Introduction

GPTBots.AI is an enterprise-grade no-code AI Agent development platform. The platform is dedicated to providing efficient and stable AI solutions for enterprises through artificial intelligence technology, driving business growth and enhancing work efficiency. With an intuitive and user-friendly design interface at its core, users can quickly build intelligent AI Agents with simple drag-and-drop operations, without any programming knowledge, and rapidly apply them to enterprise business scenarios.

GPTBots Product Features

No-Code AI Development Capability: Users can build enterprise-grade AI Agents through visual drag-and-drop operations without writing any code, facilitating quick adaptation to various complex business scenarios.
End-to-End Delivery Capability: From AI solution design, deployment, delivery to operation and maintenance, the platform provides a full-process solution for enterprises, ensuring project delivery and strong performance in production.
Powerful Product Capabilities: The platform supports: natural language database joint table queries (Text2SQL), data-driven dynamic interactive charts (Data2Chart), multimodal input/output, efficient and accurate RAG systems, comprehensive REST API, model deployment and fine-tuning services, LLM load balancing, visual Tool building, user query classification and sentiment recognition, system business alert notifications, comprehensive operational data analysis, and other powerful features and services.
Data Security and Compliance: The platform supports content security review, information anonymization, encrypted storage, ISO certification, RBAC, and other security measures and mechanisms. It also provides privatization services to meet the stringent requirements of enterprises for data security and compliance.
Enterprise-Level SLA Guarantee: Offers high availability and stability with enterprise-grade Service Level Agreements (SLA), ensuring platform reliability and continuity. Professional technical support and services provide strong service assurance for enterprise users.

Overview of GPTBots Core Functional Modules

Agent

Designed for simple business scenarios, enabling minute-level creation of AI Agents through simple configurations to quickly respond to common business needs, helping enterprises achieve business intelligence rapidly.

FlowAgent

Designed for complex business scenarios, supporting the manual design of intricate workflows and logic, enabling the orchestration of multiple specialized LLMs to achieve more controlled and efficient AI responses, effectively meeting the intelligent needs of complex enterprise operations.

Knowledge Base

• Supports various types of knowledge data such as doc/docx, pdf, txt, markdown, csv, xls/xlsx, web crawling, Q&A, etc.;
• Uses different data parsing and segmentation schemes for different data types to improve data quality and integrity;
• Supports a hybrid search scheme of sparse and dense vectors to enhance knowledge recall accuracy;
• Allows management, editing, and updating of knowledge documents at the slicing dimension;
• Supports enhancement techniques like query augmentation and rerank to improve recall rates and accuracy.

Database

• Supports databases such as MySQL, SQLite, PostgreSQL, SQL Server, Oracle, MongoDB, Redis, Elasticsearch (coming soon);
• Enables natural language multi-table joint queries, distributed queries, and computations to improve query efficiency and response speed;
• Generates dynamic interactive charts based on queried Data to enhance user experience and data visualization effects.

Tools

• Supports the creation of AI Tools in a visual manner, providing more capabilities for LLM;
• GPTBots offers a vast library of official Tools and supports developers in customizing Tools to meet enterprise needs;
• Developers can seamlessly connect with enterprise data and service capabilities through custom Tools while ensuring enterprise data security.

Models

• Supports out-of-the-box mainstream commercial models, open-source models, domain-specific models, and fine-tuned custom models;
• Allows developers to add and use enterprise-owned keys, ensuring data security and supporting key load balancing to enhance LLM service stability;
• Both commercial and open-source models can quickly generate fine-tuning corpus data based on knowledge base data and user dialogue data for model fine-tuning;
• Eliminates the need for extensive effort in LLM deployment and fine-tuning, enabling developers to focus more on core business.

Continuous Training

• Chat records support quality scoring, keyword extraction, and topic summarization, making it easier for developers to gain insights into user concerns;
• Bot training mode supports real-time correction of "conversation content," enabling continuous training of Bots for better responses.

Query Classification and Sentiment Recognition

• Supports summarization, induction, and classification of user queries, helping developers understand high-frequency user questions to optimize and supplement relevant knowledge;
• Supports sentiment recognition (5 levels) for user queries, helping developers understand user emotional states to optimize AI Agent interaction experiences;
• Supports alert notifications based on user query classifications, suitable for timely alerts and notifications during concentrated outbreaks of specific types of issues.

Operational Data Analysis and Insights

• Supports data statistics and analysis across multiple dimensions such as today's data, effectiveness, usage, users, behavior, health, credit consumption, and usage consumption;
• Provides operational data statistics, analysis, and insights at the AI Agent level, helping developers understand the operational status and performance of AI Agents, and promptly identify and resolve issues;
• Offers operational data statistics, analysis, and insights at the organizational level, helping developers understand the operational status and performance of the organization, and promptly identify and resolve issues.

How does GPTBots product solve the challenges of LLM landing in enterprises?

LLM Illusion Problem

The illusion of LLM is mainly related to the underlying architecture of the model and training data. Illusions can make enterprise LLM applications unreliable, untrustworthy, and even potentially harmful.

• Accurate knowledge supplementation of context.
• Bot training and LLM fine-tuning to correct the model.
• Designing reflection mechanisms and verification tools for LLM.
• Optimizing Prompt to limit the range of responses.

General LLM lacks domain knowledge

Due to the lack of domain knowledge, general LLMs cannot provide correct responses, making it difficult for enterprises to use LLM to solve business problems. Additionally, training models separately for various vertical scenarios is cost-prohibitive.

• Knowledge base supports precise knowledge retrieval.
• Easily import unstructured knowledge data.
• Supports connection and identification of structured data.
• Plugins bridge internal domain knowledge within the enterprise.

Single LLM cannot solve complex tasks in enterprise business scenarios

The complex reasoning ability of LLM is still weak and cannot effectively solve complex tasks in enterprise operations. Additionally, single-point single-thread tasks cannot meet the needs of actual business scenarios in enterprises.

• Break down complex problems into multiple branches.
• Flow supports the collaboration of multiple versions of LLM.
• LLM has capabilities such as long short-term memory, plugins, and knowledge base.
• Incorporate external feedback and information into the LLM response process.

AI is difficult to implement in enterprises

LLM landing involves compliance, data, computing power, engineering, and algorithms. Any quality issues in any of these areas will significantly impact the application of LLM in scenarios, especially when using open-source models, leading to a sharp increase in costs for hardware and manpower.

• Provides a simple and efficient LLMOps platform.
• Solves the challenges of knowledge data loading and retrieval.
• Provides out-of-the-box AI Bot building capabilities.
• Rich and comprehensive API and SDK.

Enterprises lack talent reserves in the AI field

Enterprises need talent in the AI field with capabilities in data, algorithms, engineering, and business. For enterprises, the shortage of AI talent, slow cultivation, and high costs are significant challenges.

• Nearly zero-threshold use of GPTBots.
• Bot training and fine-tuning LLM capabilities for product operation personnel.
• No need for extensive AI domain knowledge; enterprise business personnel can also train and optimize the Bot.
• Developers can complete integration through API interfaces.