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Partnerships
Optimizing AI workflows for enhanced precision, performance, and cost efficiency.
Our July 10 live stream is available on YouTube.
In the fast-paced world of AI and data science, efficiency, flexibility, and cost-effectiveness are paramount. That's why we're excited to announce the integration of Arcee Conductor into the Zerve platform. This collaboration enables data scientists, engineers, and AI developers to build, automate, and scale AI workflows with enhanced precision, performance, and cost efficiency.
Zerve is an agent-driven operating system for Data & AI teams. It offers a unified space where users can collaboratively build, manage, and deploy data and AI workflows. With features such as a visual workflow canvas, real-time collaboration, and the Zerve Agent for automation, Zerve streamlines the process from prototype to production, enabling teams to focus on problem-solving rather than infrastructure management.
Zerve has onboarded data teams in major organizations globally, including NASA, Canal+ Group, and Cubic3. These partnerships highlight Zerve's impact on enhancing productivity and collaboration within data science and machine learning (ML) teams.
Arcee Conductor is an intelligent model routing platform that directs each input to its ideal AI model based on factors such as complexity, domain, cost, and other requirements. By dynamically routing between large language models (LLMs) and small language models (SLMs), Conductor maximizes cost efficiency without compromising performance.
Arcee Conductor supports a range of compact, cost-efficient Arcee SLMs, as well as third-party LLMs from OpenAI, Anthropic, and DeepSeek, catering to a wide range of use cases, from general-purpose queries to advanced coding and reasoning tasks.
GenAI is an integral part of most workflows built in Zerve. Thanks to its patented architecture, the Zerve Agent splits workflows into individual coding blocks that users can individually tune and assign to dedicated compute resources. Thanks to this integration, Zerve's GenAI block now comes with the Arcee Conductor built in.
Within a workflow, the GenAI block enables users to work with a small or large language model to develop and deploy their data and AI products. By selecting Arcee Conductor in the GenAI block, user workflows will utilize the most suitable SLM or LLM at runtime.
Integrating Arcee Conductor into Zerve enhances the platform's capabilities by:
“This integration strengthens Zerve’s position as the best place to build and run AI workflows,” said Phily Hayes, CEO of Zerve. “By incorporating Arcee Conductor, we’re making it easier for our users to create scalable, cost-efficient AI systems directly on the Zerve platform. It aligns perfectly with our mission to deliver the most powerful tools for data and AI development.”
Says Mark McQuade, CEO of Arcee AI: "Partnering with Zerve enables us to bring our intelligent model routing capabilities to a broader audience. By embedding Arcee Conductor into Zerve's platform, we're helping teams optimize their AI workflows and achieve better performance at a lower cost."
In Auto Mode, Arcee Conductor intelligently routes tasks to the most suitable model based on complexity and efficiency. For instance, a data scientist would use this mode to transform large datasets, where Conductor would automatically select the optimal model for each query, ensuring accurate and cost-effective results. SLMs would handle most queries, while only the most complex data queries would require an LLM to address.
Auto Tools Mode is designed for tasks that require function calling and tool integration. In this mode, Arcee Conductor routes tasks to models optimized for managing complex tool-based interactions and API function calls. A data engineer would utilize this mode to automate data extraction from various APIs, where Conductor would ensure that the most appropriate model handled each API call, streamlining the workflow and reducing manual intervention. In many cases, Arcee Conductor would invoke an SLM instead of an LLM, thereby decreasing the workflow’s latency and costs, and delivering a higher ROI at scale.
Auto Reasoning Mode is tailored for tasks that involve complex problem-solving and reasoning. Conductor routes tasks to models specializing in analytical capabilities. For example, in an e-commerce marketplace, a data science workflow for product matching could leverage a reasoning SLM to unify duplicate listings from different sellers. After ingesting and preprocessing data—cleaning titles, extracting structured attributes, and generating embeddings—a reasoning SLM would compare product pairs that appear similar but differ in formatting, phrasing, or detail. The reasoning step would bridge structured and unstructured data, enabling more accurate catalog consolidation, which in turn improves search, pricing intelligence, and the overall customer experience.
Experience the power of intelligent model routing in your AI workflows. Sign up for Zerve and start building with Arcee Conductor today.
Join Arcee and Zerve in a livestream demo on July 9, 12:00 noon Eastern time / 6:00 pm CET.
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