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CoeveraCRM is the largest enterprise software category in the world—yet for most organizations, the investment never translates into proportional revenue. The reason is rarely the technology. It's adoption. When reps see the CRM as overhead, data quality erodes, forecasts drift, and the system meant to drive revenue becomes a cost center. Coevera (formerly Pipeliner CRM) is the AI-native CRM engineered to fix that gap. By building development directly into the daily selling workflow, Coevera earns the adoption legacy platforms can't—because the system makes reps better, not just busier. Higher adoption means cleaner data, and cleaner data means forecasts you can actually take to the board. For revenue leaders, the outcomes are concrete: a visual pipeline that flags risk and stalled deals before they slip, embedded account management and buying-center mapping to win larger strategic deals, and a revenue-intelligence loop that drives predictable revenue and forecast accuracy. The Automatizer workflow engine removes administrative drag, while native Model Context Protocol (MCP) support connects Coevera to your AI stack with full role-based permissions and no custom middleware—keeping IT and security onside. Time-to-value is measured in weeks, not quarters, lowering implementation risk and accelerating ROI. And because every capability amplifies human judgment rather than replacing it, you protect the relationships and expertise that close deals. For organizations that need CRM spend to show up in revenue, Coevera is the platform built for what's next.
What is Ferret?
A sophisticated End-to-End MLLM has been developed to accommodate various types of references and effectively ground its responses. The Ferret Model employs a unique combination of Hybrid Region Representation and a Spatial-aware Visual Sampler, which facilitates detailed and adaptable referring and grounding functions within the MLLM framework. Serving as a foundational element, the GRIT Dataset consists of about 1.1 million entries, specifically designed as a large-scale and hierarchical dataset aimed at enhancing instruction tuning in the ground-and-refer domain. Moreover, the Ferret-Bench acts as a thorough multimodal evaluation benchmark that concurrently measures referring, grounding, semantics, knowledge, and reasoning, thus providing a comprehensive assessment of the model's performance. This elaborate configuration is intended to improve the synergy between language and visual information, which could lead to more intuitive AI systems that better understand and interact with users. Ultimately, advancements in these models may significantly transform how we engage with technology in our daily lives.
What is BenchGen?
BenchGen acts as a core learning framework for AI agents, offering a user-friendly platform where developers can discover benchmarks and reinforcement learning settings. It empowers them to evaluate the entire agent system, which includes both the model and the harness, based on measurable rewards, and it also supports the exportation of structured trajectory data for further fine-tuning. This iterative process follows a cycle of benchmark → assess → refine → reassess. By streamlining this continuous loop, BenchGen significantly boosts the development and optimization of artificial intelligence agents, ultimately leading to more robust and efficient systems. Its role in enabling systematic enhancements makes it an invaluable resource for AI developers.
Media
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Integrations Supported
Additional information not provided
Integrations Supported
Additional information not provided
API Availability
Has API
API Availability
Has API
Pricing Information
Free
Open source
Free Version
Free Trial Offered?
Pricing Information
Pricing not provided
Free Version
Free Trial Offered?
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Apple
Date Founded
1976
Company Location
United States
Company Website
github.com/apple/ml-ferret
Company Facts
Organization Name
BenchGen
Date Founded
2025
Company Location
Turkey
Company Website
benchgen.com