Accurate merchant matching

Aura Intelligence integrated Anthropic's Claude via Amazon Bedrock into its data pipeline to automatically classify over 200 million job titles and industry pairings from multi-language data, replacing manual lookups and fuzzy matching. They fine-tuned foundation models on proprietary datasets and leveraged AWS infrastructure, including SageMaker and prompt management, to automate QA, report generation, anomaly detection, and real-time hiring trend analysis.

LaunchNotes leverages Claude in Amazon Bedrock in their product 'Graph' to transform engineering data into actionable insights. Graph functions as an ETL platform with Claude managing data pipelines, helping engineering managers understand development metrics, reduce incident identification time, automate updates, and generate customized release notes and technical documentation.

Snowflake integrated Anthropic's Claude into their platform to enable natural language queries on complex databases, allowing customers to extract insights without SQL expertise while maintaining security and governance.

704 Apps implemented an AI solution using Vertex AI and Gemini 1.5 Pro to automate and accelerate driver identity verification and safety monitoring. They integrated these AI models into their existing cloud infrastructure built on Firebase and Google Kubernetes Engine, centralizing real-time data for document validation and audio sentiment analysis. The system alerts the central monitoring team when risk-related language is detected, streamlining operational decision-making and enhancing security.

OPPO integrated Google Cloud’s Vertex AI, AutoML, and Gemini large language model into its mobile devices to automate user feedback analysis, power AI Recording Summary features, and enable AI Toolbox functionalities such as AI Writer and AI Reply. They re-engineered their hardware platform, operating system, and third-party ecosystem to embed AI agents that optimize power consumption and reduce computing latency, streamlining mobile development workflows and enhancing user experience.

Dataïads built an AI-powered “Post-Click Experience” system that automatically generates personalized landing pages by analyzing user context such as ad origin, product type, and behavior. The solution is implemented by integrating API access from Google Ads with Google Cloud managed services (Cloud Run and BigQuery) for automated scaling and controlled cost management, while planning to incorporate Vertex AI for further optimization. This implementation directly enhances ad campaign management and improves ecommerce conversion processes.

Hebbia built Matrix, a multi-agent AI platform that orchestrates OpenAI models including o3‑mini, o1, and GPT‑4o to automate complex financial and legal research tasks. The platform decomposes intricate queries into structured analytical steps and integrates modules like OCR, hallucination validation, and artifact generation to process complete documents, creating an infinite effective context window. This solution streamlines due diligence, contract review, and market research workflows, drastically reducing manual processing time.

Booking.com integrated OpenAI’s GPT models with its extensive proprietary data on properties, pricing, and availability to develop an AI Trip Planner that converts natural language queries into personalized itineraries. The team rapidly built the solution in 10 weeks by leveraging existing ML infrastructure and fine-tuning the model on both structured and unstructured data, which also powered additional tools like Smart Filters, Property Q&A, and AI Review Summaries. This innovation transformed travel discovery, search, and customer support processes.

Bito implemented AI-powered developer agents by integrating Anthropic's Claude and leveraging Claude 3.7 Sonnet for advanced reasoning into its code review and coding workflows. They utilized Anthropic’s robust API and developer-friendly infrastructure to embed an AI Code Review Agent and Bito Wingman directly within developers’ Git workflows and popular IDEs, enabling automated analysis of pull request diffs and code architecture. This integration streamlined code review, error detection, and code generation processes while upholding security standards.