Investment & Treasury Analytics

Capital deployment by phase breaks when land, construction, interest, and fee lines sit in different modules with different project phase tags. Boards see one aggregate spend number while project teams work off another cut of the same project, and nobody ties deployment pace to the original investment memo or construction S-curve without manual reconciliation.

FireAI maps approved phase budgets, actuals, and committed but unspent lines to a single project and phase grain with rules you own for capitalization versus expense. Real estate investment analytics shows cumulative deployment versus plan, forward burn by quarter, and variance drivers by bucket so capital deployment analytics stays comparable across projects.

How FireAI solves the problem: It keeps one mapping between ERP cost centers, project phases, and investment memo line items, refreshes as vouchers and JVs post, and tags scenarios so you can answer "what if" without rebuilding the spreadsheet monthly.

What FireAI tracks:

• Deployed, committed, and unutilized budget by phase and work package
• Deployment pace versus construction physical progress where linked
• Interest and fee capitalization against facility usage
• Variance to last approved reforecast with driver notes

What you can ask FireAI:

• "Show capital deployment by phase for Project A versus plan for the last two quarters"
• "Which phases are more than 10% over capital deployment plan YTD?"

Lender covenant monitoring fails when DSCR, loan-to-value, and minimum liquidity tests are calculated in static workbooks with different debt balances or EBITDA definitions than credit files. A surprise breach surfaces days before a test date, and treasury has little time to cure or renegotiate with evidence.

FireAI ingests loan agreements (where you provide keys), monthly trial balances, interest accruals, and defined cash flow elements so covenant tests roll forward on a consistent source of truth. Real estate investment analytics highlights headroom, trend, and the three drivers moving each ratio month on month for lender covenant monitoring.

How FireAI solves the problem: It maps your covenant text to the fields you connect, versions assumption sets for pro forma tests, and flags breach risk early when trajectory crosses internal buffers before the formal test date.

What FireAI tracks:

• DSCR, ICR, net worth, and project-specific covenants per facility
• Headroom in percentage points and absolute terms
• Utilization, undrawn limits, and interest burden feeding ratios
• Historical test outcomes and waivers for audit and board packs

What you can ask FireAI:

• "What is the lowest projected DSCR for Site North over the next four quarters?"
• "Which covenants are within 0.2x of limit under stress case?"

Investor return tracking (XIRR and IRR) is disputed when drawdown dates, distribution dates, and management fee offsets live in different emails and side letters. LPs see one return in a quarterly letter while the internal model uses a different set of actual cash points, and nobody explains the gap without a long reconciliation pass.

FireAI standardizes each capital call, distribution, and fee with dates and project tags the way your fund agreement defines, then computes and reconciles XIRR and IRR to your official ledger. Real estate investment analytics gives sponsor and IR teams a single investor return story with version history and scenario labels.

How FireAI solves the problem: It ingests or accepts structured cash point feeds, validates for missing or duplicate events, and ties commentary to the same return run used in leadership reviews so XIRR and IRR do not fork across teams.

What FireAI tracks:

• Unlevered and levered IRR, and XIRR, by fund, series, and project as configured
• Distribution to paid-in, residual value, and TVPI where inputs exist
• Variance to prior published return with cash timing and NAV drivers
• Side letter or fee timing adjustments when you load rules

What you can ask FireAI:

• "What drove the 80 bps move in blend XIRR since last quarter?"
• "Show investor IRR for Fund I versus the hurdle with actual cash points only"

Escrow utilization tracking is hard when every lender defines permitted debits, minimum balances, and top-up rules differently, and your bank files do not always tag the same project or tranche. Construction payments stall not for lack of cash but for mismatch between what left escrow and what the draw certificate allowed.

FireAI ingests bank escrow statements, draw approvals, and permitted-use lists (where you configure them) so every debit ties to a project, tranche, and purpose. Real estate investment analytics shows utilization of each escrow against caps, idle balances above required minimums, and exceptions for treasury action.

How FireAI solves the problem: It reconciles statement lines to draw requests, flags out-of-sequence or uncategorized debits, and highlights when escrow utilization approaches limits that trigger top-up or consent clauses.

What FireAI tracks:

• Balance, minimum, and available headroom by escrow and project
• Utilization by category: construction, land, taxes, fees as tagged
• Top-up and sweep recommendations against covenant minima
• Exception queue for lines without a matching approval

What you can ask FireAI:

• "Which escrows are within 5% of their utilization cap this month?"
• "Show idle cash above required minimums across construction escrows"