Enterprise RAG System with Coveo Passage Retrieval and Amazon Bedrock Agents

coveo · Tech · 2025 · source Agentic: yes · promotional bias: high · source-backed · extracted with prompt 0.5.6 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Enterprise RAG grounding and relevance

Coveo and AWS address the problem that enterprise LLM applications can produce misleading or untrusted responses unless they are grounded in permission-aware, relevant, current enterprise knowledge with citations, metadata, and performance visibility.

Constraints:

• Enterprise content spans cloud and on-prem repositories.
• RAG retrieval is the most complex component and must find precise passages, not just documents.
• The retrieval system must respect enterprise permission models.
• Generated answers need citations and metadata for trust.
• Teams need analytics to see missing, underused, or poorly performing content.

Architecture

The solution integrates Coveo's Passage Retrieval API and AI-Relevance Platform with Amazon Bedrock Agents. Bedrock interprets user queries and invokes an OpenAPI-defined action group backed by AWS Lambda; Lambda calls Coveo Passage Retrieval; Coveo's unified hybrid index performs two-stage retrieval, hybrid semantic/lexical ranking, ML relevance tuning, permission filtering, and returns ranked passages, citations, metadata, and scores for grounded LLM responses.

Diagram confidence: explicit (topology described by the source).

flowchart LR
  amazon-bedrock-agent["Amazon Bedrock Agent"] --> coveoprapi-action-group["CoveoPRAPI action group"]
  amazon-bedrock-agent --> grounded-response["Grounded response"]
  coveoprapi-action-group --> aws-lambda-bridge["AWS Lambda bridge"]
  aws-lambda-bridge --> coveo-passage-retrieval-api["Coveo Passage Retrieval API"]
  aws-lambda-bridge --> cloudwatch-logs["CloudWatch logs"]
  coveo-passage-retrieval-api --> unified-hybrid-index["Unified hybrid index"]
  unified-hybrid-index --> enterprise-content-connectors["Enterprise content connectors"]

Components

Data flow

A user asks a Bedrock Agent a question; the agent decides to call the CoveoPRAPI action group; Lambda sends the user input to Coveo Passage Retrieval; Coveo retrieves accessible documents, extracts and ranks passages with metadata/citations, returns chunks to Bedrock, and the agent summarizes a grounded answer while CloudWatch logs retrieved passages.

Why this architecture

Coveo uses Bedrock Agents plus passage retrieval because enterprise RAG needs orchestration, precise passage extraction, permission-aware hybrid relevance, citations, and analytics rather than a simple vector-search context dump.

Models used: Amazon Bedrock Agents · Claude 3 Haiku v1 · AWS Lambda · Coveo Passage Retrieval API · Coveo AI-Relevance Platform · CloudWatch · CloudFormation

Scale

• The Passage Retrieval API returns the five most relevant chunks in the demo logs.
  “returns the five most relevant chunks to the LLM.”article, stated
• The solution can connect cloud and on-premises repositories through a single index.
  “connects and unifies the content of cloud and on-premises repositories in a single index”article, stated
• Pre-built connectors include third-party services such as Salesforce, SharePoint, and Google Drive.
  “pre-built connectors to maintain seamless integration with third-party services (such as Salesforce, SharePoint, and Google Drive)”article, stated
• The sample deployment requires an AWS account, Bedrock model access, and a Coveo index.
  “Access to models in Amazon Bedrock”article, stated

Alternatives considered (roads not taken)

Use basic vector search alone.

Rejected because: Coveo combines semantic and lexical search, ML relevance, unified indexing, metadata, and permissions.

Use federated search across sources.

Rejected because: The unified hybrid index can apply ranking across sources for better multi-source relevance.

Filter permissions only after a query is run.

Rejected because: Coveo imports item-level permissions at crawl time and filters inaccessible items before query execution.

Let the LLM answer without retrieved evidence.

Rejected because: The agent is instructed to retrieve relevant passages before formulating the final response.

Patterns

rag-retrieval (retrieval)

Bedrock Agents invoke Coveo Passage Retrieval to ground answers in enterprise content.

hybrid-search (retrieval)

Coveo combines semantic vector search and lexical keyword matching over a unified index.

retrieval-ranking (retrieval)

Coveo first finds relevant documents, then extracts the most relevant passages with ranking scores.

privacy-preserving-processing (governance)

Coveo imports source permissions at crawl time and filters inaccessible content before queries run.

observability-tracing (operations)

Lambda logs retrieved passages to CloudWatch so teams can see which chunks contributed to a response.

Tradeoffs

Use a unified hybrid index.

Chose Coveo's unified hybrid index across structured, unstructured, and permission-sensitive data. over Federated search or basic vector search..

Rationale: Unified ranking and hybrid retrieval improve relevance and grounding across sources.

Expose Coveo through a Bedrock action group.

Chose OpenAPI-defined action group plus Lambda bridge. over Embedding retrieval logic directly in the model prompt..

Rationale: The agent can explicitly invoke passage retrieval when it needs enterprise documentation.

Log retrieval chunks.

Chose CloudWatch debugging logs for each retrieved passage. over Opaque retrieval hidden behind the final answer..

Rationale: Teams can inspect exactly which passages were retrieved and how they contributed.

Gotchas (what bit them)

LLM trust collapses without a reliable data foundation.

Root cause: Models can generate misleading or inaccurate responses.

Resolution: Ground responses with relevant, context-aware enterprise knowledge from Coveo retrieval.

Document-level retrieval is not precise enough for enterprise RAG.

Root cause: LLM answers need exact passages, citation links, metadata, and scores.

Resolution: Use Coveo's two-stage retrieval process to extract ranked passages from relevant documents.

Security filtering after retrieval can leak or slow results.

Root cause: Enterprise content has item-level permissions from many source systems.

Resolution: Import permissions at crawl time and filter inaccessible items before query execution.

Evaluation

The demo evaluates through Bedrock agent trace inspection, CloudWatch retrieval logs, Passage Retrieval ranking scores, citation metadata, and Coveo analytics/insights on generated-answer performance and missing or underused content.

Metrics: Ranking scores · Citation links · Retrieved chunk count · Trace rationale · Generated answer performance · Missing or underused content

Outcomes

• The demo agent retrieves Coveo documentation passages through a Lambda-backed Bedrock action group.
• CloudWatch logs show the five most relevant chunks returned to the LLM.
• The architecture reduces inaccuracies by grounding responses in permission-aware enterprise content.
• The solution is deployable through a CloudFormation stack with Bedrock and Coveo prerequisites.

The post is a solution demo rather than a customer outcome report; it emphasizes enterprise applicability across support, employee, sales, and service assistants.