AI-powered revenue intelligence and account research platform

B2B service companies lose revenue when commercial knowledge is scattered across email, call recordings, CRM systems, and calendars. Missed follow-ups, hours of manual pre-sales research, and unreliable AI output all erode pipeline and trust. This platform combines two interconnected AI products to solve that problem:

1. Research Automation Engine — an AI agent that aggregates public data sources (web, professional networks, company registries) to generate structured account and prospect research reports, cutting pre-sales research time from hours to minutes.
2. Revenue Intelligence Platform — ingests emails, call transcripts, CRM records, and calendar data to extract commercially significant signals (needs, budget, risk, objections, decisions, stakeholder changes) and action items. Every AI-derived fact carries provenance and a confidence score, and passes through a human-in-the-loop triage workflow before entering the system of record — eliminating the hallucination risk that makes raw LLM output unsafe for revenue decisions.

Built natively on AWS, the platform uses Amazon Bedrock as its LLM quality-control layer, applying cross-model verification, Bedrock Guardrails to prevent hallucinated facts, and automated evaluation pipelines that continuously score extraction accuracy against curated test sets. The architecture is completed by Amazon OpenSearch and Amazon Bedrock Knowledge Bases. Each customer deployment replicates this AWS-native architecture inside the client's own AWS account — ensuring data stays within the customer's security boundary while generating recurring AWS consumption tied directly to platform usage. Already validated internally on a live sales team, the platform is now available for commercial deployment via a paid discovery phase followed by full deployment into your AWS environment.