DeCyph for Real Estate PE

DeCyph automates the slowest part of underwriting: it parses rent rolls, T12s, and OMs, maps line items to your chart of accounts with NOI reconciliation checks, populates your pro forma model, and generates deal highlights and a quick summary — so the team starts from a completed first pass instead of a blank spreadsheet.

DeCyph is built for real CRE documents rather than generic chat: it reads rent rolls, T12s, and offering memoranda, produces standardized financials with validation checks, computes underwriting metrics like economic occupancy, loss to lease, T3 annualized revenue, and NOI per unit, and screens every deal for risk flags like understated expenses or aggressive exit caps.

Upload the rent roll or T12 to DeCyph in Excel or PDF. The platform extracts unit-level and line-item data, standardizes it, and generates analytics automatically: unit mix by bedroom and renovation status, in-place versus effective rent, occupancy, renewal and turnover rates, delinquency, loss to lease, and trailing-3 versus trailing-12 trends — with charts your team can drop into a memo.

DeCyph takes over first-pass document review, extraction, and model population. The team screens standardized outputs, risk flags, and deal highlights instead of rebuilding every model, keeping senior attention on the deals that clear the bar — so deal flow scales without headcount.

DeCyph benchmarks each new deal against your internal comp set — every deal your team has analyzed — alongside rent and expense comps near the property. Combined with configurable risk flags and custom AI agents (like OM reverse-pricing, which back-solves what the broker price implies), this shows where a deal aligns with or breaks from your thesis.

DeCyph ships a hosted MCP (Model Context Protocol) connector: add it in Claude or ChatGPT, authorize with your DeCyph account, and your AI assistant can list your deal pipeline, pull deal attributes, and read standardized income statements directly. Teams use it to build their own AI agents on top of DeCyph — an IC-prep assistant in Claude, a deal-screening workflow in ChatGPT — with DeCyph as the underwriting data layer. Setup takes a few minutes; see the MCP setup guide under Resources.

DeCyph reviews deal documents, standardizes financials, and surfaces key assumptions automatically, then layers on automated risk flags (over-market rents, understated expenses, low DSCR, aggressive exit cap rates), sponsor deep dives with SEC enforcement and litigation checks, property tax review, and a due diligence workspace for tasks, documents, key dates, and issues.

DeCyph extracts unit-level data from the rent roll and computes in-place versus market rent by unit type and renovation status, loss to lease, lease-expiry and rollover schedules, renewal and turnover rates, and delinquency exposure — benchmarked against rent comps near the property. That means the mark-to-market and rent growth assumptions in your model are backed by data instead of a broker narrative.

DeCyph benchmarks on two axes. Externally, it pulls rent comps and expense comps near the property and market analysis by property class, so in-place rents and each expense line are compared against what similar assets actually run. Internally, every deal your team analyzes builds a proprietary comp set — assumptions, metrics, and outcomes — so each new opportunity is measured against your own underwriting history, not just market averages.

Spreadsheets are useful for modeling, and generic CRE software can store data, but DeCyph automates the work in between: it parses complex Excel and PDF financials, maps T12 line items to your preferred chart of accounts with revenue, expense, and NOI reconciliation checks, populates your pro forma template, and lets you query everything with an AI assistant instead of rebuilding the same analysis manually.