Generative AI revolutionizes enterprise operations by means of varied purposes, together with conversational assistants reminiscent of Amazon’s Rufus and Amazon Vendor Assistant. Moreover, a few of the most impactful generative AI purposes function autonomously behind the scenes, an important functionality that empowers enterprises to rework their operations, knowledge processing, and content material creation at scale. These non-conversational implementations, typically within the type of agentic workflows powered by massive language fashions (LLMs), execute particular enterprise aims throughout industries with out direct consumer interplay.
Non-conversational purposes supply distinctive benefits reminiscent of increased latency tolerance, batch processing, and caching, however their autonomous nature requires stronger guardrails and exhaustive high quality assurance in comparison with conversational purposes, which profit from real-time consumer suggestions and supervision.
This submit examines 4 various Amazon.com examples of such generative AI purposes:
Every case research reveals totally different facets of implementing non-conversational generative AI purposes, from technical structure to operational issues. All through these examples, you’ll learn the way the great suite of AWS companies, together with Amazon Bedrock and Amazon SageMaker, are the important thing to success. Lastly, we listing key learnings generally shared throughout these use circumstances.
Creating high-quality product listings on Amazon.com
Creating high-quality product listings with complete particulars helps clients make knowledgeable buy choices. Historically, promoting companions manually entered dozens of attributes per product. The brand new generative AI resolution, launched in 2024, transforms this course of by proactively buying product info from model web sites and different sources to enhance the shopper expertise throughout quite a few product classes.
Generative AI simplifies the promoting associate expertise by enabling info enter in varied codecs reminiscent of URLs, product pictures, or spreadsheets and routinely translating this into the required construction and format. Over 900,000 promoting companions have used it, with almost 80% of generated itemizing drafts accepted with minimal edits. AI-generated content material supplies complete product particulars that assist with readability and accuracy, which may contribute to product discoverability in buyer searches.
For brand new listings, the workflow begins with promoting companions offering preliminary info. The system then generates complete listings utilizing a number of info sources, together with titles, descriptions, and detailed attributes. Generated listings are shared with promoting companions for approval or enhancing.
For present listings, the system identifies merchandise that may be enriched with extra knowledge.
Knowledge integration and processing for a big number of outputs
The Amazon group constructed strong connectors for inside and exterior sources with LLM-friendly APIs utilizing Amazon Bedrock and different AWS companies to seamlessly combine into Amazon.com backend programs.
A key problem is synthesizing various knowledge into cohesive listings throughout greater than 50 attributes, each textual and numerical. LLMs require particular management mechanisms and directions to precisely interpret ecommerce ideas as a result of they may not carry out optimally with such complicated, diversified knowledge. For instance, LLMs would possibly misread “capability” in a knife block as dimensions moderately than variety of slots, or mistake “Match Put on” as a method description as a substitute of a model identify. Immediate engineering and fine-tuning had been extensively used to deal with these circumstances.
Era and validation with LLMs
The generated product listings needs to be full and proper. To assist this, the answer implements a multistep workflow utilizing LLMs for each era and validation of attributes. This dual-LLM method helps stop hallucinations, which is important when coping with security hazards or technical specs. The group developed superior self-reflection methods to ensure the era and validation processes complement one another successfully.
The next determine illustrates the era course of with validation each carried out by LLMs.
Determine 1. Product Itemizing creation workflow
Multi-layer high quality assurance with human suggestions
Human suggestions is central to the answer’s high quality assurance. The method consists of Amazon.com consultants for preliminary analysis and promoting associate enter for acceptance or edits. This supplies high-quality output and allows ongoing enhancement of AI fashions.
The standard assurance course of consists of automated testing strategies combining ML-, algorithm-, or LLM-based evaluations. Failed listings bear regeneration, and profitable listings proceed to additional testing. Utilizing causal inference fashions, we establish underlying options affecting itemizing efficiency and alternatives for enrichment. In the end, listings that move high quality checks and obtain promoting associate acceptance are revealed, ensuring clients obtain correct and complete product info.
The next determine illustrates the workflow of going to manufacturing with testing, analysis, and monitoring of product itemizing era.
Determine 2. Product Itemizing testing and human within the loop workflow
Software-level system optimization for accuracy and value
Given the excessive requirements for accuracy and completeness, the group adopted a complete experimentation method with an automatic optimization system. This technique explores varied combos of LLMs, prompts, playbooks, workflows, and AI instruments to iterate for increased enterprise metrics, together with price. Via steady analysis and automatic testing, the product itemizing generator successfully balances efficiency, price, and effectivity whereas staying adaptable to new AI developments. This method means clients profit from high-quality product info, and promoting companions have entry to cutting-edge instruments for creating listings effectively.
Generative AI-powered prescription processing in Amazon Pharmacy
Constructing upon the human-AI hybrid workflows beforehand mentioned within the vendor itemizing instance, Amazon Pharmacy demonstrates how these rules may be utilized in a Well being Insurance coverage Portability and Accountability Act (HIPAA)-regulated trade. Having shared a conversational assistant for affected person care specialists within the submit Learn the way Amazon Pharmacy created their LLM-based chat-bot utilizing Amazon SageMaker, we now deal with automated prescription processing, which you’ll be able to examine in The lifetime of a prescription at Amazon Pharmacy and the next analysis paper in Nature Journal.
At Amazon Pharmacy, we developed an AI system constructed on Amazon Bedrock and SageMaker to assist pharmacy technicians course of medicine instructions extra precisely and effectively. This resolution integrates human consultants with LLMs in creation and validation roles to boost precision in medicine directions for our sufferers.
Agentic workflow design for healthcare accuracy
The prescription processing system combines human experience (knowledge entry technicians and pharmacists) with AI help for route ideas and suggestions. The workflow, proven within the following diagram, begins with a pharmacy knowledge-based preprocessor standardizing uncooked prescription textual content in Amazon DynamoDB, adopted by fine-tuned small language fashions (SLMs) on SageMaker figuring out important elements (dosage, frequency).
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Determine 3. (a) Knowledge entry technician and pharmacist workflow with two GenAI modules, (b) Suggestion module workflow and (c) Flagging module workflow |
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The system seamlessly integrates consultants reminiscent of knowledge entry technicians and pharmacists, the place generative AI enhances the general workflow in the direction of agility and accuracy to raised serve our sufferers. A route meeting system with security guardrails then generates directions for knowledge entry technicians to create their typed instructions by means of the suggestion module. The flagging module flags or corrects errors and enforces additional security measures as suggestions offered to the info entry technician. The technician finalizes extremely correct, safe-typed instructions for pharmacists who can both present suggestions or execute the instructions to the downstream service.
One spotlight from the answer is using activity decomposition, which empowers engineers and scientists to interrupt the general course of into a mess of steps with particular person modules made from substeps. The group extensively used fine-tuned SLMs. As well as, the method employs conventional ML procedures reminiscent of named entity recognition (NER) or estimation of ultimate confidence with regression fashions. Utilizing SLMs and conventional ML in such contained, well-defined procedures considerably improved processing pace whereas sustaining rigorous security requirements as a consequence of incorporation of acceptable guardrails on particular steps.
The system contains a number of well-defined substeps, with every subprocess working as a specialised part working semi-autonomously but collaboratively inside the workflow towards the general goal. This decomposed method, with particular validations at every stage, proved simpler than end-to-end options whereas enabling using fine-tuned SLMs. The group used AWS Fargate to orchestrate the workflow given its present integration into present backend programs.
Of their product growth journey, the group turned to Amazon Bedrock, which offered high-performing LLMs with ease-of-use options tailor-made to generative AI purposes. SageMaker enabled additional LLM choices, deeper customizability, and conventional ML strategies. To be taught extra about this system, see How activity decomposition and smaller LLMs could make AI extra reasonably priced and browse in regards to the Amazon Pharmacy enterprise case research.
Constructing a dependable utility with guardrails and HITL
To adjust to HIPAA requirements and supply affected person privateness, we carried out strict knowledge governance practices alongside a hybrid method that mixes fine-tuned LLMs utilizing Amazon Bedrock APIs with Retrieval Augmented Era (RAG) utilizing Amazon OpenSearch Service. This mixture allows environment friendly information retrieval whereas sustaining excessive accuracy for particular subtasks.
Managing LLM hallucinations—which is important in healthcare—required extra than simply fine-tuning on massive datasets. Our resolution implements domain-specific guardrails constructed on Amazon Bedrock Guardrails, complemented by human-in-the-loop (HITL) oversight to advertise system reliability.
The Amazon Pharmacy group continues to boost this method by means of real-time pharmacist suggestions and expanded prescription format capabilities. This balanced method of innovation, area experience, superior AI companies, and human oversight not solely improves operational effectivity, however signifies that the AI system correctly augments healthcare professionals in delivering optimum affected person care.
Generative AI-powered buyer evaluation highlights
Whereas our earlier instance showcased how Amazon Pharmacy integrates LLMs into real-time workflows for prescription processing, this subsequent use case demonstrates how related methods—SLMs, conventional ML, and considerate workflow design—may be utilized to offline batch inferencing at huge scale.
Amazon has launched AI-generated buyer evaluation highlights to course of over 200 million annual product evaluations and rankings. This function distills shared buyer opinions into concise paragraphs highlighting constructive, impartial, and adverse suggestions about merchandise and their options. Consumers can shortly grasp consensus whereas sustaining transparency by offering entry to associated buyer evaluations and conserving unique evaluations out there.
The system enhances buying choices by means of an interface the place clients can discover evaluation highlights by deciding on particular options (reminiscent of image high quality, distant performance, or ease of set up for a Hearth TV). Options are visually coded with inexperienced examine marks for constructive sentiment, orange minus indicators for adverse, and grey for impartial—which suggests buyers can shortly establish product strengths and weaknesses based mostly on verified buy evaluations. The next screenshot reveals evaluation highlights concerning noise degree for a product.
Determine 4. An instance product evaluation highlights for a product.
A recipe for cost-effective use of LLMs for offline use circumstances
The group developed an economical hybrid structure combining conventional ML strategies with specialised SLMs. This method assigns sentiment evaluation and key phrase extraction to conventional ML whereas utilizing optimized SLMs for complicated textual content era duties, bettering each accuracy and processing effectivity. The next diagram reveals ttraditional ML and LLMs working to offer the general workflow.
Determine 5. Use of conventional ML and LLMs in a workflow.
The function employs SageMaker batch remodel for asynchronous processing, considerably decreasing prices in comparison with real-time endpoints. To ship a close to zero-latency expertise, the answer caches extracted insights alongside present evaluations, decreasing wait occasions and enabling simultaneous entry by a number of clients with out extra computation. The system processes new evaluations incrementally, updating insights with out reprocessing the entire dataset. For optimum efficiency and cost-effectiveness, the function makes use of Amazon Elastic Compute Cloud (Amazon EC2) Inf2 cases for batch remodel jobs, offering as much as 40% higher price-performance to options.
By following this complete method, the group successfully managed prices whereas dealing with the huge scale of evaluations and merchandise in order that the answer remained each environment friendly and scalable.
Amazon Adverts AI-powered inventive picture and video era
Having explored principally text-centric generative AI purposes in earlier examples, we now flip to multimodal generative AI with Amazon Adverts inventive content material era for sponsored advertisements. The answer has capabilities for picture and video era, the main points of which we share on this part. In widespread, this resolution makes use of Amazon Nova inventive content material era fashions at its core.
Working backward from buyer want, a March 2023 Amazon survey revealed that just about 75% of advertisers battling marketing campaign success cited inventive content material era as their major problem. Many advertisers—significantly these with out in-house capabilities or company help—face vital obstacles as a result of experience and prices of manufacturing high quality visuals. The Amazon Adverts resolution democratizes visible content material creation, making it accessible and environment friendly for advertisers of various sizes. The impression has been substantial: advertisers utilizing AI-generated pictures in Sponsored Manufacturers campaigns noticed almost 8% click-through charges (CTR) and submitted 88% extra campaigns than non-users.
Final 12 months, the AWS Machine Studying Weblog revealed a submit detailing the picture era resolution. Since then, Amazon has adopted Amazon Nova Canvas as its basis for inventive picture era, creating professional-grade pictures from textual content or picture prompts with options for text-based enhancing and controls for shade scheme and structure changes.
In September 2024, the Amazon Adverts group included the creation of short-form video advertisements from product pictures. This function makes use of basis fashions out there on Amazon Bedrock to present clients management over visible type, pacing, digital camera movement, rotation, and zooming by means of pure language, utilizing an agentic workflow to first describe video storyboards after which generate the content material for the story. The next screenshot reveals an instance of inventive picture era for product backgrounds on Amazon Adverts.
Determine 6. Adverts picture era instance for a product.
As mentioned within the unique submit, accountable AI is on the heart of the answer, and Amazon Nova inventive fashions include built-in controls to help security and accountable AI use, together with watermarking and content material moderation.
The answer makes use of AWS Step Capabilities with AWS Lambda features to orchestrate serverless orchestration of each picture and video era processes. Generated content material is saved in Amazon Easy Storage Service (Amazon S3) with metadata in DynamoDB, and Amazon API Gateway supplies buyer entry to the era capabilities. The answer now employs Amazon Bedrock Guardrails along with sustaining Amazon Rekognition and Amazon Comprehend integration at varied steps for added security checks. The next screenshot reveals inventive AI-generated movies on Amazon Adverts marketing campaign builder.
Determine 7. Adverts video era for a product
Creating high-quality advert creatives at scale introduced complicated challenges. The generative AI mannequin wanted to supply interesting, brand-appropriate pictures throughout various product classes and promoting contexts whereas remaining accessible to advertisers no matter technical experience. High quality assurance and enchancment are basic to each picture and video era capabilities. The system undergoes continuous enhancement by means of intensive HITL processes enabled by Amazon SageMaker Floor Reality. This implementation delivers a strong device that transforms advertisers’ inventive course of, making high-quality visible content material creation extra accessible throughout various product classes and contexts.
That is only the start of Amazon Adverts utilizing generative AI to empower promoting clients to create the content material they should drive their promoting aims. The answer demonstrates how decreasing inventive obstacles immediately will increase promoting exercise whereas sustaining excessive requirements for accountable AI use.
Key technical learnings and discussions
Non-conversational purposes profit from increased latency tolerance, enabling batch processing and caching, however require strong validation mechanisms and stronger guardrails as a consequence of their autonomous nature. These insights apply to each non-conversational and conversational AI implementations:
- Job decomposition and agentic workflows – Breaking complicated issues into smaller elements has confirmed worthwhile throughout implementations. This deliberate decomposition by area consultants allows specialised fashions for particular subtasks, as demonstrated in Amazon Pharmacy prescription processing, the place fine-tuned SLMs deal with discrete duties reminiscent of dosage identification. This technique permits for specialised brokers with clear validation steps, bettering reliability and simplifying upkeep. The Amazon vendor itemizing use case exemplifies this by means of its multistep workflow with separate era and validation processes. Moreover, the evaluation highlights use case showcased cost-effective and managed use of LLMs through the use of conventional ML for preprocessing and performing components that may very well be related to an LLM activity.
- Hybrid architectures and mannequin choice – Combining conventional ML with LLMs supplies higher management and cost-effectiveness than pure LLM approaches. Conventional ML excels at well-defined duties, as proven within the evaluation highlights system for sentiment evaluation and knowledge extraction. Amazon groups have strategically deployed each massive and small language fashions based mostly on necessities, integrating RAG with fine-tuning for efficient domain-specific purposes just like the Amazon Pharmacy implementation.
- Value optimization methods – Amazon groups achieved effectivity by means of batch processing, caching mechanisms for high-volume operations, specialised occasion varieties reminiscent of AWS Inferentia and AWS Trainium, and optimized mannequin choice. Overview highlights demonstrates how incremental processing reduces computational wants, and Amazon Adverts used Amazon Nova basis fashions (FMs) to cost-effectively create inventive content material.
- High quality assurance and management mechanisms – High quality management depends on domain-specific guardrails by means of Amazon Bedrock Guardrails and multilayered validation combining automated testing with human analysis. Twin-LLM approaches for era and validation assist stop hallucinations in Amazon vendor listings, and self-reflection methods enhance accuracy. Amazon Nova inventive FMs present inherent accountable AI controls, complemented by continuous A/B testing and efficiency measurement.
- HITL implementation – The HITL method spans a number of layers, from skilled analysis by pharmacists to end-user suggestions from promoting companions. Amazon groups established structured enchancment workflows, balancing automation and human oversight based mostly on particular area necessities and danger profiles.
- Accountable AI and compliance – Accountable AI practices embody content material ingestion guardrails for regulated environments and adherence to laws reminiscent of HIPAA. Amazon groups built-in content material moderation for user-facing purposes, maintained transparency in evaluation highlights by offering entry to supply info, and carried out knowledge governance with monitoring to advertise high quality and compliance.
These patterns allow scalable, dependable, and cost-effective generative AI options whereas sustaining high quality and duty requirements. The implementations show that efficient options require not simply refined fashions, however cautious consideration to structure, operations, and governance, supported by AWS companies and established practices.
Subsequent steps
The examples from Amazon.com shared on this submit illustrate how generative AI can create worth past conventional conversational assistants. We invite you to comply with these examples or create your personal resolution to find how generative AI can reinvent your corporation and even your trade. You may go to the AWS generative AI use circumstances web page to start out the ideation course of.
These examples confirmed that efficient generative AI implementations typically profit from combining several types of fashions and workflows. To be taught what FMs are supported by AWS companies, consult with Supported basis fashions in Amazon Bedrock and Amazon SageMaker JumpStart Basis Fashions. We additionally counsel you discover Amazon Bedrock Flows, which may ease the trail in the direction of constructing workflows. Moreover, we remind you that Trainium and Inferentia accelerators present necessary price financial savings in these purposes.
Agentic workflows, as illustrated in our examples, have confirmed significantly worthwhile. We advocate exploring Amazon Bedrock Brokers for shortly constructing agentic workflows.
Profitable generative AI implementation extends past mannequin choice—it represents a complete software program growth course of from experimentation to utility monitoring. To start constructing your basis throughout these important companies, we invite you to discover Amazon QuickStart.
Conclusion
These examples show how generative AI extends past conversational assistants to drive innovation and effectivity throughout industries. Success comes from combining AWS companies with sturdy engineering practices and enterprise understanding. In the end, efficient generative AI options deal with fixing actual enterprise issues whereas sustaining excessive requirements of high quality and duty.
To be taught extra about how Amazon makes use of AI, consult with Synthetic Intelligence in Amazon Information.
In regards to the Authors
Burak Gozluklu is a Principal AI/ML Specialist Options Architect and lead GenAI Scientist Architect for Amazon.com on AWS, based mostly in Boston, MA. He helps strategic clients undertake AWS applied sciences and particularly Generative AI options to attain their enterprise aims. Burak has a PhD in Aerospace Engineering from METU, an MS in Methods Engineering, and a post-doc in system dynamics from MIT in Cambridge, MA. He maintains his connection to academia as a analysis affiliate at MIT. Outdoors of labor, Burak is an fanatic of yoga.
Emilio Maldonado is a Senior chief at Amazon answerable for Product Information, oriented at constructing programs to scale the e-commerce Catalog metadata, set up all product attributes, and leverage GenAI to deduce exact info that guides Sellers and Consumers to work together with merchandise. He’s obsessed with growing dynamic groups and forming partnerships. He holds a Bachelor of Science in C.S. from Tecnologico de Monterrey (ITESM) and an MBA from Wharton, College of Pennsylvania.
Wenchao Tong is a Sr. Principal Technologist at Amazon Adverts in Palo Alto, CA, the place he spearheads the event of GenAI purposes for inventive constructing and efficiency optimization. His work empowers clients to boost product and model consciousness and drive gross sales by leveraging progressive AI applied sciences to enhance inventive efficiency and high quality. Wenchao holds a Grasp’s diploma in Pc Science from Tongji College. Outdoors of labor, he enjoys mountaineering, board video games, and spending time together with his household.
Alexandre Alves is a Sr. Principal Engineer at Amazon Well being Companies, specializing in ML, optimization, and distributed programs. He helps ship wellness-forward well being experiences.
Puneet Sahni is Sr. Principal Engineer in Amazon. He works on bettering the info high quality of all merchandise out there in Amazon catalog. He’s obsessed with leveraging product knowledge to enhance our buyer experiences. He has a Grasp’s diploma in Electrical engineering from Indian Institute of Know-how (IIT) Bombay. Outdoors of labor he having fun with spending time together with his younger youngsters and travelling.
Vaughn Schermerhorn is a Director at Amazon, the place he leads Purchasing Discovery and Analysis—spanning Buyer Opinions, content material moderation, and web site navigation throughout Amazon’s world marketplaces. He manages a multidisciplinary group of utilized scientists, engineers, and product leaders centered on surfacing reliable buyer insights by means of scalable ML fashions, multimodal info retrieval, and real-time system structure. His group develops and operates large-scale distributed programs that energy billions of buying choices each day. Vaughn holds levels from Georgetown College and San Diego State College and has lived and labored within the U.S., Germany, and Argentina. Outdoors of labor, he enjoys studying, journey, and time together with his household.
Tarik Arici is a Principal Utilized Scientist at Amazon Choice and Catalog Methods (ASCS), engaged on Catalog High quality Enhancement utilizing GenAI workflows. He has a PhD in Electrical and Pc Engineering from Georgia Tech. Outdoors of labor, Tarik enjoys swimming and biking.
