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May 25, 2024

Squirro

Switzerland Squirro Database management

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Empower enterprise decisions with AI-driven insights.

Retrieval-augmented Generation Enterprise Search Enterprise-grade Security Semantic Search Large Language Models Refined ResponsesFree + from $30/mo

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Overview

Squirro is a versatile generative AI tool designed for enterprise search, insights, and automation. It is built with an advanced AI framework, Retrieval Augmented Generation (RAG), that enhances the accuracy of the responses generated by larger language models (LLMs).

This enhancement is made possible by incorporating external sources of knowledge to support the model's internal understanding. The SquirroGPT component of the tool uses Semantic Search to query the LLM, facilitating a more efficient and informed data retrieval process.

When a user enters a prompt, SquirroGPT searches the knowledge base, including the ingested data and documents. Relevant information is then sent to the LLM, and the response is verified against the knowledge base before being relayed to the user.

This ensures each answer is supported with evidence, and the refining process reduces inaccurate responses. The tool also specializes in providing accessibility to complex organisational data, with the capacity to define data sources and permission rights to suit individual business units.

It enables users to interact with data without needing to open documents, providing more precise results by analyzing relevant paragraphs instead of entire documents.

This tool offers enterprise-grade security and can be embedded for wide audience accessibility.

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SquirroInitial

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Initial release

April 6, 2022

Initial release of Squirro.

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Organization Squirro 🇨🇭 Switzerland

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Database management Squirro

Switzerland Squirro Database management

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Pros and Cons

Pros

Enterprise search capability

Enhances LLM accuracy

Incorporates external knowledge

Semantic search feature

Efficient data retrieval

Knowledge base verification

Reduced inaccurate responses

Access to organisational data

Ability to define data sources

Customisable permission rights

Data interaction without document opening

Precise results via paragraph analysis

Enterprise-grade security

Can be embedded

Reference supported answers

Data source and right definition

Conversationally interact with documents

Four types of summarisation

Personalised summaries

Combine search with context aware chat

Interaction with structured data in chat

Structured data analysis and visualisation

Bridges data silos

Context and intent based information sharing

Semantic enterprise search

No-code ML platform

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Cons

Complex data integration process

Inferior document summarization

Limited structured data analysis

Dependent on relevant data ingestion

Requires data source definition

Potentially high permission management

Unclear error validation process

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Q&A

What is Squirro and its key features?

Squirro is a generative AI tool purposed for enterprise search, insights, and automation. Key features include: utilization of an advanced AI framework, Retrieval Augmented Generation (RAG), for enhanced response accuracy from larger language models (LLMs); the integration of external knowledge sources to support the model's understanding; and a component, SquirroGPT, that uses Semantic Search to make the data retrieval process efficient and informed. Furthermore, Squirro offers refined responses, capabilities to access complex organizational data, options to define data sources and permission rights to match individual business units, and enterprise-grade security. Additionally, Squirro lets users chat with all organizational data and every answer is supported by a reference source.

What is Retrieval Augmented Generation (RAG) in Squirro?

Retrieval Augmented Generation (RAG) in Squirro is an AI framework designed to enhance the accuracy of responses generated by LLMs. RAG uses external sources of knowledge to complement the model's internal understanding. Squirro uses RAG, with its Semantic Search, to query the LLM, a process that ensures more precise and informed responses.

How does Squirro use larger language models (LLMs)?

Squirro uses larger language models (LLMs) in its Retrieval Augmented Generation (RAG) technology. This process starts when a user enters a prompt, triggering Squirro's semantic search to query relevant data from the knowledge base, including the ingested data and documents. The relevant information is then sent along with the query to the LLM. To verify the accuracy, the response from the LLM is checked against the knowledge base before delivering the answer to the user.

What is the role of Semantic Search in the SquirroGPT component?

In the SquirroGPT component, Semantic Search is used to query the LLM, an action that facilitates a more efficient and informed data retrieval process. When a user enters a prompt, Semantic Search is the mechanism that scans the knowledge base, including the ingested data and documents. This search strategy facilitates in acquiring contextually relevant responses.

How does the data retrieval process work in Squirro?

In Squirro, the data retrieval process works by using Squirro's Semantic Search for querying the knowledge base once a user enters a prompt. The retrieval process involves searching through the data and documents ingested, sending relevant information to the LLM, and then validating the response against the knowledge base before giving the answer to the user.

What happens when a user enters a prompt in Squirro?

When a user enters a prompt in Squirro, the system's Semantic Search initiates a scan through the knowledge base which includes ingested data and documents. SquirroGPT then sends the relevant information gathered to the Large Language Model (LLM) for response generation.

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How does Squirro ensure the accuracy of the responses?

Squirro ensures the accuracy of responses through its Retrieval Augmented Generation technology. More specifically, after the LLM generates a response, it undergoes an additional verification layer where it is validated against the knowledge base once more before it is relayed to the user. It utilizes Semantic Search and LLMs in conjunction to seek context-relevant data ensuring that each answer is supported by evidence, thus reducing the potential for inaccurate responses.

What capabilities does Squirro have in accessing complex organisational data?

Squirro has unique capabilities in accessing complex organizational data. It allows users to define the data sources and permission rights suitable for their specific business units. This accessibility allows users to interact with data without having to open the documents themselves, providing more precise results. Squirro can locate and analyze relevant paragraphs instead of entire documents.

How can individual business units customize Squirro?

Individual business units can customize Squirro by defining the data sources and permission rights that best suit their operational needs. Each answer that Squirro provides is supported by evidence - a reference source. The chat feature can be made available to a wider audience through embedding.

What type of security does Squirro offer?

Squirro offers enterprise-grade security. This ensures the safe handling and processing of data, maintaining the integrity and confidentiality of users' information. This also ensures that Squirro can safely be used across a variety of business units whilst respecting necessary data protections and permissions.

How does Squirro interact with data without opening documents?

Squirro can interact with data without having to open documents. Using its Retrieval Augmented Generation technology, Squirro provides more precise results by locating and analyzing only relevant paragraphs, instead of a whole document. This reduces unnecessary information consumption, making the entire process more efficient.

Can Squirro be embedded for wide audience accessibility?

Yes, Squirro can be embedded for wide audience accessibility. This means that Squirro's face chat, equipped with all its powerful features, can be accessed by a wide range of audience--from individual business units to multiple departments, thus providing them with the facility to chat with data, extract information and get concise responses efficiently.

Does Squirro assist in database management?

Squirro assists in database management by providing accessibility to complex organizational data. It stores such data in a knowledge base which includes ingested data and documents. The system allows users to define data sources and permission rights to suit their specific business unit requirements. Essentially, users can interact with data without having the need to open specific documents, hence, managing and accessing essential information in a more efficient manner.

How does Squirro contribute to enterprise automation?

Within enterprise automation, Squirro plays a significant role by providing advanced search and information retrieval capabilities that automate the process of extracting key insights from large data sets. By implementing Retrieval Augmented Generation (RAG) technology, Squirro offers more accurate and context-relevant responses that serve to automate decision-making by providing crucial insights that would otherwise require considerable time and resources to obtain.

What is the role of the knowledge base in Squirro?

The role of the knowledge base in Squirro is integral. It contains ingested data and documents which are essential for data retrieval. When a user enters a prompt, SquirroGPT instigates a search through the knowledge base. After the LLM generates a response, it's validated against the knowledge base again to ensure accuracy before it is relayed back to the user.

How does Squirro provide evidence-based responses?

Squirro provides evidence-based responses by utilizing Semantic Search when a user enters a prompt. The search encompasses the knowledge base, including the ingested data and documents. The relevant information is then sent to the Larger Language Model, and as an extra layer of verification, the response is checked against the knowledge base before the answer is passed back to the user. This process ensures each response is supported by evidence.

How does Squirro refine responses?

Squirro refines responses by deploying its Retrieval Augmented Generation (RAG) technology. After the Large Language Model (LLM) generates a response, it undergoes a verification layer where it is checked against the knowledge base before being delivered to the user. This allows Squirro to provide accurate, context-relevant and evidence-backed answers while filtering out potential inaccuracies.

What are some industries or business units that can benefit from Squirro?

Almost any industry or business unit that deals with large amounts of data and requires informed, quick decision making can benefit from Squirro. This includes but is not limited to Knowledge Management, Risk, Compliance & Audit, Service Management, and Sales Management. Each unit can customize data sources and permission rights to suit their specific needs.

Is there a demo or trial available for Squirro?

Yes, Squirro offers a demo. Users can book a one-on-one demo with one of Squirro's product experts to see all of the product's features firsthand. The demo is available for free.

What kind of customer support or resources does Squirro provide for its users?

Squirro provides numerous resources for its users on their website. These resources include case studies, whitepapers, webinars, and a blog. They also offer a forum and an academy. Users can also reach out for support by contacting Squirro directly through their contact page.

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