A briefing system that replaced weeks of research for the merchant organization of a global card network.
Retrieval-augmented generation over internal transaction intelligence and external signals, producing executive-ready client briefs, aligned with governance, compliance and risk requirements.
A relationship manager preparing for a client meeting, renewal or pricing conversation had to hand-assemble the picture from many disconnected systems. The process took weeks, quality varied by team, and upsell and cross-sell signals were missed.
Design and delivery of a RAG solution that fuses two kinds of data. Internal: charge volume, transaction history, relationship and contract history. External: news, announcements, regulatory sources and public signals, continuously ingested, cleaned, de-duplicated, entity-resolved, chunked and embedded into a vector index with freshness management.
At generation time the system retrieves the most relevant and recent evidence for a merchant, combines it with precomputed analytics and forecasts, and an orchestration layer prompts a large language model to write each section of the brief. Every number is injected from the analytics layer, never generated; an automated validation step checks figures against source before release.
Replaced a multi-week manual process of research, analysis, chart building and deck assembly with an automated system; made insight quality consistent across client teams; delivered from proposal to production in roughly one quarter. No measured figures exist for this engagement; none are published.
Grounded generation, with every number from a governed source and a validation gate before release, is what makes generative AI acceptable in a regulated institution.