What does consultance.ai build?

Source-grounded AI deal desks and finance systems: diligence, reconciliation, financial close, reporting, underwriting, and compliance knowledge workflows, designed, deployed, and maintained end to end.

Who does consultance.ai work with?

Private equity funds, private credit teams, CFO offices, family offices, broker-dealers, and regulated finance teams that need faster, traceable workflows around confidential financial and deal data.

What makes a finance AI system trustworthy?

Material outputs need source links, verification states, approval controls, audit logging, and a clear exception path. The system automates the intelligence work while the finance or deal team retains judgment and final approval.

How long does an AI deal desk or finance automation build take?

A scoped proof of concept typically runs on representative data first. Production timing depends on data condition, integrations, deployment requirements, and the number of workflows in scope.

Do you transfer code and IP?

Yes. Code and IP transfer at every milestone. No subcontractors, no offshore handoffs, a direct line to the engineers building.

Do clients own the system?

Yes. The engagement is designed around client-owned code, prompts, data, and deployment. The team can run the system independently or retain consultance.ai for support and further build work.

Industries · Last updated Jul 2026

AI for FP&A that kills the deck-building week.

FP&A teams spend the month assembling and reconciling, then get two days for actual analysis. We flip the ratio: AI assembles the actuals, drafts the commentary, and refreshes the forecast, so your analysts spend the month on decisions.

AI workflows for ai for fp&a

Rolling forecast automation with driver-based models

Variance analysis and commentary drafting from actuals

Board and management pack assembly

Scenario and sensitivity modeling on demand

KPI tracking with exception alerts

Operational value

Our builds draft variance commentary straight from ledger detail with every number traceable to its source. The public library ships a free finance reporting pack and board deck pack so you can test the pattern on your own numbers first.

Board packs assembled overnight
Variance commentary drafted, not typed
Analysts analyzing instead of formatting

The 80/20 problem in FP&A

Ask any FP&A lead where the month goes: gathering actuals, tying numbers between systems, formatting the deck, chasing department heads for inputs. The analysis the function exists for gets whatever hours are left, usually the two days before the board meeting.

The automatable share is the gathering and formatting. A pipeline that pulls actuals on day one, drafts the flux with commentary, and assembles the pack to your template returns most of that month. The team's output changes from a deck that describes the past to scenarios that shape the next quarter.

Numbers that check themselves

A forecast or commentary is only useful if the numbers underneath are right. Our FP&A builds follow the same rule as our close builds: any computed figure gets re-derived by an independent pass before it ships, and anything that doesn't reconcile blocks with a flag instead of publishing quietly.

That discipline matters more in FP&A than anywhere, because the output goes straight to the board. A drafted pack your CFO can trust is worth ten dashboards nobody checks.

Questions about ai for fp&a

How does AI change FP&A work?

It absorbs the assembly layer: pulling actuals, building the flux, drafting commentary, refreshing the forecast, formatting the pack. The analyst's job shifts to what the title says: analysis. Same team, several days a month returned to real work.

Can AI write variance commentary?

First drafts, yes, and good ones when grounded in ledger detail. The build pulls the drivers behind each variance and drafts the explanation with sources attached. Your analyst edits judgment calls in. The blank-page hours disappear.

Can AI build financial forecasts?

AI maintains and refreshes driver-based forecasts as actuals land, and produces scenario runs on demand. The model structure and the assumptions remain owned by your team. Where we compute numbers, an independent re-derivation checks them before anything ships.

What tools does this work with?

Whatever you run today: Excel, Google Sheets, QuickBooks, NetSuite, Xero, your BI stack. We build to your file formats and systems rather than forcing a platform migration. The output lands where your team already works.