Amazon Bedrock Implementation | RAG, Text-to-SQL & Knowledge Assistants

Stratus10 designs and builds Amazon Bedrock solutions that bridge structured and unstructured data, enabling employees and customers to ask questions in plain English across documents, knowledge repositories, and analytics data.

Retrieval-augmented generation (RAG) knowledge bases handle documents, PDFs, and images from sources such as Amazon S3, SharePoint, and Confluence. Structured knowledge bases use natural-language Text-to-SQL to query data warehouses such as Amazon Redshift. Automated ingestion pipelines keep knowledge bases up to date as new data arrives.

Common use cases include internal knowledge assistants, customer-facing search and support, and document-heavy business processes such as extraction, classification, summarization, and routing of incoming content.

Key Benefits:

• Fast path to production: Managed foundation models through Amazon Bedrock with no model hosting to run.
• One question, every data type: Query documents and analytics data together instead of in separate tools.
• Built for scale: Ingestion designed around Bedrock quotas and file limits, with automated pipelines for historical loads and new files.
• Cost control by design: Clean ingestion boundaries, deduplication, and tuned chunking keep embedding, parsing, and inference costs predictable.
• Secure and reproducible: Infrastructure as code (Terraform or CloudFormation) with least-privilege IAM roles, AWS Secrets Manager, and S3 bucket policies across every component.
• Funding support: Stratus10 helps eligible customers apply for AWS GenAI proof of concept funding.

Professional Services Process:

1. Discover: Define the business problem, users, data sources, constraints, and success measures.
2. Prioritize: Compare candidate use cases by value, feasibility, risk, cost, and time to production.
3. Prototype: Validate the highest-value use case with a focused proof of concept, along with AWS funding where eligible.
4. Build and Integrate: Data organization and ingestion pipelines, vector store (e.g., Amazon OpenSearch Serverless or Aurora PostgreSQL with pgvector), Text-to-SQL integration with your data warehouse, access controls, and deployment as infrastructure as code.
5. Evaluate and Operate: Measure answer quality, monitor performance and cost, and refine retrieval.

Deliverables:

• Use-case assessment and prioritized roadmap
• Solution architecture diagram
• Amazon Bedrock Knowledge Base(s) for structured and/or unstructured data
• Automated ingestion pipeline (e.g., scheduled or event-driven AWS Lambda)
• Infrastructure as code for all components
• Security configuration: IAM roles and policies, Secrets Manager, S3 bucket policies
• Runbook, documentation, and team training

Stratus10 is an AWS Advanced Tier Services Partner. Our forward-deployed engineers work alongside your business and technical teams from strategy through operations, keeping technical decisions connected to business outcomes.

Funding amounts and approval depend on the project and current AWS program terms.