Cube AI makes financial data decision-ready across every FP&A tool and workflow.
Learn more about AI at Cube →The governed foundation — platform capabilities.
The entire financial close and data management cycle, at a high level.
See all →For buyers in-cycle.
Guides, resources, and help.
About Cube, careers, and news.
Cube is the AI-native FP&Agents platform. Built on governed data, so every number has a source. Agents do the legwork, so finance can focus on the work that matters.
Most AI tools were built to predict the next word. Finance requires the exact number. That structural mismatch is why most AI pilots in finance fail before they reach the board.
AI generates the most likely answer based on patterns. Your chart of accounts has one correct answer. That gap costs you your credibility when the board asks a question and the numbers don’t trace.
Claude, ChatGPT, and Copilot don’t know your COGS formula, your cost center hierarchy, or the difference between your forecast and actuals versions. Without that context, every answer is a guess.
AI without role-based access control, transaction lineage, and a full audit trail is a liability in front of any board or auditor. AI without governance is a faster way to get the wrong answer.
We asked Claude 531 finance questions with exported spreadsheets, then again connected to Cube. On files, it got 65.7% right. In finance, 65% right is 100% wrong.
Clean, traceable, governed data your team controls, connected from every source system your business runs on.
Four purpose-built agent teams running the full finance lifecycle, from data integrity to board communication.
The apps your team works in. The AI your organization is adopting. All running from the same clean, traceable financial data.
Four FP&Agents handle the always-on work across your data, reports, plans and business partners. Agents scale finance's capacity, while the people remain in control.
Collects, maps and reconciles data from every source so every figure is right before anyone opens a file.
Meet The Data Managers →Explains variances to the transaction, flags anomalies and answers ad hoc questions in plain language.
Meet The Analysts →Reruns forecasts as new actuals land, builds scenarios and keeps budgets current between cycles.
Meet The Planners →Brings answers to department heads in Slack, Teams and email, so finance is in every decision.
Meet The Business Partners →Claude, ChatGPT, Copilot, and the agents you build get read and write access to the same governed numbers the Analyst Agents explain.
Claude
ChatGPT
Copilot
Gemini
Query actuals, variances, and drivers from any assistant or pipeline agent, live and governed.
Post commentary, flag anomalies, and assign follow-ups from any endpoint. Everything queues for finance approval and lands in the audit trail.
No consultants. No proprietary code behind cells. No IT queue. Finance in the driver’s seat from the moment you go live.
Unify data from every source system, ERP, CRM, HRIS, data warehouse, into a single clean layer. Native connectors. No engineering tickets.
Your COGS formula, your cost center hierarchy, your business logic, modeled exactly as your business works. No-code, so finance never loses control.
FP&Agents run the finance lifecycle. Variance analysis. Board narratives. Rolling forecasts. Every output traces back to the transaction, working while you sleep.
Thousands of finance professionals run on Cube to plan, report, and decide on one governed set of numbers.
Cube closes the gap between general AI and the precision finance requires. Every insight traces back to the transaction. Finance stays in the driver’s seat.
AI agents for FP&A are software that complete finance tasks end to end, such as checking data quality, explaining variances, drafting forecasts, or building board summaries. They work on governed financial data and hand their output to finance for review. Cube's FP&Agents are built for these workflows.
The best AI agents for finance work on a single governed data model, follow your chart of accounts and business rules, respect role-based permissions, and require approval before anything is published. Cube's FP&Agents meet those requirements across data management, analysis, planning, and reporting.
An assistant responds when you ask a question. An agent works through a task on its own, often on a schedule or when data changes, and reports back with a result to approve. Cube offers both: FP&Agents for ongoing work and the Cube MCP Server for assistants like Claude and ChatGPT.
No. Agents scale finance's capacity, while the people remain in control. They take on data preparation and first drafts so analysts can spend more time on judgment, partnering with the business, and decisions.
In Cube, agents run inside your existing roles and permissions. They propose changes, finance approves them, and every action is recorded in the audit trail. Nothing is published to a board deck or plan without sign-off.
Start with work that is repetitive and easy to verify: data mapping and sync checks, budget-to-actual variance explanations, and recurring management reports. Once the team trusts the output, extend agents to forecasting and scenario drafts.
They can be, if they run on governed infrastructure. Cube's agents use role-based access, operate on SOC 1 and SOC 2 Type II audited systems, and log every query and edit, so financial data stays protected.
AI for FP&A uses agents and assistants to automate finance work such as data mapping, forecasting, variance analysis, and reporting, while finance keeps approval over every change. The key difference from general AI is governance: answers have to come from trusted, calculated financial data. Cube runs its AI on one governed source of truth, so every answer traces back to the transaction.
The best AI tool for FP&A works on governed financial data rather than raw exports, shows its work, and fits into the tools your team already uses. Cube combines AI agents for data, analysis, planning, and reporting with an MCP Server for Claude, ChatGPT, Copilot, and Gemini, all on the same audited, permissioned model.
Cube makes financial data clean and traceable across every FP&A tool and workflow. It unifies every source system into a single clean layer and syncs it into Excel, Google Sheets, PowerPoint, Slack, and AI assistants. FP&Agents run the finance lifecycle. The layer, not another tool.
Through Trace to Truth: every AI-generated insight maps back to a specific GL transaction. Cube also provides unified financial data, your specific business context, enterprise governance (RBAC, audit trail, SOC 2), and finance-native capabilities general AI can’t replicate.
Trace to Truth is Cube’s core data architecture: every AI-generated insight, dashboard figure, and board narrative maps back to a specific GL transaction. A board slide shows 12% growth. Click it and Cube drills past the summary, past the formula, to the specific transactions that produced it.
FP&Agents are Cube’s purpose-built AI agents in four teams: The Data Managers (data integrity), The Analysts (reporting and variance), The Planners (forecasting and modeling), and The Business Partners (board decks and investor narratives). Built exclusively for the precision finance requires.
The Cube MCP Server connects Cube’s clean, governed financial data to every AI assistant your organization uses, including Claude, ChatGPT, Microsoft Copilot, and Gemini, in under a minute. Every AI tool works from the same traceable financial source.
General AI tools have no context about your business: no chart of accounts, no COGS formula, no version history. They operate on patterns. Finance operates on facts. Cube’s MCP Server connects those tools to your governed data, so you’re not choosing between Cube and AI.
No. Cube lives inside Excel and Google Sheets with direct, bi-directional sync. Your team stays in the tools they already use while Cube’s clean data layer runs underneath. Your spreadsheets stay. Everything underneath gets smarter.
AI for FP&A applies machine learning, large language models, and agents to financial planning and analysis: consolidating data, building forecasts, explaining variances, and drafting reports. The goal is to give finance teams more time for judgment and partnership, and less time on manual data work.
What separates finance-grade AI from general AI is governance. Finance runs on facts, so every answer has to come from trusted, calculated data, respect who is allowed to see what, and trace back to the source transaction.
One model that unifies the GL, CRM, HRIS, and other sources, so AI works on the figures of record instead of exports.
Your chart of accounts, mappings, and formulas, so the AI understands how your business actually calculates its numbers.
Agents prepare forecasts, analyses, and reports. Finance reviews and approves before anything is published.
Every AI answer links back to the underlying transactions, with permissions and an audit trail on every action.
Business now moves at the speed of AI, and finance is expected to keep up. Most FP&A teams still spend the majority of their week collecting, cleaning, and reconciling data. AI built for finance takes on that work, shortens close and reforecast cycles, and lets the team spend its time on decisions instead of spreadsheets.
Teams that get value from AI quickly tend to follow the same path. Start with the data foundation, then expand what the AI is allowed to do.
| General AI on files | AI connected to Cube | |
|---|---|---|
| Data source | Uploaded exports and spreadsheets | One governed model synced from source systems |
| Business logic | Infers formulas from patterns | Uses your chart of accounts and business rules |
| Accuracy | Answers can vary between prompts | Same calculated figure of record every time |
| Permissions | Whoever has the file sees everything | Role-based access applied to every answer |
| Audit trail | None | Every query, edit, and approval logged |
| Traceability | No link back to the source | Drill from any answer to the GL transaction |
| Agent team | What it handles | Example |
|---|---|---|
| Data Managers | Data integrity: mapping, syncs, and anomaly checks | Flags an unmapped GL account before close and suggests the mapping |
| Analysts | Reporting and variance analysis | Explains why March opex is $84K over plan, with the drivers ranked |
| Planners | Forecasting, scenarios, and models | Drafts a reforecast after a hiring plan change for finance to approve |
| Business Partners | Board decks and stakeholder narratives | Builds the monthly board summary from approved numbers |