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features
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support

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Write a Review

Description

Aurora GPS enables B2B companies to leverage win-loss analysis to uncover the fundamental reasons behind their successes and setbacks, equipping sales teams with the practical strategies they require to accelerate their business closures. The sales process does not need to be a puzzling enigma. While your CRM offers insights into deal volume, funnel progression, and overall win rates, it raises critical questions: What factors contributed to your victories? What led to your defeats? Often, the answers are reduced to brief notes within the deal documentation. To gain a comprehensive understanding of these factors—and to implement effective changes—you must explore further. Cultivating connections with both successful and unsuccessful deals signals your dedication to continuous improvement. For instance, a prominent medical device firm consistently lagged behind its rivals in securing adoption of its products by key healthcare institutions. Although it achieved success with smaller healthcare providers, it struggled to fulfill its revenue and profit aspirations solely through these smaller partnerships. By analyzing the reasons behind these discrepancies, the company could identify strategic adjustments necessary for broader market success.

Description

Effortlessly deploy all leading machine learning frameworks like Pytorch, Onnx, and XGBoost with a single line of code. If you lack your own models, take advantage of our prebuilt options that are optimized for performance with sub-second latency. You can also fine-tune smaller models for specific tasks, which helps to reduce both costs and latency while enhancing overall performance. With just a few lines of code, you can avoid the hassle of managing infrastructure because we handle that for you. Seamlessly integrate with premier ML observability platforms to receive alerts about any feature or prediction drift, allowing for quick comparisons between model versions and prompt issue resolution. Additionally, you can identify the root causes of prediction and feature drift to tackle any decline in model performance effectively. Gain insights into which features are most influential in driving your model's performance, empowering you to make informed adjustments. This comprehensive approach ensures that your machine learning processes are both efficient and effective.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

PyTorch No 
TensorFlow No 

Integrations

PyTorch Yes 
TensorFlow Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$ 0.00055 per second
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Aurora

Country

United States

Website

aurora-gps.com/intelligence-services/win-loss-analysis-sales-effectiveness/

Vendor Details

Company Name

Cerebrium

Website

www.cerebrium.ai/

Product Features

Product Features

Artificial Intelligence

Chatbot No 
For Healthcare No 
For Sales No 
For eCommerce No 
Image Recognition No 
Machine Learning No 
Multi-Language No 
Natural Language Processing No 
Predictive Analytics No 
Process/Workflow Automation No 
Rules-Based Automation No 
Virtual Personal Assistant (VPA) No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

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