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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Citrusˣ offers a comprehensive platform focused on AI transparency and explainability, empowering organizations to uphold trust in their models. Through the web UI and SDK, data scientists can access Summary and Validation pages to evaluate their models' performance, analyze outcomes, and troubleshoot any issues that arise. Meanwhile, data science managers and Chief Data Officers can oversee their teams' progress, benchmark different models, and confirm that key performance indicators (KPIs) are being achieved. Risk officers and Model Risk Managers (MRMs) can utilize the web interface and detailed reports to ensure the models' reliability, evaluate associated risks, and confirm that AI is employed in a responsible and equitable manner in accordance with regulatory standards. Additionally, executives and regulatory bodies can leverage tailored summary reports to assess the robustness and precision of the models, comprehend the rationale behind their decisions, pinpoint potential risks, and guarantee adherence to compliance protocols, ultimately safeguarding the organization against legal repercussions and preserving its reputation in the industry. This multi-faceted approach ensures that all stakeholders are informed and engaged in the AI governance process.

Description

Integrate AI seamlessly into your business to enhance trust and efficiency. Accelerate development and streamline deployment with the advantages of a cloud-native platform that allows for versatile deployment options. Effortlessly import models and access a well-organized model catalog from various departments within your organization. Save valuable time by quickly annotating data through model-in-the-loop hinting. Gain comprehensive insights into the origins and history of your data, models, workflows, and inferences, ensuring transparency at every step. Deploy models precisely where needed, including in edge and IoT scenarios, bridging gaps between technology and real-world applications. Valuable insights can be harnessed by all team members, not just data scientists, thanks to Chariot’s intuitive low-code interface that fosters collaboration across different teams. Rapidly train models using your organization’s production data and benefit from the convenience of one-click deployment, all while maintaining the ability to monitor model performance at scale to ensure ongoing efficacy. This comprehensive approach not only improves operational efficiency but also empowers teams to make informed decisions based on data-driven insights.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
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 No 
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) Yes 
In Person No 

Types of Training

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

Vendor Details

Company Name

Citrusˣ

Website

www.citrusx.ai/

Vendor Details

Company Name

Striveworks

Founded

2018

Country

United States

Website

www.striveworks.com/platform

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 

Product Features

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 

Alternatives

Alternatives