Clazar
Clazar is the leading Cloud Sales Acceleration Platform, built to help cloud GTM teams scale revenue across the AWS, Microsoft Azure, and Google Cloud marketplaces. Clazar streamlines the entire cloud marketplace sales journey, from listing and offer management to co-selling, metering, and revenue reconciliation, all from one unified platform with zero operational overhead.
With seamless integrations into Salesforce and HubSpot, Clazar enables sales, partnerships, RevOps, and finance teams to run marketplace and co-sell motions directly from their CRM. Companies can launch listings faster, create private offers in minutes, manage contracts end-to-end, and gain real-time visibility into pipeline, billing, and cash flow through powerful analytics dashboards.
With robust governance controls, enterprise-grade security compliance, and an embedded automation builder, Clazar is trusted by 300+ high-growth leaders like Pinecone, Perplexity, Confluent, Supabase, and Secureframe.
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Vertex AI
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case.
Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection.
Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex.
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txtai
txtai is an all-in-one open source embeddings database designed for semantic search, large language model orchestration, and language model workflows. It unifies vector indexes (both sparse and dense), graph networks, and relational databases, providing a robust foundation for vector search and serving as a powerful knowledge source for LLM applications. With txtai, users can build autonomous agents, implement retrieval augmented generation processes, and develop multi-modal workflows. Key features include vector search with SQL support, object storage integration, topic modeling, graph analysis, and multimodal indexing capabilities. It supports the creation of embeddings for various data types, including text, documents, audio, images, and video. Additionally, txtai offers pipelines powered by language models that handle tasks such as LLM prompting, question-answering, labeling, transcription, translation, and summarization.
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