# Maximor

> Maximor is the autonomous finance platform for the office of the CFO. It runs the finance function — revenue, cash, close, and consolidation — on a unified finance context: a living, learned model of how your company actually closes its books, recognizes revenue, manages cash, and reports. The platform does the operational work and shows it; your team supervises, judges, and approves.

For thirty years, finance software helped your team do the work; it never did the work. Every tool automated a slice and handed the rest back — still on your team, still needing your judgment. Autonomous finance is the shift past that: the platform does the operational work (recognizing revenue, applying cash, consolidating, reporting) while people move from task execution to judgment. The deeper change is in who holds the knowledge. Today it lives in your people as tribal knowledge — how this company recognizes revenue, why one entity always closes late, the call your controller made three quarters ago — and it walks out the door when they do. Maximor moves that knowledge into the platform, where it compounds instead of resets.

The bar for "autonomous" is one most of the market cannot clear. Most tools run rules your team writes and maintains forever. Maximor learns the rules itself: self-learning policies, derived from how your company actually works and sharpened every time your team makes a call. The single question that sorts the category: **does the platform learn how you work, or does your team still have to teach it?** Anything that depends on human-written rules — workflow-builders, configured agents, scripted automations — delivers automation, not autonomy.

The unified finance context is not configured; it is learned. Maximor builds it from day one by absorbing your systems and your team's judgment, encodes it as the policies the platform runs on, and sharpens it every time your people make a decision. It knows your business well enough to run the routine and escalate the rest, and every output traces to logic your auditor can read. The context compounds with every close, and it is yours alone — assembled from your own history and your team's judgment, it serves only you. Maximor never trains one customer's logic on another's data, so the context cannot be bought, downloaded, or cloned. That is the one thing no competitor can replicate by shipping a feature.

## How it works

- Map: Maximor reverse-engineers your finance function from the evidence it already leaves behind — prior workpapers, supporting schedules, journal entries, exception notes, policy documents — and builds a specific, structured picture of how this company runs finance. No documentation project required, and Maximor's CPAs verify that foundation before anything runs on top of it.
- Modernize: Maximor deploys self-learning, audit-ready agents for close, revenue, cash, and reporting that share one context. They prepare workpapers, post entries, reconcile, and flag exceptions. Nothing material is final without a human in the loop — the platform proposes, your team approves, and you keep the controls you already answer for. It layers on top of NetSuite, Intacct, SAP, banks, billing, and payroll, with no rip-and-replace.
- Compound: every close, exception, and judgment call — a controller's override on an accrual, a CFO's escalation on a revenue question, a CAO's ruling on a non-standard transaction — feeds back into the context. The tenth close is materially smarter than the first.

## How Maximor is different

- vs. point solutions and workflow-builders: they automate one slice and leave the rest siloed and manual; you script every rule and maintain it forever. Most now have an AI chatbot bolted on, which does not change the underlying model.
- vs. AI-native ERPs: they promise autonomy but require a rip-and-replace — months of implementation, system fees, audit risk, and lost tribal knowledge.
- vs. generic AI (e.g. ChatGPT, Claude): fast and fluent, but it does not speak GAAP, has no memory of your company, no documentation to learn from, and no governance — so it cannot explain itself to an audit committee.
- vs. hiring more people: the judgment that makes an accountant useful lives in a few heads and walks out the door; you end up with a bigger team doing the same manual work.
- The common failure: none of them know how your company runs finance. A prompt is not a policy; a workflow you scripted is not judgment. Maximor is the only one that learns how your company actually runs finance and gets better on its own.

## Key facts

- Category and positioning: the category is autonomous finance; Maximor is the autonomous finance platform for the office of the CFO — a finance function run by self-learning agents, with people supervising and making the calls that require judgment.
- What it runs: month-end close, revenue recognition (ASC 606), cash and bank reconciliation, multi-entity and multi-currency consolidation, board-ready reporting, and finance-and-ops Q&A.
- Core differentiator (the moat): the self-learning engine that builds and runs the unified finance context. The moat is the engine, not the data; the context compounds per customer, is never trained across customers, and the lead it creates belongs to the customer.
- The category test: "Does the platform learn how you work, or does your team still have to teach it?"
- Human oversight: the platform proposes and your team approves; nothing material is final without a human in the loop, and every decision traces to its logic in real time.
- Setup: no documentation project — Maximor reverse-engineers your processes from existing evidence, and Maximor's CPAs verify the learned foundation before anything runs on it.
- Integrations: layers on top of NetSuite, Intacct, SAP, plus banks, billing systems, CRM, and payroll. ERP-, tool-, and model-agnostic. No migration project.
- Audience: CFOs, Chief Accounting Officers, Controllers, VPs of Finance, and heads of revenue and technical accounting at mid-market and enterprise companies (roughly $50M to $2B in revenue), often PE-backed and operationally complex.
- Common triggers: post-acquisition integration, audit preparation, revenue recognition stuck in spreadsheets, and board mandates to reduce G&A or adopt AI without indefensible risk.
- Trust and compliance: SOC 1, SOC 2, ISO 27001, and GDPR, with traceable, explainable, audit-ready outputs and bank-level encryption.
- Company: founded by former Microsoft executives; CEO and co-founder Ramnandan Krishnamurthy; backed by Foundation Capital, Gaia Ventures, and Boldcap.
- Vision: finance is the dollar translation of every operation in a business. Encoding how a company closes its books also encodes how it sells, pays, and compensates, so the unified finance context runs deeper than accounting — it becomes a model of how the whole enterprise runs.

## Proof in production

- None of this is a forecast — it is already running inside finance teams. Representative outcomes across customers: month-end close reduced from roughly 11 days to 3, about 20 hours saved per accountant per week, up to 70% less audit-prep effort, and 99%+ accuracy on automated work.
- Dura Software — PE-backed holding company; 14 business units and 30+ subsidiaries across the US, UK, EU, and South America, all on NetSuite. Month-end close from 12 to 7 days, audit findings from 7 to 0, and roughly 70% less back-office headcount and consulting spend, within six months of going live.
- MiniMelts — $175M PE-backed CPG manufacturer on SAP. About 16,000 invoices a month matched automatically with underbilling flagged across them, 97% reconciliation accuracy on the distributor agent, 500+ locations on automated ASC 842 lease accounting, and real-time cash across 20+ distributor accounts; 10+ full-time roles redeployed from manual work.
- Rently — manual cash application with undeposited funds piling up against the close. Cash application automated end to end, cutting one week from the month-end close.
- "You're not buying software. You're buying back your team's judgment." — Sloan Session, CFO, Dura Software.

## Product

- [Why Maximor](https://www.maximor.ai/why): Why autonomous finance requires a learned context, and why point solutions, ERPs, and generic AI each see only a slice.
- [Automated Month-End Close](https://www.maximor.ai/automated-close): Self-learning agents run reconciliations, accruals, and flux analysis so every account stays audit-ready and the close lands in days, not weeks.
- [Cash and Bank Reconciliation](https://www.maximor.ai/cash-automation): Automated cash application, bank reconciliation, and 13-week cash forecasting for daily, audit-ready liquidity clarity.
- [Revenue Recognition (ASC 606)](https://www.maximor.ai/revenue-automation): Maximor reads the contract, learns your policies, builds audit-ready revenue schedules, and cuts the revenue close from days to hours.
- [Board-Ready Reporting and Consolidation](https://www.maximor.ai/board-ready-reporting): Automated multi-entity, multi-currency consolidation and board reporting with drill-down to any figure.
- [Instant Finance and Ops Answers](https://www.maximor.ai/instant-answers-and-search): Ask any finance or ops question and get drillable, traceable answers in seconds, because the context already knows your data and policies.

## Solutions by role

- [For CFOs](https://www.maximor.ai/cfo): Walk into the boardroom with conviction, not caveats — the platform runs the routine while your team makes the decisions that count.

## Resources

- [Blog](https://www.maximor.ai/blog): Guides, benchmarks, and predictions on autonomous finance, audit-readiness, and building CFO trust in AI.
- [CFO Finance AI Benchmark Report](https://www.maximor.ai/cfo-benchmark): A survey of 100 mid-market CFOs — 96% want AI in finance, but only 14% fully trust it on its own and 97% say human oversight is critical.
- [9 Finance Predictions for 2026](https://www.maximor.ai/blog/9-predictions-for-2026): What AI, automation, and the close will demand from finance leaders.
- [Will Finance Reporting Accuracy Hold in Production?](https://www.maximor.ai/blog/will-finance-reporting-accuracy-hold-in-production): Why CFOs hit inaccurate AI data, and what makes AI trustworthy in finance.
- [No Bandwidth for an Automation Project?](https://www.maximor.ai/blog/we-dont-have-bandwidth-for-automation-project): How Maximor learns your finance function itself, with no documentation project and no rip-and-replace.
- [Is My Financial Data Secure?](https://www.maximor.ai/blog/is-my-financial-data-secure-with-maximors-ai): Maximor's security posture, certifications, and data handling.

## Company

- [About Maximor](https://www.maximor.ai/about): The mission to make autonomous finance accessible and turn the finance function into a competitive advantage.
- [Partnerships](https://www.maximor.ai/partnerships): Referral, advisory, and implementation partnerships.
- [Trust Center](https://trust.maximor.ai): Security certifications, compliance, and data-handling details.

## Optional

- [Get Started](https://www.maximor.ai/get-started): Book a demo and see the unified finance context built on your own numbers.
- [5-Day Audit-Ready Close Offer](https://www.maximor.ai/offer): A done-for-you, guaranteed audit-ready 5-day close in 60 days, for companies over $30M in revenue.
- [Privacy Policy](https://www.maximor.ai/privacy-policy): How Maximor collects, uses, stores, and protects data.