What challenges did Coast Capital Savings face before AI implementation?

Employees needed quicker access to information buried in a large intranet. HR queries were time-consuming to handle. Staff needed faster self-service answers for lending and related internal topics.

What was the impact of the AI solution?

The initial chatbot was designed to reduce human resources queries by 25-30%. Employees can find answers quickly using natural language. The chatbot provides quick access to accurate, up-to-date information and links to source information. The company expects the tool to free staff for more specialized tasks.

Source

Evidence: Medium50/100

Coast Capital Savings builds staff-facing generative AI chatbot with Amazon Bedrock Knowledge Bases

Use case typeStaff assistantUpdated Jun 13, 2026

Coast Capital Savings, a member-owned Canadian credit union, built a staff-facing generative AI chatbot to help employees find answers buried in a large intranet more quickly. The solution began with lending-team questions and was later expanded to human resources content, with the goal of improving self-service information retrieval across the organization.

Organization: Coast Capital Savings
Industry: Finance
Location: Canada
Published: May 2026

Reported outcomes

25-30%

quantified impactOther quantified impact

Strategic outcomes

Speed & agilityBuilt a staff-facing AI chatbotNew product / capabilityExpanded self-service knowledge accessCustomer experience & trustProvided source-linked answers to staffScale & capacityFreed staff for specialized tasks

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Quantified impact: 25-30% decrease

AWSMay 27, 2026Customer storyInferred claimMedium evidence strength

The initial chatbot was designed to reduce human resources queries by 25-30%.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Coast Capital Savings
Provider: AWS
Maturity: Unknown
Linked source: AWS

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Internal knowledge assistant
2Employee productivity
3Enterprise search

Employees needed quicker access to information buried in a large intranet.
HR queries were time-consuming to handle.
Staff needed faster self-service answers for lending and related internal topics.

Built a staff-facing chatbot prototype in about 3 weeks using Amazon Bedrock.
Iteratively expanded the chatbot to include human resources content.
Used Amazon Bedrock Knowledge Bases to ground responses in private internal data sources and provide source-linked answers.
Cleaned and prepared intranet data to improve response quality.
Implemented the solution with a serverless architecture and intelligent enterprise search.

Technologies

Amazon Bedrock Amazon Bedrock Knowledge Bases Intelligent Enterprise Search

The initial chatbot was designed to reduce human resources queries by 25-30%.
Employees can find answers quickly using natural language.
The chatbot provides quick access to accurate, up-to-date information and links to source information.
The company expects the tool to free staff for more specialized tasks.

Architecture

A serverless generative AI architecture built on Amazon Bedrock and Amazon Bedrock Knowledge Bases, with intelligent enterprise search and cleaned private intranet data as the knowledge source for a staff-facing chatbot.

Sources & evidence1

Evidence: Medium50/100Evidence strength