Artemis · Echo — the decision-making layer for investment projects
Complex real estate development and commercialization projects suffer from the exact same disease as market analysis. Decisions regarding multi-million budgets are made based on massive, multi-sheet Excel financial models that only their author fully understands. Every attempt to change a scenario requires hours of recalculation, leaving the investment committee with a static report representing a single version of the truth.
Artemis and Echo reshape these proportions. They pull advanced financial and operational data from pre-calculated Excel models and leverage Clio to load them into the database across various business variants. Iris transforms these raw structures into an interactive, clean visual map accessible to the investor instantly via a web browser.
A single screen. The complete structure of the three financial statements. Data drill-down from macro levels right down to individual budget line items. Zero formula searching.
The problem Artemis · Echo solves
A real estate financial model is a living organism. Capital expenditure (CapEx) schedules, rental rates, commercialization levels, operating expenses, and seasonality parameters for hotels and short-term rentals (STR) fluctuate constantly.
The core issue is that traditional BI systems are far too rigid to reflect the multi-variant nature of DCF models, while traditional Excel sheets remain unreadable during board meetings. Investors are left stranded with a file where tweaking a single parameter demands clicking through ten different tabs, and the constant risk of a broken formula paralyzes the decision-making process.
Reality without Artemis · Echo
Comparing an optimistic scenario to a pessimistic one requires opening separate files or manually copying tables over to PowerPoint presentations.
CapEx structure analysis hits a wall at broad categories — uncovering the exact cost of window joinery or a BMS system requires digging deep through construction cost estimates.
The impact of seasonality on liquidity (Cash Flow) is estimated roughly based on annual averages, instead of a precise, day-by-day analysis mapped across the entire year.
The board and the fund operate on reports that are a week old. There is a complete lack of rapid data exports containing the structural context required for external systems or AI models.
What Artemis · Echo changes
Artemis visualizes the complete business logic of a project. It provides investors with an interface where entire financial statements can be toggled between scenarios A, B, C, and Baseline with a single click. Every chart, table, and cost structure rebuilds instantly.
The system goes beyond merely presenting dry outcomes; it enables immediate drill-down. Spot an anomaly in revenue? Click it to descend directly to the level of a specific commercial space (Space 1, 2, 3) and its daily occupancy rates.
System architecture — how data flows from model to screen
Layer One: Artemis – Advanced Excel Model + Clio
The computational core remains a robust, high-precision Excel financial model that maps the underlying physics of the investment. The Clio engine automatically aggregates generated business variants, cleans the data, and pipes it into a structured database, safeguarding model history and versioning.
Layer Two: Iris Financial Render (React)
Iris hooks directly into the API to render financial data through dedicated UI components. The system automatically structures the financial statements (Three Statements) and commercialization models, maintaining mathematical alignment between macro charts and micro tables.
Layer Three: Multi-Export & AI Prompt Context
All calculated data is immediately available for download in XLSX, PDF, and a tailored AI JSON format. The JSON file contains the complete structural financial context, perfectly packaged for language models to interpret results automatically.
What Artemis · Echo calculates and Iris visualizes
Cumulative DCF Breakeven — multi-scenario break-even point
Traditional charts reflect a single return curve. Iris overlays cumulative discounted cash flows for all scenarios simultaneously onto a single timeline. The cursor precisely tracks the break-even zero-crossing and maximum capital exposure points, exposing the project’s margin of safety in a fraction of a second.
Financial Statements Model — integrated three statements
Visualization of the Income Statement (P&L), Balance Sheet, and Cash Flow Statement across dynamic annual trend charts. Investors can analyze relationships between Incomes vs. Costs, EBITDA vs. Cumulative EBITDA, and track dynamic correlations between capital structure and year-end cash equivalents.
Monthly Drill-down — micro-accounting data structures
No more blind trust in aggregated annual figures. Clicking any year on the chart expands a detailed monthly breakdown (from M01 to M12) directly underneath. The system exposes granular cost lines (depreciation, external services, taxes, fees) and computes monthly Cumulative EBITDA for the selected period.
CapEx & Budgets Drill-down — from broad categories down to single licenses
A comprehensive breakdown of capital expenditures (CapEx) and fixed costs based on budget hierarchy. Users can audit building construction costs step-by-step or drill down into operating budget details (e.g., the SaaS category), monitoring monthly outlays for hosting, domains, or specific Microsoft Power Automate and Power BI licensing.
Income Structure — revenue diversification split by commercial spaces
A histogram and linked data table showcasing the structure of revenues (Incomes) over time, dissected by individual commercial units (e.g., Space 1, Space 2, Space 3). It highlights instantly which segment drives the bulk of financial performance and which falls short of model projections.
Daily Occupancy Heatmap — the physics of seasonality and occupancy
The deepest operational layer of the commercialization model (tailored for hotels/STR). An interactive Heatmap spells out occupancy levels (Occupancy %) for every single day of the year (days 1 to 31 across 12 months), incorporating strict market pricing and seasonality parameters. It exposes the actual operational yield of properties day by day.
Budgets & OpEx — granular operating cost control
The system eradicates flat, generalized cost categories, substituting them with a complete operational hierarchy. The panel allows for precise filtering of fixed and variable costs by year and specific category (e.g., Services, SaaS, Office Expenses, Insurance).
In the detailed view, every single budget line item (such as hosting, domains, administration, or specific seats like Power Apps, Power BI Pro, and Power Automate) is broken down month-by-month (Jan–Dec), complete with an automatic calculation of the annual total for that subcategory. This provides comprehensive visibility into operational spend and instantly flags the exact moment a budget begins to drift.
The AI Layer — interpreting high-precision calculated data
The embedded AI module avoids guesswork and steers clear of generic market advice.
Utilizing the “Interpret with AI” function, the system ingests the generated JSON file containing the hard, complete computational context straight from Excel. The algorithm evaluates the loaded statement variants, assists in interpreting the results in plain natural language, and translates them into a cohesive narrative structure.
Implementation impact — practical outcomes
- Instant project resilience testing. Instead of tedious parameter adjustments within worksheets, the investment committee tests model resilience against escalating construction costs or dropping occupancy rates via simple variant selectors.
- Budget transparency for partners and LPs. Passive investors and funds receive a clean, professional dashboard that fosters trust — displaying the complete structure of the three financial statements without demanding a trek through a jungle of formulas.
- Cost drill-down down to individual line items. The system enables rapid transition from macro CapEx sums to atomic line-item estimates, ensuring tight oversight over budget contingency burn rates.
- Seasonality framed mathematically, not intuitively. By mapping out day-by-day occupancy heatmaps, liquidity movements (Cash Flow) reflect the real physics of the underlying business rather than smoothed-out averages on a sheet.
- Data structured for downstream workflows. A single click extracts a polished PDF report for presentations, a clean XLSX sheet for in-house analysts, or a structural JSON file configured for automated external reporting frameworks.
FAQ
How does data move from the spreadsheet into the system?
The entire pipeline runs automatically via the Clio module. The financial model file, adhering to the specified structure, is mapped and ingested. Each business variant computed in Excel surfaces as a distinct, standalone scenario (A, B, C, Baseline) directly inside the Iris panel.
Can I control permissions and visibility for budget data?
Yes. The visual layer within Iris incorporates comprehensive role management. You can determine whether a specific partner, investor, or project manager views the full scope of the three statements or gains access exclusively to designated operational or capital budgets (e.g., CapEx for a specific project phase).
What is the purpose of the AI JSON button?
The button exports the comprehensive set of calculated metrics and financial structures bundled alongside a specialized context prompt. This file is formatted explicitly for advanced LLMs, allowing you to feed your project’s analytics instantly into external AI engines to generate narrative-driven reporting.