LLM-Generated Executive Briefings: Automating 10-K & Earnings-Call Analysis for Enterprise Sellers
Year: 2025
Type: article
Abstract: Outbound noise is at an all-time high and generic outreach underperforms. Recent benchmarks put cold-email replies and cold-call success in the low single digits, pushing enterprise sellers toward deeper, company-specific context. This paper proposes a practical, single-pass long-context approach that ingests a target company’s latest 10-K/10-Q and earnings-call transcript, then produces a seller-ready brief and 2 - 3 persona-specific draft emails. Each recommendation includes a verbatim quote with a precise source reference (timestamp or filing section) to ground the demand hypothesis. We detail an AWS-aligned implementation (Bedrock + Lambda + S3) and a lightweight evaluation rubric (factuality, utility, and operational metrics). We also discuss a data-fusion extension that incorporates CRM and conversation-intelligence data (e.g., meeting notes) to tailor outputs to an account’s live opportunities. A brief sidebar outlines optional enhancements (ephemeral chunking and retrieval) for teams that need lower latency/cost at scale. While the reference design is AWS-centric, the pattern is portable to any stack with equivalent services.
Author: Adish Rai
Institution: Amazon (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414020043