Shortened sales cycle with real-time responses to customer inquiries
Increased sales capacity
Improved accuracy and compliance
Modernizing Sales Enablement in a Regulated Environment
The Challenge: Balancing Innovation with Security
Our customer offers a broad portfolio of business banking solutions, including cash management, payments, deposits and liquidity solutions. A product sales team of more than 100 business relationship managers supports this portfolio, navigating complex product information while operating under strict regulatory, security and compliance requirements.
As our customer embarked on their first generative AI (Gen AI) initiative, they sought a way to increase sales productivity without sacrificing data security, auditability or permissions-aware access. To bring this vision to life, our customer banked on TGS for our deep AWS expertise and proven ability to deliver secure, enterprise-grade solutions in highly regulated environments.
Transforming Sales Conversations With AI-Powered Search
From Manual Search to Instant Insight
Prior to this engagement, the sales team relied on manual searches across SharePoint repositories, product documentation and user guides to answer client questions. They found this approach time-consuming and often impractical during live meetings or calls; it introduced delays, increased the risk of inaccuracies, and limited each representative’s ability to scale beyond supporting more than 50 customer relationships.
Recognizing the opportunity to transform how sales teams access and apply institutional knowledge, our experts introduced a conversational, Gen AI approach. By implementing Amazon Q Business, we enabled sales representatives to ask natural language questions and receive fast, accurate and citation-backed answers grounded in approved sales materials. This brought trusted insights directly into the flow of client conversations.
Amazon Q Business Architecture
Secure, Scalable and Permissions-Aware by Design
We implemented Amazon Q Business as the primary conversational AI platform, powered by Amazon Bedrock foundation models with multiregion inference to support scalable, resilient access to knowledge. Using an enterprise-grade retrieval-augmented generation (RAG) architecture, the solution indexed our customer’s sales content stored in Amazon S3, extracting text, enriching metadata, and applying intelligent chunking to ensure accurate and contextually relevant responses.
Critically, the solution preserves existing SharePoint access controls. Permissions-aware retrieval ensures users see only information they are authorized to access, maintaining compliance with regulatory and security requirements.
To streamline access, we integrated AWS IAM Identity Center with the bank’s identity provider using SAML 2.0—eliminating the need for separate AWS user accounts while enforcing centralized authentication policies and MFA. All access is granted through temporary credentials via AWS STS, removing long-lived access keys and strengthening security posture.
Bold indicates key services used in the project.
Responsible AI Built In
Relentlessly Focused on Trust and Compliance
As a trusted AWS Premier Tier Services Partner with the AWS AI Competency, TGS helps customers design and implement secure, cost-effective Gen AI and agentic AI solutions that deliver measurable business outcomes across cloud initiatives.
From Day 1, our experts embedded responsible AI controls using Amazon Bedrock Guardrails. Each query now passes through a multistage safety pipeline, including toxicity filtering, topic validation, blocked-phrase enforcement and denied topic controls, to ensure responses remain aligned with approved business domains.
For observability and audit readiness, Amazon CloudWatch tracks chat usage, index metrics and sync jobs, while AWS CloudTrail captures all API activity with full user identity context. Private connectivity via AWS PrivateLink ensures traffic never traverses the public internet.
The engagement followed a proven three-phase approach:
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Project setup: confirmed system access, designed the solution architecture and identified pilot users
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Experimentation: configured Amazon Q Business, conducted iterative prompt engineering with SME validation and developed sales-focused prompt playbooks
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Readout: demonstrated the prototype, trained pilot users and completed operational handover
We delivered comprehensive documentation and knowledge transfer, enabling our customer to operate, maintain and scale the solution independently.
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Real-World Results
The Amazon Q Business implementation immediately transformed how the bank’s sales team accessed and applied product knowledge.
Sales representatives now retrieve verified, citation-backed answers in real time—replacing manual SharePoint searches and reducing reliance on memory during high-stakes client conversations. Natural language querying supports meeting preparation, objection handling and follow-up communications, improving speed and confidence.
The overall impact includes:
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Expanded sales capacity: AI-augmented expertise enables relationship managers to scale from approximately 50 customers to a range of 50 times to 100 times more capability across thousands of knowledge dimensions, resulting in quicker sales cycles.
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Faster, more accurate responses: Citation-backed answers reduce risk and improve trust during client interactions.
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Enterprise-ready AI foundation: The solution enabled our customer to establish their first production Gen AI implementation on AWS, positioning the bank for broader adoption across departments.
