AWS Chatbot
ML5.js
Nanonets
AWS CodeCommit
AWS CodePipeline
TensorFlow
Face Swap Live
Datadog
Qrvey
DevicePilot
AnswerRocket
Syndigo
Amdocs CES
Yellowfin
Toucan Toco
Omniscope
Designed for external use cases where SaaS companies need to provide their customers with powerful and customizable analytics capabilities.
Qrvey is the only full stack solution that offers all the embedded visualization and self-service analytics tools along with a unified data pipeline that offers a data lake optimized for multi-tenant analytics.
Qrvey's embedded visualizations empower engineering teams to build custom experiences, along with full white labeling and CSS customization options to make Qrvey’s javascript widgets blend seamlessly into a SaaS application. ⋅⋅* Qrvey’s data-driven automation workflows enable the creation of complex workflows based on data triggers, such as conditional logic, nested functions, data write-backs with notification integrations to third party systems such as Slack. ⋅⋅* Qrvey supports natural language querying of data using generative AI to easily spot trends and outliers, augmented analysis capabilities. ⋅⋅* Qrvey also supports pixel perfect reporting to generate printable reports from the same analytics data.
Qrvey simplifies data management by providing a single data pipeline solution featuring a data lake solution that is optimized for multi-tenant analytics. This contains native data connectors and APIs to ingest data in any type from any source, including real-time data with live connections. ⋅⋅* Qrvey’s semantic layer can inherit and map security models from your multi-tenant SaaS application, saving software development teams the hassle of duplicating users and roles. ⋅⋅* Qrvey’s robust API allows you to create data delivery services and managed download functions that go beyond basic exporting.
AWS Chatbot QrveyNo features have been listed yet.
Qrvey's answer:
Product Leaders that include Product Management and Engineering Teams and CEO/CTO/CPOs of B2B SaaS Companies
Qrvey's answer:
Customers choose Qrvey for the following reasons:
Qrvey's answer:
Qrvey's approach to embedded analytics is different. Qrvey combines the best of BI, data warehousing, and data visualization into a single solution built exclusively for SaaS applications.
Qrvey's key features include:
100% Embeddability - Everything is embeddable with JS based components that supports full white labeling so you can create unique analytics experiences within your SaaS application.
Data Warehouse included - Visualizations are useless without a scalable data layer built specifically for analytics use cases. Qrvey includes native multi-tenant support so your data is ready for your multi-tenant SaaS application. This includes data syncing and API support that allows for any type of data to be ingested into the Qrvey data layer.
Self-Hosted - Deployed to Your AWS Environment. Customers get ultimate control as Qrvey is deployed to their AWS environment inheriting and respecting their security policies. Your data never leaves, but it's ready for analytics now.
Based on our record, AWS Chatbot should be more popular than Qrvey. It has been mentiond 6 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
For visibility of the pipelines I have set up a NotificationTopic , this topic is a SNS Topic that has AWS ChatBot as a subscriber. Chatbot will then send the updates to my Slack workspace that I have set up. This way when the pipeline is triggered I will get the notifications on my phone and laptop. - Source: dev.to / 4 months ago
Setup AWS Chatbot for best experience to get notified directly on Slack and MS Teams. - Source: dev.to / 9 months ago
AWS Chatbot: Monitor, operate, and troubleshoot your AWS resources with interactive ChatOps. - Source: dev.to / about 1 year ago
Meet AWS Chatbot. Interactive agent that makes it easier to monitor and interact with your Amazon Web Services (AWS) resources from your team’s Slack channels. By integrating AWS Chatbot with Slack, DevOps teams can receive real-time notifications, view incident details, and response incident quickly without need to cycle among other tools. - Source: dev.to / over 1 year ago
Modern machine learning algorithms are basically pattern recognition machines. They can recognize patterns in speech and can create convincing variations of that speech. Modern chatbot programs have become widely available to the public over the last decade. It's easy for anyone with a few hundred bucks to buy AWS Chatbot time and some basic programming knowledge to create a chatbot that could post convincingly... Source: over 1 year ago
Since you're on AWS already, check out https://qrvey.com. Source: 5 months ago
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