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CASE STUDY

Secure Agentic AI Architecture for Marketing Insights and Personalization on AWS Bedrock

 
 

Mactores built a secure, scalable Agentic AI platform on AWS Bedrock for Total Expert to automate marketing insights, improve personalization, and ensure SOC2-aligned, responsible AI operations.

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About_Customer_TotalExpert

About 
The Customer

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Total Expert provides a leading marketing and customer engagement platform used by financial institutions to deliver personalized communications. Their cloud-native ecosystem powers data-driven customer insights, orchestrated campaigns, and compliance-aligned engagement across the customer lifecycle.

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Customer Situation

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Total Expert needed to modernize how marketing insights, summaries, and recommendations were generated across customer journeys. Existing processes required heavy manual analysis, lacked real-time context, and created operational bottlenecks for relationship managers. At the same time, the company needed to maintain stringent SOC2 Type II controls while handling sensitive CRM and behavioral data. Any GenAI solution requires high accuracy, low latency, traceability, and alignment with internal governance. 

Total Expert also needed seamless integration with its AWS analytics platform, proprietary APIs, and identity providers, while ensuring full explainability and auditability of AI-driven decisions. The challenge was to balance autonomy and control through a secure, scalable Agentic AI framework.

Our Approach

Mactores designed a modular Agentic AI architecture using AWS Bedrock models, Claude 3 Sonnet, Titan Text Lite, and Titan Embeddings, accessed through private VPC endpoints. Lambda, Step Functions, and ECS Fargate-powered agent workflows for data retrieval, reasoning, validation, and content generation. A SOC2-aligned IAM model enforced granular access, short-lived credentials, mTLS, and per-agent isolation. 

Responsible AI guardrails, robust governance, validation layers, and audit logging ensured the production of ethical, accurate, and explainable outputs. Observability, enabled by CloudWatch and OpenSearch, facilitated continuous monitoring and improvement.



Business Outcomes

Total Expert reduced manual review workload by 65% and accelerated customer insight delivery with real-time summarization and recommendation generation. The platform delivered consistently accurate, explainable outputs with zero safety or bias incidents. Marketing and analytics teams gained reliable automation while maintaining full governance, auditability, and operational confidence.

Technical Outcomes

The solution provided sub-2-second average inference latency, secure VPC-contained Bedrock access, and auto-scaling compute using Lambda, Fargate, and Step Functions. IAM boundaries enforced zero-trust agent interactions, while KMS encryption, audit logs, validation layers, and moderation guardrails ensured security and compliance. Unified observability enabled rapid debugging, drift detection, and continuous optimization.
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Getting 
Started

Mactores began with discovery workshops to understand Total Expert’s business goals, compliance constraints, and integration dependencies. A benchmarking phase evaluated Bedrock models for accuracy, cost, and latency. Architectural design established secure connectivity, IAM boundaries, and orchestration patterns. A pilot deployed MVP agent workflows using synthetic data before scaling to production. Mactores delivered IaC templates, governance documentation, and training to enable seamless adoption and ongoing iteration.

 

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