Platform · How Built AI works | Built AI

The building becomes its numbers.

Every tower, lease, and cashflow line resolved into one knowledge graph, with every figure traced back to its source.

Block 37 · Levered cashflow

Deterministic engine · recomputed live

Selected GPR · FY26 = $14.21M traces to the lease schedule (14 leases) and Argus assumption §3.1 · every cell cites its source

Block 37 levered cashflow on the deterministic engine. Cited cells (●) link to the lease, GL line or Argus assumption behind the number.

A real estate knowledge graph, entities, documents, and cashflows in one place

Every institutional real estate firm already has the data it needs; the problem is that it lives in pieces. The rent roll is in Yardi, the model in Excel, the valuation in Argus, the lease a PDF in a data room, and none of them can answer a question that spans all of them.

Built AI's foundation is a single normalized graph that stitches those pieces back together: funds, assets, leases, tenants, loans and covenants modeled as connected entities, not files in folders.

We model the things institutional real estate actually cares about as first-class entities, funds, vehicles, JV structures, assets, units, leases, tenants, loans, covenants, counterparties, connected to the documents that govern them and the cashflows they produce.

Once those relationships exist, questions that used to take an analyst two days collapse into a single traversal of the graph. Which assets in Fund III breach a DSCR covenant if rates move another fifty basis points? Which leases rolling in the next eighteen months sit above market, and what does re-leasing at market do to NOI and to the waterfall? Which counterparties appear across more than one position?

Connect → Normalize → Reason → Author

The platform runs the same four-step loop over everything it touches, and it is worth being precise about what each step does, because the rigor of the later steps depends entirely on the discipline of the earlier ones.

Connect. We integrate with the systems of record you already operate, accounting platforms, the valuation engine, scenario models, the warehouse, and with the unstructured corpus you accumulate on every deal: offering memoranda, term sheets, rent rolls, leases, loan packages, appraisals, K-1s.
Normalize. A lease from one landlord's template and a lease from another's describe the same economic reality in different words. We resolve those into a common schema, the same field names, the same units, the same entity identities, so that "base rent," "minimum rent" and "fixed rent" become one concept the engine can compute against.
Reason. On the normalized graph, the deterministic engine computes. IRRs, DSCRs, debt yields, cap rates, NOI bridges, waterfall distributions, sensitivity tables, all run as real, auditable calculations, not as a language model's best guess at arithmetic.
Author. Finally, the platform writes IC memos, investor letters, covenant packs, variance narratives, lease abstracts, in your templates, with your formatting, with every figure traceable to the graph node that produced it.

The core loop:

1. Connect: Read your systems of record and the unstructured corpus, no migration.
2. Normalize: Resolve every source into one schema, each value pointing back to its origin.
3. Reason: Compute IRR, DSCR, NOI, waterfalls on a deterministic engine, not in prose.
4. Author: Write the artifact in your template, every figure traced, then wait for approval.

The same four-step loop runs over everything the platform touches. The rigor of the later steps depends on the discipline of the earlier ones.

Every source in, every output out

A spreadsheet stores numbers. A document stores words. A graph stores relationships, and relationships are where the answers live. Every source flows into the same normalized graph, and every artifact is authored from it, cited cell by cell.

Integration

We sit on top of your stack, not instead of it

An orchestration layer that reads your systems of record and writes back only on approval. Read-then-write-on-approval, by design.

The fastest way to lose an institutional real estate firm is to ask it to rip out a system it has spent a decade and a fortune trusting.

The read-then-write-on-approval model

Connections default to read. The platform pulls from your systems, normalizes what it finds, and reasons over it, and at that point it has changed nothing in your environment. That is deliberate. An orchestration layer that can silently mutate the books, the model or the warehouse is a liability dressed as a feature.

Comparison

The only stack this complete

The differentiator: Deterministic, not generative, math

The deterministic calculation engine

At Built AI no financial number is ever produced by a language model: every IRR, DSCR, debt-yield test and waterfall tier runs on a native deterministic engine that shows its work cell by cell.

Document-driven scenarios

From a document to a decision in one pass

The clearest way to see the platform work is to follow one document through it: a term sheet, an offering memorandum, a loan package or a rent roll. Built AI compresses what used to take days of manual extraction and re-keying into a single auditable pass: extract → simulate → decide.

1. Upload: Term sheet, OM, loan package or rent roll hits the data room.
2. Extract: Up to 114 fields lifted, each with a confidence flag and a source pointer.
3. Simulate: IRR, DSCR, occupancy and stress scenarios run on the deterministic engine.
4. Decide: A branded, fully traceable IC memo, staged for human review and sign-off.

AI agents: The catalog that runs the lifecycle

A catalog of agents that runs the lifecycle

Every agent does the same three things: it watches for an event, does the work on the graph, then stops and waits for a named human to approve.

Deployment & governance: Your tenant, your model, your control

A platform that handles a fund's leases, loans, books and investor communications has to meet the bar institutional real estate sets for any system that touches capital. Built AI is built to deploy inside your perimeter and run under your policies by default, not as an afterthought bolted on for the security review.