Qrvey is the only solution for embedded analytics with a built-in data lake. Qrvey saves engineering teams time and money with a turnkey solution connecting your data warehouse to your SaaS application.
Qrvey’s full-stack solution includes the necessary components so that your engineering team can build less.
Qrvey’s multi-tenant data lake includes:
Qrvey’s embedded visualizations support everything from: - Standard dashboards and templates - Self-service reporting - User-level personalization - Individual dataset creation - Data-driven workflow automation
Qrvey delivers this as a self-hosted package for cloud environments. This offers the best security as your data never leaves your environment while offering a better analytics experience to users.
The result: Less time and money on analytics.
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Qrvey's answer:
Product Leaders that include Product Management and Engineering Teams and CEO/CTO/CPOs of B2B SaaS Companies
Qrvey's answer:
Qrvey takes a different approach to embedded analytics. Instead of focusing almost completely on the front end, we know that any analytics function starts with data.
Qrvey includes a full-featured data lake powered by Elasticsearch, not a basic relational caching layer. Furthermore, by including a data lake, the cost to scale out is much less than traditional data warehouses.
For the user-facing components of the platform, Qrvey offers more embedded components and APIs to personalize the experience beyond static dashboards. Qrvey offers:
All of this is backed by a semantic layer that makes integrating Qrvey into the security model of SaaS applications simple.
Qrvey's answer:
Customers choose Qrvey for the following reasons:
Based on our record, Tesseract seems to be a lot more popular than Qrvey. While we know about 75 links to Tesseract, we've tracked only 1 mention of Qrvey. 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.
Many of the OCR services are based on the free, open-source Tesseract OCR, but don’t expose all of the options. If you’re handy with shell scripts or Python, you can probably get better performance by hand-tuning options for your particular images. For example, if I recall there are page segmentation options to tell Tesseract to expect multi-column text. That alone might get you better performance than the... - Source: Hacker News / 26 days ago
If you want to learn more visit the complete tesseract documentation. - Source: dev.to / about 2 months ago
AI copilots: Copilots powered by various LLMs like Pieces Copilot can leverage computer vision technologies for inputs beyond text and code. For example, optical character recognition software at Pieces uses Tesseract as its main OCR code engine, extended with bicubic upsampling. Pieces then uses edge-ML models to auto-correct any potential defects in the resulting code/text, which users can input as prompts to... - Source: dev.to / about 2 months ago
You will also need to install the Tesseract OCR engine, which can be downloaded and installed from the following link: https://github.com/tesseract-ocr/tesseract. - Source: dev.to / 5 months ago
Tesseract is an open-source OCR engine developed by Google. It is highly accurate and supports multiple languages. This library will do all the heavy lifting for us. We'll use it in this tutorial to quickly read the text in some images. - Source: dev.to / 8 months ago
Since you're on AWS already, check out https://qrvey.com. Source: 7 months ago
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