AI Knowledge Assistant
Answers grounded in your handbooks, policies, and documentation — with citations.
- Project range
- $5,000–15,000
- AWS running cost
- $50–400/mo
- Time to deploy
- 3–5 weeks
- Best-fit industries
- Professional services, Manufacturing
Executive summary
A retrieval-augmented (RAG) assistant that answers questions using only the company's own documents and cites its sources. It becomes an always-available expert for the employee handbook, policies, engineering docs, HR, and IT — so staff self-serve instead of interrupting colleagues.
Business problem
Institutional knowledge is scattered across PDFs, wikis, and people's heads. Employees waste time searching or asking around, answers are inconsistent, and onboarding is slow. Generic chatbots hallucinate because they aren't grounded in the company's real documents.
Architecture
AWS services
Amazon API Gateway
Networking- — Upload + query endpoints
- — Rate limiting
AWS Lambda
Compute- — Document ingestion (extract, chunk)
- — Retrieval + answer orchestration
Amazon Bedrock
AI / ML- — Text embeddings
- — Grounded answer generation
Bedrock Knowledge Bases
AI / ML- — Managed RAG option
- — Vector search over synced documents
Amazon S3
Storage- — Source document store
- — Knowledge Base data source
Amazon DynamoDB
Database- — Self-managed vector store
- — Document + chunk metadata
Amazon CloudWatch
Observability- — Query logs, metrics, guard alarms
Data flow
- 1
An admin uploads documents; Lambda extracts text (PDF/DOCX/MD), chunks it, and creates embeddings via Bedrock.
- 2
Embeddings and chunk text are stored in the vector store (DynamoDB for smaller sets, or a Bedrock Knowledge Base for larger ones).
- 3
An employee asks a question; Lambda embeds it and retrieves the most relevant chunks.
- 4
Bedrock composes an answer grounded strictly in the retrieved passages, returning citations.
- 5
If nothing relevant is found, the assistant says so rather than guessing.
Security considerations
- Documents and vectors encrypted at rest; access scoped per team/business.
- Answers are grounded and cited, minimizing hallucination and liability.
- Optional per-document access controls (metadata filtering) for sensitive content.
- No company data is used to train public models.
Cost considerations
- Self-managed embeddings + a DynamoDB vector store keep idle cost near $0 and avoid an OpenSearch Serverless minimum (~$350/mo).
- A managed Bedrock Knowledge Base is simpler but adds a standing vector-store cost.
- Embedding + generation via Bedrock are the main usage costs.
Scalability
- Ingestion scales with document volume; retrieval scales with the vector store.
- Swap DynamoDB retrieval for OpenSearch/Aurora pgvector or a managed KB as the corpus grows.
- Multi-tenant via per-business partitioning and metadata filters.
Deployment roadmap
Phase 1 — Corpus & access
Week 1- — Identify document sources and access rules
- — Stand up storage + ingestion
Phase 2 — Ingest & tune retrieval
Weeks 2–3- — Chunking strategy, embeddings, retrieval quality
- — Evaluate answer accuracy
Phase 3 — Roll out
Weeks 4–5- — Add chat surfaces (web, Slack, Teams)
- — Monitor and expand coverage
Future enhancements
- Slack/Teams bot surfaces for in-flow answers.
- Automatic re-sync when source docs change.
- Role-aware answers (HR vs engineering vs sales).
- Feedback loop to flag and fix weak answers.