Case study
AI Offer Memorandum Parsing & Investment Intelligence
- Client: Finance Lobby
- Industry: Real Estate & PropTech
- AI capability: Intelligent Document Processing
- OM analysis time, down from several hours
- <3 min
- Extraction accuracy on critical financial fields
- 95%+
The challenge
Every OM is different, and the numbers hide inside tables
Offering memorandums arrive in countless formats from different brokerages, and each one hides the same underwriting inputs in a different place: NOI, cap rates, rent rolls, tenant summaries.
Generic OCR fails on complex financial tables, and misses context that only exists in narrative paragraphs. Finance Lobby needed a system that reads an OM the way an experienced underwriter does, at marketplace scale.
What we built
Document AI built for commercial real estate
Layout, tables and narrative are handled by the model best suited to each.
Offering memorandum
Harbor Point Plaza
182 units · Sample listing
Financial summary
| Gross potential rent | $4,620,000 |
| Vacancy allowance | −$323,400 |
| Operating expenses | −$1,440,000 |
| Net operating income | $3,180,000 |
Market overview
1/4Any brokerage format goes in. Every page is classified, so the pipeline knows what it is looking at.
An offering memorandum runs 40 to 150 pages, and the numbers that decide a deal sit inside tables and paragraphs. The pipeline reads the whole document and returns one structured investment record in under three minutes.
Layout detection
Reads the structure of each page first, so tables are treated as tables and prose as prose.Financial table extraction
A table transformer rebuilds nested rent rolls and expense grids into clean rows and columns.Microsoft Table TransformerNarrative extraction
Language models pull the qualitative parts: risks, market positioning and lease expiry notes.LLMOne structured record
Every field lands in a single record your underwriting model and deal filters can read.JSONCSV
The impact
Underwriting starts with data, not data entry
- OM analysis in under three minutes
- 95%+ accuracy on critical financial fields
- Any brokerage format accepted
Analysing an offering memorandum went from several hours to under three minutes, so brokers and lenders can filter and underwrite far more deals in a day.
Extraction reaches 95%+ accuracy on critical financial fields, and every value can be checked against the page it came from.
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