second kind, and the difference shows up in what happens after the answer.
In practice the agent works through 44 typed tools in four groups: Read, Plan, Forecast and Decide. It can query fact tables and pull a P&L or balance sheet, update drivers and write member formulas, fit 15 forecast algorithms and compare them on R², RMSE, MAE and MAPE, then draft the commit and export the report. A request like "hire three engineers and show the runway impact" chains several of those in one pass, and the user watches each step stream in before ratifying the final commit.
What makes that safe on live financials is the write path. No tool writes to the database directly: every write-capable tool produces a draft, the user posts it, and the backend re-validates before anything commits. Tool calls run with the calling user's permissions, so the model never holds credentials, and each single-row change is logged with actor, timestamp and before-and-after values. That is the bar we think AI for FP&A has to clear — not "the model was usually right", but "you can prove what happened".
How the agent layer is built
Most AI for FP&A today is a chatbot bolted onto a planning tool built long before tool-using models existed. NashOS inverted the design: the agent is the primary interface, traditional grids are the fallback. 44 tools across four categories.
Read
Query fact tables, pull a P&L, balance sheet or cash flow, summarize a report, pull audit-log entries, list scenarios.
Plan
Update drivers, write member formulas, create scenarios, manage entities and dimensions, schedule connectors.
Forecast
Run 15 forecast algorithms, compare with R²/RMSE/MAE/MAPE, lock the winner, run what-if scenarios.
Decide
Draft commits, ratify writes, export PDFs, push notifications, manage approvals, query the audit log.
What makes agentic AI in finance safe to run
An agent that can write to your financials needs to be safe by default. Every write surface is gated behind these four primitives.
Draft-before-commit
Every write produces a card. User clicks Post. Backend re-validates. Then commit happens.
Server-side auth on tools
Tool calls run with the user's permissions. LLM never gets credentials. RBAC enforced at the API layer.
Full audit trail
Every row-level change logged: actor, timestamp, before/after JSON; bulk loads logged as attributed runs. Filterable.
Streaming + interruptible
Watch the agent run tools live via SSE. Cancel mid-task if it goes wrong.
Common questions
What is an agentic FP&A platform?▾
An agentic FP&A platform is built around AI agents for finance — software that can chain multiple tool calls, read and write financial data under the calling user's permissions, and carry a multi-step finance task through to a reviewable result — rather than a chatbot UI bolted onto a legacy planning tool. NashOS was built around agents from day one: 44 tools cover the day-to-day work, every write produces a draft for human review, and every action is audit-trailed.
How is this different from a chatbot in Anaplan or Adaptive?▾
The AI for FP&A in those tools arrived as a chatbot bolt-on after the fact — it answers questions, but the planning model underneath was never built for tool-using agents. NashOS reversed the design: every primitive (cube schema, RBAC, draft queue, audit trail) is designed for an agent to operate within safely. The agent can plan, forecast, draft writes, run scenarios, and commit changes — not just summarize what's already there.
Is the agent safe? What stops it from messing up my financial data?▾
Three safeguards. (1) Draft-before-commit: every chat-driven write produces a draft card the user must Post. The LLM never auto-mutates the database. (2) Server-side authentication: every tool call runs with the calling user's permissions; the LLM never gets credentials. (3) Audit trail: every row-level change logged with actor, timestamp, and before/after JSON; bulk loads are logged as attributed runs. You can prove what happened.
Which LLMs does NashOS use?▾
Claude Sonnet 4.6 by default; Gemini 2.5-flash supported as a fallback. Both are swappable via environment variable. Prompt caching keeps API costs low at typical usage.
What can agentic AI in finance actually do inside a planning model?▾
Read fact tables, draft entries, run 15 forecast algorithms, commit writes through the draft queue, export reports as CSV/Excel/PDF, manage scenarios and what-ifs, query the audit log, manage drivers and formulas, and answer plain-English questions about the cube. 44 tools cover the day-to-day work.
Can the agent handle multi-step tasks?▾
Yes. Example: 'Hire 3 engineers and show the runway impact' chains four tool calls — update HEADCOUNT_ENG, recompute SALARIES_ENG via member formula, run runway forecast, render the waterfall card. The user sees each step stream in real time and ratifies the final commit.
What should a finance team look for in AI for FP&A?▾
Four things. Whether the AI can act — chain tool calls and produce a reviewable result — or only describe. Whether every write is drafted for a person to approve, or committed automatically. Whose permissions it runs on: the calling user's, or a service account with broad access. And whether each change is logged — with before and after values on every row-level edit — so any number can be traced back. NashOS answers act, drafted, the user's, and logged.
Watch the agent run live.
Open the demo, ask the agent a question, watch it chain six tool calls in real time.