Your deal, analyzed in plain English.
Describe any commercial acquisition in a sentence. Minervian AI builds the full cash flow model — then stays ready for your what-ifs.
Minervian AI takes a commercial real estate deal end to end: from the offering memorandum and rent roll, through extraction, proposed assumptions, and review, to an institutional-grade model and investment-memo-ready tables. Upload a rent roll, PDF, or screenshot — or just describe the deal — and the AI maps the terms onto structured inputs and fills the gaps those documents leave. A deterministic engine then projects every lease, recovery, loan, and distribution, producing levered and unlevered returns and a full deal summary in five to ten minutes.
No spreadsheet setup
No invented logic
Results in seconds
Built for real complexity
What does the AI actually do?
Seven things, all of them around the model rather than inside the arithmetic: extraction from rent rolls, PDFs, and screenshots; autocomplete for assumptions you have not supplied; adding and editing inputs from plain-language instructions; validation loops that flag incomplete or inconsistent entries; explanations of how any section is calculated; market norms applied to assumptions you do not have to hand; and reasoning about tradeoffs when you ask what-if questions. The calculations themselves are never the AI's job.
What happens to assumptions I cannot provide?
Most offering memoranda supply only 30 to 60 percent of what a complete underwriting needs. Autocomplete proposes the rest across every section of the model — leasing, operating expenses, lease-up, financing, fees, exit, and the waterfall — and keeps looping until nothing is left open, because filling one gap tends to create the next. Each value is marked as proposed, so a first draft comes back complete rather than riddled with blanks. Every proposed figure stays visible and editable, and the validation pass flags anything left inconsistent with the rest of the deal. You are never guessing which numbers came from your documents and which the model filled in.
How is this different from prompting ChatGPT with an offering memorandum?
A general-purpose LLM reads a rent roll well — extraction is a language task. What it cannot do reliably is rebuild a commercial-grade underwriting model on every prompt: expense recoveries grossed up per tenant, multi-tranche levered cash flow, and a promote waterfall. Because it re-derives the structure each time, outputs drift between runs and token spend scales with the size of the model. Minervian AI uses the language model for the language half and a fixed engine for the arithmetic half, so the same inputs always return the same numbers.
How does it compare to Argus Enterprise?
Argus is the long-standing incumbent for commercial cash flow modeling. Four things differ here. Depth: the demanding institutional case is squarely in scope — a large multi-tenant office tower with mixed lease structures, base-year stops, options, renewal probabilities, downtime, and market rent that varies by floor — as are several asset classes inside one deal, S-curve development budgets, New York style tiered commissions, and a waterfall with no fixed structure. AI: documents are read directly, missing assumptions are proposed, and the model can be edited by conversation rather than by form-filling. Access: it runs in a browser, nothing to install, usable from anywhere. Price: $25 per seat per month, published rather than quoted.
Can it handle portfolios and mixed-use deals?
Yes. A single deal can hold multiple buildings, each tagged with its own asset class, and commercial and multifamily can coexist in the same model with their revenue, operating expenses, and exits rolled up together. Deal types cover core, value-add, and development, so a lease-up, a renovation program, and a stabilized asset can sit side by side in one projection.
What does it do when a document contradicts itself?
It asks instead of guessing. Two worked examples from the gallery above. A broker spreadsheet arrives with non-standard column headings: every lease is populated automatically, a rent figure that looks like a monthly total is converted to an annual rate per square foot, and the interpretation is shown for confirmation before anything is written. Separately, a stated 65 percent loan-to-cost is checked against the loan already in the model, and the mismatch is raised before the existing figure is overwritten.
How does it handle ambiguous lease language?
Recovery language is the common case. A lease reading "NNN with a base year" describes two conflicting structures, so the AI asks which was meant rather than picking one silently. What it extracts from a long offering memorandum lands on the input field it belongs to rather than in a summary, so a reviewer checks the assumption in place and changes it there. Signing in unlocks the full gallery, including bridge-to-permanent debt sequencing and splitting an office tower and a retail pad into separately modeled buildings.