Negotiation AI

IntelliNego technology is rooted in academic research, integrating mathematical negotiation models, advanced algorithms, and machine learning techniques. The result is our autonomous AI Negotiation Agent, a deterministic and transparent system capable of operating with varying degrees of autonomy.

Our Negotiation Management App stands out for its adaptability and user-friendly design. The AI algorithms work “out of the box”, which means that company historical data are not necessary. Negotiation Agents learn the preferences of counterparts in real-time and continuously refine their strategies to secure better deals, faster.

All AI settings are pre-set to values that have been extensively tested to ensure optimal performance from the start. However, you have the option to adjust the AI settings to align the negotiation strategy with your company’s objectives.

Automated Negotiations

Automated negotiations involve the use of artificial intelligence systems to autonomously conduct and optimize negotiation processes on behalf of organizations. These systems leverage mathematical models, algorithms, machine learning, and decision-making models to analyze information, formulate offers, counteroffers, and ultimately reach agreements. This technology is particularly useful in business contexts where negotiations are frequent, and efficiency and optimization of outcomes are critical.

Automating negotiations significantly increases the number of suppliers and negotiation rounds, reducing processing time. Speed is a key driver of value. Buyers' roles evolve with an emphasis on strategic expertise, engaging in more procurement options, utilizing templates or scorecards for business unit needs, and identifying more qualified suppliers to enhance supply chain resiliency. Business unit heads maintain responsibilities in defining requirements, terms, conditions, and budgets.

Suppliers enjoy shorter sales cycles, real-time feedback on their standing, and confidence in fair treatment, even in situations involving entrenched incumbent suppliers.

Automated Negotiations vs. e-auctions

Automated negotiations and e-auctions are both digital approaches to reaching agreements, but they differ in their mechanisms and goals. Here's an outline of the main differences between automated negotiations and e-auctions:

Automated Negotiations

Negotiation Process
Dynamic Interaction: Automated negotiations involve dynamic interactions between computer algorithms or Agents representing each party or between a software agent and a human. These algorithms analyze information, propose offers, and counteroffers based on predefined rules and criteria.

Decision-Making
Algorithmic Decision-Making: The negotiation decisions are made algorithmically, considering factors such as predefined objectives, constraints, and optimization criteria. The process aims to find an agreement that meets the specified criteria.

Customization
Tailored Algorithms: Automated negotiation systems often allow customization of negotiation algorithms based on the specific preferences and strategies of the parties involved. This customization enables a more personalized negotiation approach.

Information Processing
Advanced Analytics: Automated negotiation systems leverage advanced analytics, various algorithms, and artificial intelligence to process large amounts of data, assess patterns, and make informed decisions.

Flexibility
Adaptability to Complex Scenarios: Automated negotiations can handle complex scenarios and multiple variables, adapting in real-time to changing conditions during the negotiation process.

E-auctions

Auction Process
Bidding-Based Process: E-auctions involve a bidding-based process where participants submit bids electronically through an online platform. The highest bidder typically wins, and the process is characterized by a series of rounds with predefined rules.

Decision-Making
Winner-Takes-All: In e-auctions, the decision-making is straightforward, with the participant offering the highest bid winning the auction. The focus is on obtaining the best possible terms within the constraints of the auction format.

Customization
Limited Participant Customization: E-auctions may have predefined rules and formats, limiting the level of customization for individual participants. The auction platform sets the parameters that participants must adhere to.

Information Processing
Transparent Information: E-auctions often involve transparent information sharing, where participants can see relevant details about the bidding process, including current bids and bid history. However, specific details may be limited to maintain a competitive edge.

Flexibility
Rigid Structure: E-auctions typically follow a rigid structure with fixed timelines and rules. There is limited flexibility for participants to negotiate beyond the predefined parameters.

In summary, automated negotiations involve algorithmic decision-making with a focus on customization, adaptability, and dynamic interaction, while e-auctions are characterized by a bidding-based process with a more rigid structure, winner-takes-all decision-making, and limited participant customization.

What is AI?

AI, or Artificial Intelligence, refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks include decision making, learning, reasoning, problem-solving, perception, language understanding, and even speech recognition. The goal of AI is to create machines that can replicate and simulate cognitive functions associated with human intelligence.

There are two main types of AI:
Narrow AI (Weak AI): This type of AI is designed and trained for a specific task or a narrow set of tasks. Narrow AI is proficient at performing well-defined tasks, but it lacks the broad cognitive abilities and understanding that humans possess. Examples include virtual personal assistants, image recognition systems, and speech recognition software.
General AI (Strong AI): General AI refers to a form of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence. General AI would have the capacity to perform any intellectual task that a human being can. As of now, true General AI remains a theoretical concept, and the field is primarily focused on developing Narrow AI applications.

AI can be implemented using various techniques, including:
Rule-Based Systems: Using explicit rules to make decisions or solve problems.
Machine Learning: Allowing systems to learn from data and improve their performance over time.
Natural Language Processing (NLP): Enabling machines to understand, interpret, and generate human-like language.
Computer Vision: Empowering machines to interpret and make decisions based on visual data.

Machine Learning (ML)
Machine Learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through learning from data, without being explicitly programmed. The primary goal of machine learning is to develop models that can generalize patterns from data and make accurate predictions or decisions.

Natural Language Processing (NLP) AI
Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language. The goal of NLP is to enable machines to understand, interpret, and generate human-like language in a way that is both meaningful and contextually relevant.

In summary, Machine Learning is a broader field focused on developing algorithms that learn from data, while Natural Language Processing is a specialized area within AI that deals specifically with the understanding and processing of human language. NLP often utilizes machine learning techniques to achieve its goals.

AI Agent
In the context of artificial intelligence, an AI agent refers to a software program or system that is designed to perform specific tasks or make decisions autonomously. The concept of an AI agent is rooted in the field of agent-based systems, where autonomous entities, called agents, interact with their environment, devising a strategy to achieve predefined goals. An AI agent has the following key characteristics:
Autonomy
An AI agent is autonomous, meaning it can operate independently and make decisions without direct human intervention. The level of autonomy can vary, from simple rule-based decision-making to more sophisticated learning-based approaches.
Perception
AI agents are equipped with sensors or mechanisms for perceiving their environment. Perception involves gathering information about the state of the environment, which the agent uses to make informed decisions.
Decision-Making
AI agents possess decision-making capabilities to choose actions or responses based on their perception of the environment. The decision-making process can be rule-based, heuristic-driven, or involve machine learning algorithms.
Action
Following the decision-making process, AI agents take actions to influence or interact with their environment. Actions can range from simple commands to complex sequences of behaviors, depending on the agent's design and purpose.
Goal-Oriented
AI agents are typically designed to achieve specific goals or objectives. These goals guide the agent's behavior and determine the actions it takes in pursuit of those objectives.
Learning (Optional)
Some AI agents incorporate learning capabilities, allowing them to adapt and improve their performance over time. Learning may involve techniques such as machine learning, reinforcement learning, or other forms of adaptive behavior.