LLM-Ready Knowledge Base

What it includes

We turn the documents, tickets, code, conversations, and structured records your organization already produces into a retrieval substrate AI systems can use safely: governed source inventory, per-source contracts, content-type-aware chunking, embeddings with refresh policies, hybrid retrieval with reranking, permission propagation, citations, and an eval set built from real questions. Hosted end-to-end on AWS — S3 for source-of-truth storage, Bedrock for embeddings and reasoning models, OpenSearch Serverless for hybrid retrieval, IAM and Lake Formation for access boundaries.

• Source inventory with owners and access boundaries
• Chunking and embeddings tuned per content type
• Hybrid retrieval (OpenSearch + Bedrock embeddings) with rerank
• Citations, permissions, freshness, and eval set

Sources we typically ingest

Wikis and documents (SharePoint, internal docs in S3), support tickets and knowledge bases, code repositories, structured records from analytical tables and operational stores, approved conversation transcripts, and product documentation. Each source gets a contract — owner, freshness target, classification, retention — and lands in S3 with lineage emitted to AWS Glue Data Catalog. Permissions flow from Lake Formation and IAM into retrieval, so an agent answering on behalf of a user cannot return content that user is not allowed to see.

How we build it on AWS

S3 as the source of truth for raw and processed content with versioning and lifecycle policies. AWS Glue or Lambda for ingestion, chunking, and PII redaction. Amazon Bedrock Titan or Cohere embeddings for vectors, written into OpenSearch Serverless vector indexes alongside a BM25 keyword leg for hybrid retrieval. Bedrock Knowledge Bases as a managed option when it fits; a custom pipeline on the same primitives when it does not. Bedrock Guardrails for safety classification on outputs. CloudWatch and OpenTelemetry for traces. Lake Formation and IAM for the access boundary; KMS for encryption at rest; PrivateLink for keeping inference traffic inside the VPC.

• S3 source-of-truth with versioning and lifecycle
• Bedrock Titan or Cohere embeddings via Bedrock
• OpenSearch Serverless for vector + BM25 hybrid retrieval
• Lake Formation + IAM permissions resolved at query time

Why a built knowledge base beats a doc dump

Pointing an LLM at a document dump produces an agent that sounds confident and cites the wrong paragraph. A built knowledge base treats retrieval as a measured system: chunks are sized for the content type, embeddings refresh on a schedule, permissions are enforced at query time, citations are verified, and quality is tracked against an eval set drawn from real questions. The agent stops guessing because the substrate stops being a guess.

Closed loop with conversation intelligence

Resolved support threads, Slack answers, and successful agent conversations feed back into the knowledge base through a reviewed pipeline — draft entries proposed from real interactions, approved by the source owner, versioned with attribution, and picked up by retrieval at the next refresh. The knowledge base does not just store what was written; it grows with what was answered.

Common starting points

Support-grounded knowledge base for a specific product area where ticket resolution and documentation will share one substrate; internal-employee knowledge across HR, IT, and policy sources where permission boundaries matter most; or a customer-facing knowledge base wired to a documentation site and chat surface, with eval coverage on the top 200 real questions before launch.