Instead of an analyst spending a full day manually copying numbers from a 200-page PDF into Excel, the AI reads the entire document and builds the model for you in minutes.

Alt-X's Endex platform uses a multi-stage ML pipeline combining OCR, NLP-based table detection, and large language models (likely GPT-4 class or fine-tuned variants via their OpenAI Startup Fund relationship) to ingest raw deal materials—offering memoranda, rent rolls, financial statements, appraisals—and automatically extract structured data including line items, time series, footnotes, and assumptions. The system then maps extracted data to a canonical financial model schema, generating native Excel formulas (not static values) that link revenue builds, expense schedules, debt waterfalls, and returns analyses into a fully functional three-statement DCF or LBO model. Each extracted value carries cell-level provenance metadata linking back to the source document page and paragraph, enabling instant audit verification. This eliminates the most time-consuming and error-prone step in deal underwriting: the manual transcription and structuring of unstructured information into a working model.

It's like having a photographic-memory intern who can read a 300-page offering memo, perfectly type every number into the right Excel cell, and write all the formulas connecting them—in the time it takes you to grab coffee.

The AI acts like a tireless senior analyst who checks every single formula, cross-reference, and assumption in your model for mistakes or suspicious numbers before anyone else sees it.

Financial models in institutional settings routinely contain thousands of interlinked cells, and studies show that over 80% of complex spreadsheets contain at least one material error. Endex deploys ML-based anomaly detection that operates at multiple levels: formula-level validation (checking circular references, broken links, inconsistent units), statistical outlier detection (flagging growth rates, margins, or cap rates that deviate significantly from historical norms or comparable transactions), and structural integrity checks (ensuring balance sheets balance, cash flow waterfalls tie out, and debt covenants are properly modeled). The system learns from patterns across thousands of institutional-grade models to build a baseline of expected relationships between financial variables, then surfaces deviations with confidence scores and suggested corrections. Every flagged issue includes an explanation of why it was flagged and a direct link to the affected cells, creating an auditable QA layer that satisfies both internal compliance teams and external regulators. This transforms model review from a manual, error-prone process into a systematic, ML-augmented workflow.

It's like spell-check for spreadsheets, except instead of catching typos it catches the kind of formula errors that could accidentally make a $500 million deal look like a bargain.

Instead of manually tweaking dozens of assumptions across multiple tabs to see what happens if interest rates spike, you just type "show me what happens if rates go up 200bps and vacancy doubles" and the AI runs it instantly.

Traditional scenario analysis in institutional finance requires analysts to manually identify assumption cells scattered across complex multi-tab models, create data tables, adjust inputs one by one, and rebuild output summaries—a tedious process that limits the number of scenarios teams can realistically evaluate before an investment committee deadline. Endex's natural language scenario engine uses LLMs to parse plain-English queries ("What happens to IRR if exit cap rates widen 50bps and we lose the anchor tenant in year 3?"), map the intent to specific model cells and assumptions using its understanding of the model's structure and financial ontology, execute the parameter changes, and return formatted sensitivity tables and tornado charts directly in Excel. The system can chain multiple variable changes simultaneously, run Monte Carlo–style probabilistic distributions across key assumptions, and automatically generate executive summary narratives explaining the results. Because the LLM understands both the model architecture and financial terminology, it can suggest relevant stress scenarios that the analyst may not have considered, drawing on patterns from comparable deals and macroeconomic indicators. This dramatically expands the decision surface available to investment committees.

It's like having a co-pilot who not only instantly runs any "what if" you can dream up, but also whispers "hey, you should probably also check what happens if the Fed raises rates again" before you even think of it.