PerformLine是一家营销合规服务提供商，为应对复杂网页内容合规审查的挑战，其工程团队与AWS合作，利用Amazon Bedrock构建了一个可扩展、服务器化的事件驱动架构。该方案通过AI驱动的内容提取与分析，能够准确理解包含多产品的网页内容，并将其转化为结构化数据，输入到PerformLine的规则引擎中进行合规检查。此举不仅提高了处理效率和准确性，还通过多层级模型推理和提示管理实现了成本优化，显著减轻了人工审查负担，为客户提供更高效、更具成本效益的合规解决方案。

💡 **AI驱动的网页内容合规分析**：PerformLine利用Amazon Bedrock的生成式AI能力，构建了一个能够理解复杂网页内容（特别是包含多产品信息）的系统。通过AI推理，该系统能准确提取和分析网页中的文本和视觉元素，模拟用户浏览习惯，为合规审查提供上下文感知的数据。这使得PerformLine能够处理传统静态解析难以应对的动态和复杂网页。

🚀 **可扩展的事件驱动架构**：PerformLine采用基于AWS Lambda、Amazon SQS、Amazon S3和Amazon EventBridge的事件驱动无服务器架构，实现了高度的可扩展性和灵活性。该架构能动态处理不可预测的工作负载，并与现有系统无缝集成。通过使用Amazon Bedrock的提示管理功能，PerformLine能够高效地版本化、测试和部署AI模型，确保模型的一致性和可控性。

💰 **多层级模型推理与成本优化**：为平衡AI推理的成本与准确性，PerformLine采用了多层级模型推理策略。首先使用轻量级模型进行初步筛选，再用更强大的模型进行深度分析，同时通过精心设计的提示和上下文处理，最大限度地减少了token消耗。这种方法不仅提高了提取精度，还显著降低了AI推理成本，例如通过避免重复处理和优化模型选择。

📊 **显著提升效率与降低成本**：该解决方案通过自动化内容处理和AI分析，使PerformLine能够处理海量网页数据，并减少了约15%的人工审查工作量，使人工评估人员能专注于关键页面。此外，通过避免重复处理未更改的页面，分析师的工作量减少了超过50%。这些优化显著提升了整体运营效率和成本效益。

🌟 **未来展望与功能增强**：PerformLine计划进一步利用Amazon Bedrock的Prompt Caching和Amazon Bedrock Flows功能。Prompt Caching有望带来高达85%的延迟改进和90%的成本降低。而Bedrock Flows则能通过可视化工作流构建器，简化多步骤生成式AI任务的编排，为PerformLine带来更多功能扩展和开发机会。

This post is co-written with Bogdan Arsenie and Nick Mattei from PerformLine.

PerformLine operates within the marketing compliance industry, a specialized subset of the broader compliance software market, which includes various compliance solutions like anti-money laundering (AML), know your customer (KYC), and others. Specifically, marketing compliance refers to adhering to regulations and guidelines set by government agencies that make sure a company’s marketing, advertising, and sales content and communications are truthful, accurate, and not misleading for consumers. PerformLine is the leading service providing comprehensive compliance oversight across marketing, sales, and partner channels. As pioneers of the marketing compliance industry, PerformLine has conducted over 1.1 billion compliance observations over the past 10+ years, automating the entire compliance process—from pre-publication review of materials to continuous monitoring of consumer-facing channels such as websites, emails, and social media. Trusted by consumer finance brands and global organizations, PerformLine uses AI-driven solutions to protect brands and their consumers, transforming compliance efforts into a competitive advantage.

“Discover. Monitor. Act. This isn’t just our tagline—it’s the foundation of our innovation at PerformLine,” says PerformLine’s CTO Bogdan Arsenie. PerformLine’s engineering team brings these principles to life by developing AI-powered technology solutions. In this post, PerformLine and AWS explore how PerformLine used Amazon Bedrock to accelerate compliance processes, generate actionable insights, and provide contextual data—delivering the speed and accuracy essential for large-scale oversight.

The problem

One of PerformLine’s enterprise customers needed a more efficient process for running compliance checks on newly launched product pages, particularly those that integrate multiple products within the same visual and textual framework. These complex pages often feature overlapping content that can apply to one product, several products, or even all of them at once, necessitating a context-aware interpretation that mirrors how a typical consumer would view and interact with the content. By adopting AWS and the architecture discussed in this post, PerformLine can retrieve and analyze these intricate pages through AI-driven processing, generating detailed insights and contextual data that capture the nuanced interplay between various product elements. After the relevant information is extracted and structured, it’s fed directly into their rules engine, enabling robust compliance checks. This accomplishes a seamless flow, from data ingestion to rules-based analysis. It not only preserves the depth of each product’s presentation but also delivers the speed and accuracy critical to large-scale oversight. Monitoring millions of webpages daily for compliance demands a system that can intelligently parse, extract, and analyze content at scale—much like the approach PerformLine has developed for their enterprise customers. In this dynamic landscape, the ever-evolving nature of web content challenges traditional static parsing, requiring a context-aware and adaptive solution. This architecture not only processes bulk data offline but also delivers near real-time performance for one-time requests, dynamically scaling to manage the diverse complexity of each page. By using AI-powered inference, PerformLine provides comprehensive coverage of every product and marketing element across the web, while striking a careful balance between accuracy, performance, and cost.

Solution overview

With this flexible, adaptable solution, PerformLine can tackle even the most challenging webpages, providing comprehensive coverage when extracting and analyzing web content with multiple products. At the same time, by combining consistency with the adaptability of foundation models (FMs), PerformLine can maintain reliable performance across the diverse range of products and websites their customers monitor. This dual focus on agility and operational consistency makes sure their customers benefit from robust compliance checks and data integrity, without sacrificing the speed or scale needed to remain competitive.

PerformLine’s upstream ingestion pipeline efficiently collects millions of web pages and their associated metadata in a batch process. Downstream assets are submitted to PerformLine’s rules engine and compliance review processes. It was imperative that they not disrupt those processes or introduce cascading changes for this solution.

PerformLine decided to use generative AI and Amazon Bedrock to address their core challenges. Amazon Bedrock allows for a broad selection of models, including Amazon Nova. Amazon Bedrock is continuously expanding feature sets around using FMs at scale. This provides a reliable foundation to build a highly available and efficient content processing system.

PerformLine’s solution incorporates the following key components:

AI inference with Amazon Bedrock – Provides seamless access to FMs for content extraction and analysis Application inference profiles – Enables precise tracking and optimization of inference costs Event-driven serverless processing pipeline –Provides a lightweight, scalable approach to handling dynamic workloads using Amazon EventBridge, Amazon Simple Queue Service (Amazon SQS), AWS Lambda, Amazon Simple Storage Service (Amazon S3), and Amazon DynamoDB. Prompt management in Bedrock – Supports versioning, testing, and deployment of prompts for improved AI consistency and control Task orchestration – Uses Amazon SQS to manage work queues efficiently, facilitating smooth and scalable task execution

PerformLine implemented a scalable, serverless event-driven architecture (shown in the following diagram) that seamlessly integrates with their existing system, requiring less than a day to develop and deploy. This made it possible to focus on prompt optimization, evaluation, and cost management rather than infrastructure overhead. This architecture allows PerformLine to dynamically parse, extract, and analyze web content with high reliability, flexibility, and cost-efficiency.

PerformLine’s process consists of several steps, including processing (Step 1), event trigger and storage (Steps 2–6), structured output and storage (Step 7), and downstream processing and compliance checks (Steps 8–9):

Millions of pages are processed by an upstream extract, transform, and load (ETL) process from PerformLine’s core systems running on the AWS Cloud. When a page is retrieved, it triggers an event in the compliance check system. Amazon S3 allows for storage of the data from a page according to metadata. EventBridge uses event-driven processing to route Amazon S3 events to Amazon SQS. Amazon SQS queues messages for processing and enables messages to be retried on failure. A Lambda Function consumes SQS messages and also scales dynamically to handle even unpredictable workloads:
This function uses Amazon Bedrock to perform extraction and generative AI analysis of the content from Amazon SQS. Amazon Bedrock offers the greatest flexibility to choose the right model for the job. For PerformLine’s use case, Amazon’s Nova Pro was best suited for complex requests that require a powerful model but still allows for a high performance to cost ratio. Anthropic’s Claude Haiku model allows for optimized quick calls, where a fast response is paramount for additional processing if needed. Amazon Bedrock features, including Amazon Bedrock Prompt Management and inference profiles are used to increase input code variability without affecting output and reduce complexity in usage of FMs through Amazon Bedrock. The function stores customer-defined product schemas in Amazon DynamoDB, enabling dynamic large language model (LLM) targeting and schema-driven output generation.
Amazon S3 stores the extracted data, which is formatted as structured JSON adhering to the target schema. EventBridge forwards Amazon S3 events to Amazon SQS, making extracted data available for downstream processing. Compliance checks and business rules, running on other PerformLine’s systems, are applied to validate and enforce regulatory requirements.

Cost optimizations

The solution offers several cost optimizations, including change data capture (CDC) on the web and strategic multi-pass inference. After a page’s content has been analyzed and formatted, it’s written back to a partition that includes a metadata hash of the asset. This enables upstream processes to determine whether a page has already been processed and if its content has changed. The key benefits of this approach include:

Alleviating redundant processing of the same pages, contributing to PerformLine experiencing a 15% workload reduction in human evaluation tasks. This frees time for human evaluators and allows them focus on critical pages rather than all the pages. Avoiding reprocessing unchanged pages, dynamically reducing PerformLine’s analysts’ workload by over 50% in addition to deduplication gains.

LLM inference costs can escalate at scale, but context and carefully structured prompts are critical for accuracy. To optimize costs while maintaining precision, PerformLine implemented a multi-pass approach using Amazon Bedrock:

Initial filtering with Amazon Nova Micro – This lightweight model efficiently identifies relevant products with minimal cost. Targeted extraction with Amazon Nova Lite – Identified products are batched into smaller groups and passed to Amazon Nova Lite for deeper analysis. This keeps PerformLine within token limits while improving extraction accuracy. Increased accuracy through context-aware processing – By first identifying the target content and then processing it in smaller batches, PerformLine significantly improved accuracy while minimizing token consumption.

Use of Amazon Bedrock

During initial testing, PerformLine quickly realized the need for a more scalable approach to prompt management. Manually tracking multiple prompt versions and templates became inefficient as PerformLine iterated and collaborated.

Amazon Bedrock’s Prompt Management service provided a centralized solution, enabling them to version, manage, and seamlessly deploy prompts to production. After the prompts are deployed, they can be dynamically referenced in AWS Lambda, allowing for flexible configuration. Additionally, by using Amazon Bedrock application profile inference endpoints, PerformLine can dynamically adjust the models the Lambda function invokes, track cost per invocation, and attribute costs to specific application instances through setting up cost tags.

To streamline model interactions, PerformLine chose the Amazon Bedrock Converse API which provides a developer-friendly, standardized interface for model invocation. When combined with inference endpoints and prompt management, a Lambda function using the Amazon Bedrock Converse API becomes highly configurable—PerformLine developers can rapidly test new models and prompts, evaluate results, and iterate without needing to rebuild or redeploy. The simplification of prompt management and ability to deploy various models through Amazon Bedrock is shown in the following diagram.

Future plans and enhancements

PerformLine is excited to dive into additional Amazon Bedrock features, including prompt caching and Amazon Bedrock Flows.

With prompt caching, users can checkpoint prompt tokens, effectively caching context for reuse in subsequent API calls. Prompt caching on Amazon Bedrock offers up to 85% latency improvements and 90% cost reduction in comparison to calls without prompt caching. PerformLine sees prompt caching as a feature that will become the standard moving forward. They have a number of use cases for their data, and having the ability to apply further analysis on the same content at a lower cost creates new opportunities for feature expansion and development.

Amazon Bedrock Flows is a visual workflow builder that enables users to orchestrate multi-step generative AI tasks by connecting FMs and APIs without extensive coding. Amazon Bedrock Flows is a next step in simplifying PerformLine’s orchestration of knowledge bases, prompt caching, and even Amazon Bedrock agents in the future. Creating flows can help reduce time to feature deployment and maintenance.

Summary

PerformLine has implemented a highly scalable, serverless, AI-driven architecture that enhances efficiency, cost-effectiveness, and compliance in the web content processing pipeline. By using Amazon Bedrock, EventBridge, Amazon SQS, Lambda, and DynamoDB, they have built a solution that can dynamically scale, optimize AI inference costs, and reduce redundant processing—all while maintaining operational flexibility and compliance integrity. Based on their current volume and workflow, PerformLine is projected to process between 1.5 to 2 million pages daily, from which they expect to extract approximately 400,000 to 500,000 products. Additionally, PerformLine anticipates applying rules to each asset, resulting in about 500,000 rule observations that will require review each day.Throughout the design process PerformLine made sure their solution remains as simple as possible while still delivering operational flexibility and integrity. This approach minimizes complexity, enhances maintainability, and accelerates deployment, empowering them to adapt quickly to evolving business needs without unnecessary overhead.

By using a serverless AI-driven architecture built on Amazon Bedrock, PerformLine helps their customers tackle even the most complex, multi-product webpages with unparalleled accuracy and efficiency. This holistic approach interprets visual and textual elements as a typical consumer would, verifying that every product variant is accurately assessed for compliance. The resulting insights are then fed directly into a rules engine, enabling rapid, data-driven decisions. For PerformLine’s customers, this means less redundant processing, lower operational costs, and a dramatically simplified compliance workflow, all without compromising on speed or accuracy. By reducing the overhead of large-scale data analysis and streamlining compliance checks, PerformLine’s solution ultimately frees teams to focus on driving innovation and delivering value.

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About the authors

Bogdan Arsenie is the Chief Technology Officer at PerformLine, with over two decades of experience leading technological innovation across digital advertising, big data, mobile gaming, and social engagement. Bogdan began programming at age 13, customizing bulletin board software to fund his passion for Star Trek memorabilia. He served as PerformLine’s founding CTO from 2007–2009, pioneering their initial compliance platform. Later, as CTO at the Rumie Initiative, he helped scale a global education initiative recognized by Google’s Impact Challenge.

Nick Mattei is a Senior Software Engineer at PerformLine. He is focused on solutions architecture and distributed application development in AWS. Outside of work, Nick is an avid cyclist and skier, always looking for the next great climb or powder day.

Shervin Suresh is a Generative AI Solutions Architect at AWS. He supports generative AI adoption both internally at AWS and externally with fast-growing startup customers. He is passionate about using technology to help improve the lives of people in all aspects. Outside of work, Shervin loves to cook, build LEGO, and collaborate with people on things they are passionate about.

Medha Aiyah is a Solutions Architect at AWS. She graduated from the University of Texas at Dallas with an MS in Computer Science, with a focus on AI/ML. She supports ISV customers in a wide variety of industries, by empowering customers to use AWS optimally to achieve their business goals. She is especially interested in guiding customers on ways to implement AI/ML solutions and use generative AI. Outside of work, Medha enjoys hiking, traveling, and dancing.

Michael Zhang is a generalist Solutions Architect at AWS working with small to medium businesses. He has been with Amazon for over 3 years and uses his background in computer science and machine learning to support customers on AWS. In his free time, Michael loves to hike and explore other cultures.