An AI knowledge base is a retrieval-augmented assistant that answers questions from your own content, with citations, instead of from whatever the model remembers from the internet. It reads documentation, wikis, tickets, PDFs, Slack threads, Confluence spaces, and SharePoint libraries, finds the passages that matter, and composes an answer a person can verify in one click. The business outcome is fewer repeated questions to senior people, faster onboarding, and support and engineering teams that resolve issues from the first answer instead of the fifth search.
Most knowledge base chatbot pilots stall between the demo and the rollout. The demo answers curated questions over a clean folder; the rollout has to handle stale pages, duplicated content, documents half the company cannot see, and users who phrase things nothing like the docs do. Production-grade means permission-aware retrieval, ingestion that keeps up with changes, evals that catch regressions, and answers that always say where they came from.
What We Build
Ingestion and Chunking Pipelines
Connectors for Confluence, SharePoint, Google Drive, Slack, Jira and Zendesk tickets, Git repositories, and PDFs, with document-aware chunking that keeps tables, code blocks, and headings intact. Every chunk carries its source, its access control list, and a last-modified timestamp, so freshness and permissions are properties of the index, not afterthoughts.
Hybrid Retrieval and Reranking
Vector similarity alone misses exact product names, error codes, and ticket IDs; keyword search alone misses paraphrases. We run both on OpenSearch or Elasticsearch, fuse the results, and rerank with a cross-encoder so the model sees the few passages that actually answer the question. This is where most assistants win or lose.
Permission-Aware Answers
Access control is enforced at query time. The user's group memberships become a filter on the retrieval query, so a document they cannot open in SharePoint cannot show up in an answer, and a permission change applies on the next question, not the next reindex. Answers cite their source passages, and the assistant says so when the sources do not cover the question.
Evals, Freshness, and Observability
A golden set of questions with expected sources and answers runs on every change to chunking, prompts, models, or the index. Incremental sync keeps the index current with edits and deletions at the source. Traces of every question, retrieved passages, and answer show where retrieval fails, which drives the next improvement.
How It Runs on Your Stack
The assistant runs in your cloud or on-prem, and your content never leaves your environment. On AWS the reference build is Amazon Bedrock for embeddings and generation, Amazon OpenSearch Service for hybrid retrieval with vector and keyword indexes, and Lambda or ECS for the ingestion and query services, delivered as an AWS Advanced Tier Services Partner with the AI Services Competency. Elsewhere we build on self-managed OpenSearch or Elasticsearch with the model provider of your choice. The full component breakdown is in our end-to-end RAG pipeline guide; for the concepts, start with what RAG is.
An assistant and a search page are different products. A search page ranks documents and leaves the reading to the user; an assistant reads them and returns an answer with its sources. Both can share one index and retrieval layer. If ranking and discovery over your content is the goal, see our semantic search solutions.
Proof
Boomi engaged us to accelerate the development of Boomi Knowledge Hub. Their team credits the engagement with resolving key product and technical uncertainties and establishing a scalable technical foundation they could build on with confidence. For ScreenSteps, a knowledge base platform, we built a hybrid vector and keyword retrieval framework on Elastic Cloud, Bedrock, Nova, and Cohere that dramatically improved search precision, the same retrieval layer an assistant depends on.
From Idea to Production in Weeks
Knowledge assistants run through the AI Launchpad Launch Sequence. Use-Case Fit picks the content sources and user group with the clearest payoff, audits the content for permissions and freshness, and defines the first golden question set. Build on Your Data stands up connectors, chunking, hybrid retrieval, and the answer service against your real documents, in your cloud or on-prem. Harden for Production adds evals, guardrails, cost and latency tuning, and observability so it survives real traffic. Launch and Run rolls it out, hands over, and keeps improving retrieval as questions come in. Related builds: an assistant that handles customer tickets is AI customer support automation, and extracting structured data from the documents themselves is intelligent document processing.
Frequently Asked Questions
What is the difference between an AI knowledge base and a chatbot?
A generic chatbot answers from the model's training data and will confidently make things up about your product. An AI knowledge base retrieves passages from your own documents first and answers only from those, with citations. If the documents do not cover the question, it says so instead of guessing.
How do you stop the assistant from leaking documents users should not see?
Each indexed chunk stores the access control list of its source document, and every retrieval query is filtered by the asking user's identity and groups. Restricted content is excluded before the model ever sees it, so there is nothing for a prompt to leak. Permission changes at the source apply on the next query.
Can it run on-prem or does our data go to a third party?
It runs where your data lives. On AWS, Bedrock and OpenSearch keep everything inside your account. On-prem or in another cloud, we use self-managed OpenSearch or Elasticsearch with a model endpoint you control. Documents, embeddings, and query logs stay in your environment.