Industries
AI for finance that a controller signs off.
The CFO org runs the company on numbers it has to defend: the close, the forecast, the board pack, the audit. That is exactly where AI saves days, and exactly where a wrong number is a restatement, not a typo. We build production AI for finance teams: close reporting and reconciliation drafted, FP&A analysis accelerated, procurement and AP cleared, with a controller signing every number and the audit trail built in from day one. Working worldwide since 2019.
Who builds production AI for corporate finance and FP&A?
extendfuture, an AI agency working worldwide since 2019. For the CFO org, not financial-product companies, we build financial pacing and close reporting, reconciliation, FP&A analysis, capital-allocation support, and procurement and AP automation. Digital workers draft and reconcile; a controller or analyst owns the sign-off, and no number reaches a filing or a board pack without a person behind it. We have not shipped under a named corporate-finance logo yet; what we run in production is every primitive the work is built from: predictive models, document intelligence over statements and invoices, and digital workers for research and reporting. Every build ships with an audit trail, segregation of duties and data residency, designed to support SOX control expectations. Engagements start with a founder-led call.
The problems finance teams bring us
The pattern is consistent whether the company is SaaS, manufacturing or services. Growth adds transactions, entities and complexity, but the finance calendar never moves, so the close runs on manual pulls, spreadsheets and re-keying. Reconciliations are worked by hand, the same breaks investigated every month. FP&A spends the period building the report and has no time left for the analysis. And every number must tie to a source and survive an auditor, which most AI pilots treat as an afterthought. That is usually when we get the call.
- A close that runs on manual pulls, spreadsheets and re-keying, against a calendar that never moves
- Reconciliations worked by hand, the same breaks investigated month after month
- FP&A that spends the period building the report, not analyzing it
- Procurement and AP: invoices matched, coded and chased by hand
- Capital-allocation cases assembled from scattered data against a deadline
- A pilot that demoed well and stalled before it could tie to a source and pass audit
What we build for finance
The recurring shape is high-volume assembly work that ends in a number someone has to defend. That is a digital-worker and agentic-AI problem with a hard human layer, and it is what we build. Digital workers draft and reconcile; a controller or analyst owns the sign-off, and segregation of duties is preserved: the system that drafts is not the system that approves. Each service has a scoped entry engagement, so the first commitment is small.
- Close and reporting: digital workers draft the pacing report, the variance commentary and the board pack from your systems of record, with a controller owning the sign-off, via digital workers (/services/digital-workers/)
- Reconciliation: agents match transactions, surface breaks with the likely cause and draft the reconciliation for an analyst to clear, via agentic AI development (/services/agentic-ai-development/); the system never writes off a break on its own
- FP&A analysis and forecasting: driver-based models and variance analysis surfaced where the team already works, built on your history
- Procurement and AP automation: invoice capture, coding, three-way match and exception routing, via AI process automation (/services/ai-process-automation/)
- Human review on the control points, where a wrong number is a misstatement: human-in-the-loop operations (/services/human-in-the-loop/)
- A use-case portfolio scored by value and control exposure: AI consulting (/services/ai-consulting/)
Proof: the primitives, in production
We are honest that corporate finance is a priority vertical we are building into, not a logo we already hold. What lets us start fast is that every part a finance program needs, we already run in production elsewhere. The engineering carries over; only the domain and the controls change.
- Predictive models: sales analytics that lifted qualified-lead conversion by roughly a quarter, the same discipline pointed at pacing, variance and forecasting (/work/predictive-sales-analytics/)
- Document intelligence: systems that read statements, contracts and reports and surface the numbers with every finding traceable to its source, the backbone of reconciliation and AP
- digital workers: research and reporting roles run and measured like staff, the shape a close-reporting worker takes
- Instrumented unit economics: an autonomous newsroom we operate publishes daily at under $2 per article because cost was engineered from day one, the same discipline a finance pipeline needs
How an engagement starts
It starts with a founder-led 30-minute call, booked from the contact page. Bring the workflow that hurts: the close that slips, the reconciliation backlog, the AP queue, the forecast nobody trusts. We give an honest read on whether AI fits, what it has to beat, and which entry engagement proves it fastest. No discovery phase billed by the month.
- A 30-minute call with a founder, not a sales rep: bring the workflow, the volumes and the controls
- A fixed entry engagement: an Agent Readiness Audit, One Workflow Automated, or an AI Opportunity Map
- A working system on your real ledgers and documents, with accuracy reported as a number, before any commitment to scale
- An honest no-go if the data, the volumes or the economics do not support a build yet
What a finance AI project needs
Less than most teams expect, but the list is non-negotiable. Close, reconciliation and FP&A projects move fast when these exist on day one.
- Access to the systems of record: ERP, general ledger, billing, and the spreadsheets the close actually runs on
- Your chart of accounts, close calendar and control matrix written down
- A controller or FP&A owner in the room from day one, not at sign-off
- A clear line: what the AI drafts, and what a person reviews, approves and signs
- Segregation of duties named up front: who prepares and who approves
- A security posture agreed up front: least-privilege access, audit trail, data residency, designed to support SOX control expectations
If you build the software too
Finance teams increasingly build their own tooling: allocation logic, reporting pipelines, the models behind the forecast and the close. When AI writes that code, a clean-looking diff can still misstate a total, double-count a cost or quietly break a control. everylayer, our evidence gate, scores what your tests actually prove across seven layers, writes the missing tests as draft pull requests, and blocks unproven AI-written changes before they merge, so the code carries the same audit discipline your numbers do. It runs self-hosted inside your own network, so financial data and source never leave your control.
- Evidence that allocation, reconciliation and reporting logic was proven before release, at the cheapest layer that catches the bug
- Segregation of duties on the code path: a change is reviewed and proven, not merged on trust
- An audit trail on the code itself, the same discipline your controls demand of the numbers
- The same discipline as our review queues, applied to your code: prove it before you trust it (/everylayer/)
Is this the same as your fintech work?
No, and the distinction matters. Our fintech work is for financial-product companies: lending, payments and wealth products that face customers, with KYC and AML at the center. This is the internal finance function inside any company: the close, FP&A, controls, procurement and AP. Different users, different rules. If you build financial products, start at /industries/fintech/ instead.
Will the AI close the books or sign off on the numbers?
No. Digital workers draft the reporting, reconcile the accounts and flag the variances; a controller or analyst reviews and signs off. Segregation of duties is preserved by design: the system that drafts is not the system that approves, and every number stays traceable to its source. The AI takes the manual assembly off your team so they spend the close on judgment, not on re-keying.
How do you handle SOX and the audit trail?
As a design constraint, not a disclaimer. Every action is logged with an attributable, time-stamped trail, segregation of duties is enforced in the workflow, each figure traces to its source system, and control points sit behind human approval. Data residency is honored. The posture is built to support SOX control expectations; your controls owner and external auditors make the call, and we build so that call is easy.
Can AI do reconciliation?
Yes, on the matching and exception side, which is where the hours go. Digital workers match transactions across systems, surface the breaks with the likely cause attached, and draft the reconciliation; an analyst clears the exceptions. The system never writes off a break or posts an adjustment on its own, and every match and exception is logged, so the reconciliation holds up in audit.
Can AI help with FP&A and forecasting?
Yes. It is the same predictive discipline behind our production sales-analytics work, which lifted qualified-lead conversion by roughly a quarter, pointed at pacing, variance analysis and driver-based forecasts instead of pipeline. The models surface inside the tools the team already uses, with the reason attached, and the analyst owns the narrative that goes to leadership. The system proposes; a person explains and signs.
Our finance AI pilot stalled. Can you take it over?
Yes, that is a common starting point. Most pilots die between demo and production because the boring half was never built: evals, guardrails, the audit trail, segregation of duties and human approval on the control points. We audit what exists, add that layer, and either productionize the pilot or tell you plainly why it will not work.
Talk to the people who build.
One call. An honest read on what AI can do for this, and the number it has to beat.