As iOPEX’s AI footprint scaled, its ServiceOps and Product Ops teams found that the institutional knowledge needed to support that footprint — wikis, knowledge bases, support tickets, PDF documents, and Slack/Teams conversations — remained fragmented and ungoverned:
iOPEX’s Cloud & AI Engineering team designed a Retrieval-Augmented Generation (RAG) based Agent Assist capability that ingests and unifies wikis, tickets, PDFs, and chat content into a single, governed knowledge layer — surfacing grounded, cited answers directly inside the ElevAIte platform and Command Agents.
Unstructured data enters through Amazon API Gateway and an Application Load Balancer. AWS Step Functions orchestrates the processing pipeline, invoking Amazon Bedrock for language understanding and generation, Amazon Textract for document and OCR extraction, and Amazon Comprehend for entity, key-phrase, and sentiment extraction. Content is chunked, embedded, and indexed with metadata in Amazon DynamoDB alongside processed documents in Amazon S3, so that at query time the system performs semantic search over the index and uses Bedrock to generate a grounded, source-cited answer — with LangChain and LangGraph composing the multi-step retrieve-reconcile-recommend-update workflow.
| AWS Service | Role in the Solution |
|---|---|
| Amazon Bedrock | Grounded, retrieval-augmented language understanding and generation |
| Amazon Textract | Document and OCR extraction from scanned/PDF sources |
| Amazon Comprehend | Entity, key-phrase, and sentiment extraction; PII detection |
| AWS Step Functions | Orchestrates the ingestion-to-answer processing pipeline |
| Amazon S3 | Governed storage for raw and processed documents |
| Amazon DynamoDB (with DAX) | Metadata and vector/keyword index for retrieval |
| Amazon SQS | Decouples ingestion bursts from downstream processing |
| AWS Lambda / ECS (Fargate) / EKS | Serverless and containerized compute for the pipeline |
| Amazon API Gateway / ALB | Public-facing entry point for wikis, tickets, PDFs, chat, and email |
| AWS IAM / Identity Center | Least-privilege access and identity federation |
| AWS KMS | Encryption at rest with customer-managed keys |
| AWS WAF, Shield, GuardDuty | Perimeter protection and continuous threat detection |
| Amazon CloudWatch, CloudTrail, X-Ray | Metrics, logs, alarms, and immutable audit trail |
This engagement is documented at the solution-design stage (SDD v1.0, August 2026); the figures below are the design targets iOPEX will measure against once the solution is live, rather than reported production results.
Beyond the metrics, the solution is designed to consolidate fragmented knowledge into one governed layer, give newer agents the same quality of grounded guidance as tenured staff, and establish a reusable, modular pattern that iOPEX intends to extend from ServiceOps and Product Ops to RevOps, Finance Ops, Field Service, and Marketing Ops.
Specific, realized dollar savings were not yet available at the time of this case study — the engagement is still at the solution-design stage. iOPEX is tracking cost impact through a declining cost-to-serve KPI (total compute, inference, and operational cost per resolved ticket/case), measured quarter-over-quarter via AWS Cost Explorer and AWS Budgets once live. The value drivers behind that KPI are already defined in the design:
To convert this into a measured ROI figure, iOPEX would need to supply its current cost-to-serve baseline (cost per resolved ticket/case) and target rollout volume, which can then be modeled against post-go-live Cost Explorer and ServiceOps ticketing data. We’d recommend adding a measured ROI figure to this case study once the solution has been in production and the KPI targets in Section 12 have been validated.
