CASE 01
FINTECH · GROWTH STAGEREG D · ROLLING
$120M
$120 million Regulation D raise by an AI-managed investment platform entering institutional markets.
Seed through growth equity. The investor presentation, memorandum and financial model, prepared for the partner meeting and the investment committee that follows.
Sell-side preparation for an acquirer's committee. The confidential information memorandum, management presentation and a model built to withstand diligence.
Venture debt and credit facilities. A lender package that presents runway, margins and coverage the way a credit committee reads them.
Corporate and commercial partners assessing a company before they commit. Materials that establish financial durability and strategic fit.
Board packs and investor updates that reconcile to the approved plan, period after period, ahead of the next financing.
A senior executive weighing the plan before accepting equity. A clear, defensible account of the business, its trajectory and the role.
Each task is assigned to the model that performs best on it under evaluation, across Anthropic, OpenAI and Perplexity. Reasoning models handle judgment and complex analysis, smaller models handle drafting and extraction, and research models retrieve cited sources. New models are adopted once they outperform on the firm's tests.
Language models locate and extract figures but do not calculate them. All arithmetic is performed in code using exact decimals, with rounding applied only to the reported figure, so identical inputs always produce identical results.
Figures are treated as verified only when they recalculate from their sources at the precision, units and period shown. Subtotals, balance checks and rollforwards are tested as arithmetic, and any failure stops the document.
Each figure is linked to the cell, record or contract page it came from, or to the calculation that produced it. Missing information is left blank and annotated, and corrections are recorded as new versions.
Material outputs are reviewed by separate systems running on different providers from those that produced them. Accuracy is tested against sealed benchmark cases graded outside production, and each confirmed error is added to the permanent test set.
Accounting and policy decisions are made by named individuals and recorded as rulings. The founder directs each engagement, and every document a client relies on carries a responsible name and a date.
Market data is drawn from current, cited research rather than model recall. Benchmark thresholds are maintained in a versioned library and refreshed each quarter from published studies.
Client materials are processed in isolated environments, with document parsing sandboxed. Each engagement's data is held separately, encrypted in transit and at rest, and confidential materials are exchanged under NDA.