Finance & Cash Flow Analytics

Project cash flow modeling breaks when inflows from bookings sit in CRM, outflows to contractors sit in ERP, and land or finance costs live in separate trackers with different cut-off rules. Leadership sees a single net position only after analysts merge files, so stress tests on sales pace or cost inflation arrive too late for lender conversations.

FireAI maps scheduled collections from payment plans, expected construction and approval-linked spends, interest and fee accruals, and other project-specific lines into a rolling cash view by project and consolidated entity. Real estate finance analytics shows month-by-month surplus or deficit, minimum cash months, and variance to the last approved model.

How FireAI solves the problem: It keeps one calendar and mapping layer for inflows and outflows, refreshes as bookings and certifications update, and lets you scenario-tag pace and cost so project-wise cash flow modeling stays comparable across your portfolio.

What FireAI tracks:

• Inflow by payment plan stage, tower, and buyer segment
• Outflow by contractor package, land, interest, and overheads
• Net liquidity and runway by project and reporting period
• Variance to last baseline model with driver tags

What you can ask FireAI:

• "Which projects turn cash negative in the next two quarters under base case?"
• "Show project-wise cash flow for Project Alpha with 10% slower collections"

Collection efficiency and aging analysis suffers when demand letters, bank credits, and booking adjustments are not tied to unit stage, buyer finance approval, or dispute flags. Finance sees overdue lists while sales sees pipeline, without a shared real estate finance analytics view of who pays on time and why slips cluster.

FireAI links receipts to booking value, payment plan tranches, and unit or tower attributes so aging buckets and collection efficiency roll up the way leadership expects. You see days-sales-outstanding style metrics by project, early versus delayed payers, and concentration in a few large defaulters.

How FireAI solves the problem: It reconciles gateway, bank, and ERP receipt lines to demand schedules, flags mismatches and partials automatically, and refreshes collection efficiency aging so collections and CRM can work one queue.

What FireAI tracks:

• Collected vs due by period, project, and payment milestone
• Aging buckets and movement month to month
• Early payment discounts and penalty accrual where configured
• Buyer or broker tags for follow-up prioritization

What you can ask FireAI:

• "What share of dues is older than 90 days for Tower C?"
• "Show collection efficiency trend for subvention versus self-funded buyers"

Tranche disbursement tracking is painful when lender conditions span technical certificates, escrow utilization, and interest servicing, each confirmed in different inboxes. Drawdown delays stall contractor payments while finance manually checks what blocked the last release.

FireAI connects lender sanction letters, draw request logs, technical sign-offs, and actual credit timestamps so each tranche has a visible status and blocker. Real estate finance analytics shows utilization against sanctioned limits, pending conditions, and slippage versus construction need.

How FireAI solves the problem: It maps your lender’s milestone vocabulary to internal project events, surfaces missing documents or approvals before submission, and tracks tranche disbursement from request to credit with audit-friendly history.

What FireAI tracks:

• Sanctioned, drawn, and undrawn limits by facility and project
• Condition checklist status per draw
• Days from request to disbursement by lender
• Interest and fee accrual against drawn balances

What you can ask FireAI:

• "Which pending conditions blocked the last two tranches for Site B?"
• "Show tranche disbursement vs construction cash need for Q2"

Projected IRR by project drifts when price assumptions, construction cost curves, and working capital assumptions update informally across teams. Boards see one IRR in the investment memo while operations run on another cost forecast, and nobody ties the gap to timing of collections or interest during construction.

FireAI ties booking price and pace, cost to complete, land and finance charges, and tax and fee assumptions into a project economics layer with transparent drivers. Real estate finance analytics shows base, upside, and downside IRR with contribution from each lever so capital allocation debates use one numbers backbone.

How FireAI solves the problem: It versions assumptions with effective dates, links actuals into the same model spine as forecasts, and highlights which projects moved IRR the most month on month with driver attribution.

What FireAI tracks:

• Unlevered and levered IRR with cash timing
• Margin and payback alongside IRR for governance packs
• Sensitivity to price, pace, cost, and interest
• Variance to business case signed at land or launch

What you can ask FireAI:

• "Which project had the largest IRR drop after the last cost revision?"
• "Show projected IRR if collections slip 5% for Project West"