Transforming historical email archives into a Generative AI knowledge base

A major retail chain in the office supplies, electronics, and printing services segment has transformed its historical email archives into a searchable knowledge database powered by Generative AI. The implemented solution (AWS Bedrock + OpenSearch Vector Engine) has significantly accelerated customer request resolution, improved support quality, and created a foundation for further data-driven initiatives.

16. september 2025 ┃ 4 minút čítania

Challenge

Over the years, the client accumulated a vast amount of customer emails stored in PST archives and other legacy systems that were difficult to access. Within this unstructured data lay valuable insights about customer needs, recurring requests, and demand trends. Traditional search methods, however, were slow and often returned irrelevant results.

Key Challenges:

Inefficient customer support – long search times for historical communication.
Missed business opportunities – patterns in historical data were not utilized.
Operational bottlenecks – manual processing of PST files was labor-intensive.
Knowledge loss – organizational memory was tied to individuals rather than systems.
Competitive risk – need to accelerate data processes to keep up with competition.

Solution

In collaboration with an implementation partner, Generative AI Smart Assistant was designed and deployed for semantic search in email archives and extraction of business-relevant patterns.

Key components:

Foundation models via Amazon Bedrock for NLU and response generation.
Amazon OpenSearch Service (Vector Engine) as an index/vector database for semantic search.
Amazon S3 for staging and storing raw archives.
API Gateway, AWS Lambda pre-orchestration of queries.
Amazon DynamoDB for managing conversation state and metadata.

Functionality of the solution:

Semantic search (meaning > keywords) across tens of thousands of historical reports.
Generating contextual responses and summaries for support agents.
Identification of business patterns (repeat orders, recurring questions, potential demand for products).

Architecture (in brief)

Ingest pipeline: PST → parser (external) → S3 (staging) → text cleaning → vectorizer → OpenSearch (vector indices).
AI pipeline: API Gateway → Lambda → Bedrock (model) + OpenSearch (retrieval) → DynamoDB (session state) → UI.
Security & observability: VPC endpoints, CloudTrail, CloudWatch, GuardDuty, encryption in S3, TLS for APIs.
CI/CD and model lifecycle: Git repository, CodePipeline/CD (or equivalent CI), human checks for prompt changes.

Implementation

Project phases:

Analysis & design – data flows, security rules, multi-account AWS architecture.
Ingest & cleaning – external PST parsing, text normalization, upload to S3.
Vectorization & indexing – creating embeddings and populating OpenSearch.
AI orchestration – Bedrock + Lambda + OpenSearch integration, prompt configuration, and human-in-the-loop testing.
UI & adoption – simple interface for agents + training.
Security & monitoring – VPC, IAM least-privilege, CloudWatch, CloudTrail, GuardDuty.

Deployment into production took place in iterations: pilot (support team) → expansion → stabilization.

Timeframe (from first sprint to pilot): ~4 months (iterative approach).

Results and benefits

The solution deployment delivered measurable improvements in the efficiency and quality of request processing. Key metrics confirmed a significant reduction in ticket resolution time and higher accuracy in information retrieval. In addition to immediate results, the project also demonstrated a long-term benefit in knowledge management and the development of advanced analytical capabilities.

Time to Resolution (support)
- Before implementation: 10–15 minutes spent manually searching through historical communication.
- After implementation: 2–3 minutes (≈70% reduction).
Search Success Rate (result relevance)
- Before: ~55% relevant findings with keyword search.
- After: >90% relevant results with vector-based semantic search.

Additional benefits:

Reduction of manual workload when processing archives.
Better continuity of knowledge within the organization.
Basis for additional features: attachment processing (OCR), predictive analytics, sales insights.

"The project has transformed archived communications into a practical tool that improves the quality of our customer responses on a daily basis."

— client

Conclusion

The project confirmed that even large, long-term unused archives can deliver immediate business value when connected with modern AI tools. Shifting from manual processing to intelligent search reduced operational losses, accelerated decision-making, and created a reliable foundation for data-driven innovation. The solution proves that knowledge transformation doesn’t have to be risky or costly when built on a scalable and secure cloud architecture.