AI Agents

Context

Built for banking as a Stand-alone Quick Win and used by RM and Servicing teams, this agent solves a very concrete problem: relationship managers needing to jump between multiple systems and screens just to understand “who is this customer and what’s going on with them right now?”. It sits in front of the RM, inside their desktop or CRM, as a single, unified customer snapshot.

What it does

The agent pulls together the key information an RM needs before a call, meeting, or case: core customer details, recent interactions, key products and balances, simple risk or status indicators, and the latest servicing or sales context. From this, it generates a concise, bank-specific summary that answers “who they are, what they have, what just happened, and what likely matters next,” so the RM doesn’t have to click through multiple tabs and internal systems to rebuild that picture every time.

Core AI functions

Its core function is summarization: it reads structured and semi-structured customer data and recent activity from existing systems and condenses it into a short, human-ready summary tailored to RM and servicing workflows. The aim is not to invent new analytics, but to translate the information you already have into something quickly usable in a conversation or case.

Problem solved

Without this agent, RMs and servicing staff lose time context-switching—opening core banking, CRM, ticketing, email, and notes just to get to a basic understanding of the customer before acting. That slows down response times, makes calls feel disjointed, and increases the risk of missing relevant context that is technically available but buried. The agent cuts that overhead by providing a single “at-a-glance” view.

Business impact

The primary impact is faster case handling and better-prepared interactions. RMs spend more time engaging with customers and less time assembling context, which shortens handling times and helps conversations feel more informed and personalised. For Tier 2–3 banks, it’s a practical way to improve RM productivity and customer experience without a full core or CRM replacement.

Integration and adjacent use cases

Integration complexity is low: the agent reads from existing customer, product, and interaction systems and surfaces its summary directly in the RM desktop, CRM, or servicing tools, writing back only lightweight context where needed.

Common combinations in this stack:

• Alternative Risk Assessor to add an alternative-data risk view into the RM’s snapshot for thin-file customers;

• Customer Profiling & Product Recommender to surface a simple “next best product” view alongside the customer summary;

• Credit Score Improvement Assistant to give RMs ready-made, concrete suggestions customers can follow to improve eligibility;

• Adverse Media Screening Agent to overlay a quick adverse-news signal on higher-value or higher-risk profiles;

• Inbound Email Agent (RM Copilot) to summarise inbound client emails and draft replies while linking back to the same 360 view; and

• Legal Contract Drafting Assistant to generate clean draft contracts and confirmation letters once RM conversations turn into concrete product agreements.