AI-Assisted Earnings Processing

Processing one earnings transcript manually takes 45-60 minutes for a thorough analysis. Processing twenty transcripts — a typical competitive set during earnings season — takes two weeks. AI reduces this to hours. The methodology: feed each transcript through a structured extraction prompt that pulls language signals, financial data points, forward guidance, and defensive Q&A patterns. Then feed all extracted data through a cross-company synthesis prompt that identifies market-level patterns. The human analyst reviews the synthesized output, validates the key findings, and produces the intelligence brief.

## AI Earnings Extraction Prompt

Analyze this Q[X] earnings transcript for [Company].

EXTRACT:
1. LANGUAGE SIGNALS: Key phrases for growth, efficiency,
   investment, competition. Compare to last quarter's
   language if provided.

2. FINANCIAL HIGHLIGHTS: Revenue, margins, segment
   breakdown, guidance. Note any metric selection changes.

3. FORWARD GUIDANCE: Every forward-looking statement
   with implied timeline and specificity level.

4. DEFENSIVE Q&A: Questions that received hedged,
   redirected, or non-answers. Note the topic probed.

5. NARRATIVE-NUMBER GAPS: Where executive commentary
   diverges from financial data.

OUTPUT: Structured JSON with fields:
{ language_signals, financial_data, forward_guidance,
  defensive_patterns, narrative_gaps }