Amazon Nova

Taiwan retail platform uniopen adapted Amazon Nova 2 Lite to its two-axis moderation policy using LoRA supervised fine-tuning in Amazon SageMaker AI, plus a prompt-format change. According to AWS, Per Behavior Macro F1 rose from 0.5852 to 0.8550 and Subject Type Macro F1 from 0.4162 to 0.8491, both above production targets.

Aderant built a serverless ticket triage system on Amazon Nova Lite that reviewed 109 tickets in its first 2.5 weeks with roughly 96% routing accuracy. The company estimates the system recovers 8-14 engineering hours per week at under $30 in total monthly operating cost.

Amazon Bedrock's prompt caching feature can cut input token costs by up to 90% on cache hits by storing repeated context like documents, system prompts, and tool definitions. AWS outlines six implementation patterns and pricing details, including a 25% premium for cache writes and 90% discount on cache reads.

AWS published a reference architecture showing how to deploy a WhatsApp ordering assistant on Amazon Bedrock AgentCore, using Nova 2 Lite for text and Nova 2 Sonic for voice, with shared cross-channel memory and MCP-based tool access to backend systems.

Guardoc Health built a multi-stage document processing pipeline on Amazon Nova Pro, Nova Lite, and Titan Text Embeddings to extract and classify medical conditions from clinical PDFs at scale. The company claims a 46 percent reduction in documentation errors, 70 percent fewer audit fines, and over $400K in annual ROI for a single facility.

AWS has released infrastructure for deploying multi-turn reinforcement learning to train Amazon Nova models on SageMaker HyperPod. The system requires a minimum of 10 ml.p5.48xlarge instances and costs approximately $786-$1,180 per hour when running.

AWS has released an automated pipeline for redacting personally identifiable information in images, using Amazon Nova 2 Lite as an intelligent coordinator. The solution combines Nova's contextual vision reasoning with Meta's SAM 3 model deployed on SageMaker and Amazon Textract to handle complex PII detection scenarios including faces, fingerprints, ID cards, and license plates.

AWS published a technical guide showing how Amazon Nova 2 Lite performs object detection through natural language prompts without requiring model training. The multimodal model returns bounding box coordinates in JSON format at $0.0003 per thousand input tokens and $0.0025 per thousand output tokens, with typical images costing approximately $0.00057 to process.

Amazon Web Services released a model distillation pipeline on Amazon Bedrock that transfers video search routing intelligence from Nova Premier to Nova Micro. According to AWS, the approach reduces inference cost by over 95% and latency by 50% compared to using Claude Haiku for intent routing.

Amazon Web Services published a practical guide for fine-tuning Amazon Nova models using the Nova Forge SDK's data mixing capabilities. According to AWS, blending customer data with Amazon-curated datasets preserved near-baseline MMLU scores while delivering a 12-point F1 improvement on a Voice of Customer classification task spanning 1,420 leaf categories.

AWS has introduced Lambda-based reward functions for Amazon Nova model customization through reinforcement fine-tuning (RFT). The serverless architecture automatically scales from 10 concurrent evaluations per second during experimentation to 400+ during production training, supporting both objective RLVR and subjective RLAIF approaches.

Amazon Bedrock now enables fine-tuning of Amazon Nova models using supervised fine-tuning (SFT), reinforcement fine-tuning (RFT), and model distillation. The service automates infrastructure provisioning and training orchestration, requiring only data upload to S3 and a single API call. Fine-tuned models run on-demand at standard inference pricing without provisioned capacity requirements.