Every Argus assumption becomes a cited node

Built AI reads your Argus exports and models: property-level assumptions, market leasing profiles, growth rates, recovery structures. Each value lands in the knowledge graph with its origin attached, so a cap rate in an IC memo traces back to the Argus assumption it came from.

Because the graph also holds the GL, the rent roll, and the lease documents, Argus stops being an island. The platform reconciles the model against actuals asset by asset, flags where they diverge, and explains why, with every figure computed on the deterministic engine rather than re-keyed into a spreadsheet.

The rule

Built AI does not replace Argus. It reads it, reconciles it, and puts it to work alongside everything else your firm knows. Analysts keep the tool they trust; the firm gets one governed source of truth.

Forecasting

Argus-parity forecasts, reconciled line by line.

Budgets and forecasts reproduced to full parity with the model, with every divergence surfaced and explained rather than discovered at review.

Scenarios

Sensitivities in minutes, every figure cited.

Exit caps, rent growth, and hold periods run on the deterministic engine, so a scenario grid is a computation you can audit, not a copy-paste.

Monitoring

Assumptions tested against live actuals.

Covenant Watch and the Variance Explainer test Argus assumptions against the live GL, forecasting breaches and drift before the test date.

Built AI reads Yardi live

Every Yardi entry lands in the knowledge graph as it posts, joined to the lease, the asset, and the model line it belongs to. Reconciliation stops being a period-end sprint and becomes a property of the system: breaks surface the day they appear, with the entries that caused them.

Because actuals arrive continuously, everything downstream moves earlier. Accruals are drafted before the period opens, the NOI bridge builds itself as the numbers land, and covenant tests run forward against the live book rather than backward against a closed quarter. Yardi remains the system of record; nothing writes back without a named human approving it.

A continuous close, days not weeks.

GL actuals reconciled to source as they post, accruals drafted ahead of the period, and breaks surfaced with their entries attached.

The NOI bridge, drafted with drivers.

On new actuals the Variance Explainer isolates turnover, concessions, and one-offs, and drafts the narrative with every figure cited.

DSCR and LTV on live actuals.

Covenant Watch projects every test to its date using the live GL and debt schedule, and drafts the lender heads-up for approval.

One normalized book across MRI and everything else

MRI data rarely lives alone: the model is in Argus or Excel, the leases are PDFs, the debt schedule is a spreadsheet. Built AI resolves all of it into one schema, each value pointing back to its origin, so a portfolio question stops requiring three exports and a weekend.

On top of that normalized book, the deterministic engine computes what the firm actually uses: NOI, DSCR, valuations, waterfalls. The agent catalog authors the artifacts, in your templates, pausing for approval before anything leaves your tenant. Lease intelligence stays cited to the term: rent steps, options, and recoveries abstracted from the executed documents and reconciled against MRI, clause by clause.

Onboarding

Onboarding is a read, not a migration. Connect read-only credentials, map the portfolio once, and the graph builds itself; the first reconciled assets are live in weeks. One client onboarded 700 assets in a week, because onboarding is reading and normalizing, not migrating.

Nothing about your workflows changes on day one. Adoption is incremental by design: start with one workflow, a variance pack or a covenant watchlist, and expand as the output earns trust. The platform earns write access workflow by workflow, and every write waits for approval. There is no cut-over to plan and no parallel run to staff.