AI Bidding Proposal System for Intent-Based Answer Generation
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Solution Overview
Problem
The complexity of preparing bidding proposals, involving multiple tasks and varying languages and policies, makes it difficult for suppliers and contractors to efficiently create consistent bids that meet the requirements of different organizations.
Innovation Solution
A machine learning-based system that uses natural language processing to generate predicted answers to bid proposal questions, allowing for efficient preparation of bidding proposals by identifying user intent and providing relevant answers, which can be modified and exported for use in the proposal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual preparation of bidding proposals is used, then flexibility in handling different languages and policies is maintained, but productivity and consistency are reduced
Solution Approach 1:
The system enables self-service by automatically generating bidding proposal answers through machine learning models that analyze questions and retrieve relevant information from datasets, reducing the need for manual intervention while maintaining consistency across multiple languages and policies
Solution Approach 2:
The system handles parameter changes by adapting to different languages, policies, and project requirements through configurable datasets and machine learning models that can be trained on specific tendering requirements, allowing the same system to serve multiple different bidding scenarios
2Loss of time
If manual preparation of bidding proposals is used, then adaptability to different project requirements is maintained, but loss of time and inefficiency increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing bidding-related information in structured datasets during training phases, so that when actual bidding questions arise, the machine learning model can quickly retrieve and generate appropriate answers without time-consuming manual research
Solution Approach 2:
The system achieves universality by designing a multi-functional machine learning model that can handle various types of bidding questions, multiple languages, and different policy requirements through a single integrated platform, reducing time loss while maintaining adaptability
3Productivity
If automated answer generation is implemented, then productivity and consistency are improved, but measurement precision of user intent may deteriorate
Solution Approach 1:
The system implements feedback mechanisms where the machine learning model's intent identification and answer generation are continuously evaluated against user corrections and actual bidding outcomes, allowing the model to learn from mistakes and improve both productivity and measurement precision over time through iterative training
Data Source
AI summary
Systems and methods of the present disclosure provide a bidding proposal system for bidding proposal preparation. The bidding proposal system includes an artificial intelligence (AI)-assisted system, which generates a predicted bidding proposal based on a received request. In the system, a natural language processing technique is applied to automatically generate potential answers to the questions asked by the purchaser. The systems and methods described herein enable a computing system to understand natural language of a user by identifying the user intent and providing information to generate an answer based on the user intent.