AWS Financial Services Global

BPC

Customer Service Automation · IT Operations Automation

To support this, we began exploring the implementation of AI features to benefit both our employees and customers.” To improve the customer experience, BPC started to develop an intelligent chatbot that could respond to customer inquiries. “We use generative AI to enhance our operational efficiency,” says Eugene Tochilkin, engineering manager at BPC.

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Additionally, BPC is already using Amazon Connect , which empowers contact center agents to deliver superior customer experiences from day 1.

BPC architecture diagram 100 Using Amazon Bedrock, we can resolve matters quickly and identify the causes of disruptions.

BPC plans to roll out Amazon Q Developer to all its 200 developers in phases.

acceptance rate achieved 46%

Reported impact

Business outcome

BPC plans to roll out Amazon Q Developer to all its 200 developers in phases. To further improve productivity, the company’s engineers are working on building their own custom LLMs using Amazon SageMaker , which developers use to build, train, and deploy machine learning models for virtually any use case with fully managed infrastructure, tools, and workflows. Key reported metrics include reduction in chatbot costs 66%; lines of code generated 4000; acceptance rate achieved 46%.

Solution

Learn how BPC, a global leader in payments solutions, uses generative AI on AWS to streamline its developer and customer service workflows. Global payments solutions provider BPC has been experimenting with generative artificial intelligence (AI) for over 1 year to drive efficiencies across its internal workflows. Because it operates in a highly regulated industry, BPC must protect its customers’ data as it expands its technology road map. To maintain data sovereignty in the cloud, BPC uses Amazon Web Services (AWS) to adopt AI-powered solutions. Since beginning its AI journey, BPC has trialed AI across multiple divisions, eventually launching two generative AI solutions for its customer service agents and developers. By implementing AI-powered solutions across its workforce, BPC has improved its customer support and accelerated the release of new features and updates.

Data flow

Source data enters the BPC workflow through its application services, processed with Claude 3 Haiku, Amazon Bedrock, Claude 3, and delivered through Amazon Q Developer, Amazon Q.

Solution architecture

5 components · 2 layers

Compute
- Claude 3 Haiku Serves as the low-latency large language model for generation or analysis tasks.
- Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.
- Claude 3 Serves as the primary large language model for generation or analysis tasks.
Serving
- Amazon Q Developer Assists developers with code generation, troubleshooting, and software delivery tasks.
- Amazon Q Provides conversational AI assistance for employees or end users.

Architecture clues

Eugene Tochilkin Engineering Manager, BPC Global payments solutions provider BPC has been experimenting with generative artificial intelligence (AI) for over 1 year to drive efficiencies across its internal workflows.
Next, BPC focused on streamlining its developers’ workflows by testing Amazon Q Developer , a generative AI–powered assistant for software development.
To further improve productivity, the company’s engineers are working on building their own custom LLMs using Amazon SageMaker , which developers use to build, train, and deploy machine learning models for virtually any u

Evidence from the source

Additionally, BPC is already using Amazon Connect , which empowers contact center agents to deliver superior customer experiences from day 1.

BPC architecture diagram 100 Using Amazon Bedrock, we can resolve matters quickly and identify the causes of disruptions.

BPC plans to roll out Amazon Q Developer to all its 200 developers in phases.

Next, BPC focused on streamlining its developers’ workflows by testing Amazon Q Developer , a generative AI–powered assistant for software development.

To support this, we began exploring the implementation of AI features to benefit both our employees and customers.” To improve the customer experience, BPC started to develop an intelligent chatbot that could respond to customer inquiries.

“We recognize value in AWS technology and AI services.” Figure 1.

Transparency Evidence & methodology 95/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth20/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
5 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence87%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
6 direct source quotes retained.
Date provenance8/10
The publication date comes from publisher-controlled metadata.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

AWS
BPC customer story

First-party
https://aws.amazon.com/solutions/case-studies/bpc/

Source type
Vendor-published customer story

Published
Oct 25, 2024
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date

Date evidence

Publisher catalog date Oct 25, 2024 · AWS · First-party
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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