US-20260127649-A1 - AUTOMATING QUALITY CONTROL OF QUOTES USING INTEGRATED PROGRAMMATIC AND SPECIALIZED GUIDED AND CONSTRAINED ARTIFICIAL INTELLIGENCE

Abstract

The system and method for guiding an Artificial Intelligence (AI) engine to automate the quality control process for generating quotes. The quote generation process involves receiving quote requests from a customer relationship management (CRM) system or a data structure entry point. Moreover, retrieving quote data associated with the quote request from data sources, including a CRM platform, via application programming interfaces (APIs) triggered by the submission of the quote request. Furthermore, the prompts are generated by a prompt generator to guide the AI engine in validating the retrieved quote data. The prompts are then transferred to the AI engine for validation, a process that involves analyzing the quote data against predefined rules and conditions, such as price structures, terms, and conditions. Subsequently, a quality control result is generated based on the validation, indicating whether the quote passes or fails the validation process.

Inventors

• Arthur Michel
• Colin Guilfoyle

Assignees

• TRILOGY ENTERPRISES, INC.

Dates

Publication Date

20260507

Application Date

20251103

Claims (16)

1. 1 . A method for guiding an Artificial Intelligence (AI) engine for automating quality control of quotes in quote generation comprising: executing code using one or more processors of a computer system to cause the computer system to perform operations comprising: receiving a quote request from a customer relationship management (CRM) system or a data structure entry point, wherein the quote request includes at least one of renewal, professional services, or new business quotes; retrieving a quote data associated with the quote request from a data sources, including a CRM platform, via one or more application programming interfaces (APIs), wherein the retrieval is triggered by the submission of the quote request; generating a prompt by a prompt generator to guide the AI engine to validate the retrieved quote data; transferring the prompt to the AI engine to validate the retrieved quote data, wherein the validation comprises analyzing the quote data against predefined rules and conditions, including price structures, terms, and conditions; generating a quality control result based on the validation, wherein the quality control result indicates whether the quote passes or fails the validation; and providing real-time feedback to a user, wherein the feedback includes detailed information on any discrepancies or required corrections if the quality control result indicates a failure.
2. 2 . The method of claim 1 wherein the data structure entry point is configured to handle alternative deal structures, providing the necessary quote data for validation through predefined data formats.
3. 3 . The method of claim 1 wherein retrieving the of quote data is conducted via one or more APIs, wherein the one or more APIs are connected to a cloud-based platforms configured to manage the flow of data between the AI engine and the data sources.
4. 4 . The method of claim 1 wherein the AI engine uses machine learning algorithms to dynamically adjust the validation criteria based on historical data and patterns identified in the previous quote requests.
5. 5 . The method of claim 1 further comprising the step of automatically triggering the validation process upon submission of the quote request, wherein the process initiates without requiring manual intervention.
6. 6 . The method of claim 1 wherein the predefined rules and conditions used for validation include verification of terms and conditions, pricing accuracy, compliance with company policies, and alignment with customer-specific agreements.
7. 7 . The method of claim 1 wherein the feedback provided to the user includes detailed instructions for correcting identified discrepancies, and the feedback is delivered through automated messaging systems or email in real time.
8. 8 . The method of claim 1 wherein upon a successful validation and passing of the quality control checks, the messaging systems or email automatically forwards the quote for further processing, including submission for electronic signature through a document-signing platform.
9. 9 . A system for guiding an Artificial Intelligence (AI) engine for automating quality control of quotes in quote generation comprising: one or more processors of a computer system; and a memory, coupled to the one or more processors, that stores code and execution of the code by the one or more processors causes the computer system to perform operations comprising: receiving a quote request from a customer relationship management (CRM) system or a data structure entry point, wherein the quote request includes at least one of renewal, professional services, or new business quotes; retrieving a quote data associated with the quote request from a data sources, including a CRM platform, via one or more application programming interfaces (APIs), wherein the retrieval is triggered by the submission of the quote request; generating a prompt by a prompt generator to guide the AI engine to validate the retrieved quote data; transferring the prompt to the AI engine to validate the retrieved quote data, wherein the validation comprises analyzing the quote data against predefined rules and conditions, including price structures, terms, and conditions; generating a quality control result based on the validation, wherein the quality control result indicates whether the quote passes or fails the validation; and providing real-time feedback to a user, wherein the feedback includes detailed information on any discrepancies or required corrections if the quality control result indicates a failure.
10. 10 . The system of claim 9 wherein the data structure entry point is configured to handle alternative deal structures, providing the necessary quote data for validation through predefined data formats.
11. 11 . The system of claim 9 wherein retrieving the of quote data is conducted via one or more APIs, wherein the one or more APIs are connected to a cloud-based platforms configured to manage the flow of data between the AI engine and the data sources.
12. 12 . The system of claim 9 wherein the AI engine uses machine learning algorithms to dynamically adjust the validation criteria based on historical data and patterns identified in the previous quote requests.
13. 13 . The system of claim 9 further comprising the step of automatically triggering the validation process upon submission of the quote request, wherein the process initiates without requiring manual intervention.
14. 14 . The system of claim 9 wherein the predefined rules and conditions used for validation include verification of terms and conditions, pricing accuracy, compliance with company policies, and alignment with customer-specific agreements.
15. 15 . The system of claim 9 wherein the feedback provided to the user includes detailed instructions for correcting identified discrepancies, and the feedback is delivered through automated messaging systems or email in real time.
16. 16 . The system of claim 9 wherein upon a successful validation and passing of the quality control checks, the messaging systems or email automatically forwards the quote for further processing, including submission for electronic signature through a document-signing platform.

Description

CROSS-REFERENCE TO RELATED APPLICATION(S) This application claims the benefit under 35 U.S.C. § 119(e) and 37 C.F.R. § 1.78 of U.S. Provisional Application No. 63/714,899, which is incorporated by reference in its entirety. FIELD OF THE INVENTION The present invention relates in general to the field of electronics, and more specifically to systems and methods for automating quality control of quotes using integrated programmatic and specialized guided and constrained artificial intelligence. BACKGROUND The traditional method of quote generation and quality control (QC) has long relied on manual processes. This manual approach is not only inefficient but also introduced a host of problems that impacted both the workflow and the overall outcomes. One of the major issues with the manual processes is that it is time-consuming. In a business environment where time is of the essence, the manual process of generating quotes and conducting quality checks could result in significant delays. These delays occurred because the process required a finance team, or sometimes a dedicated QC team, to meticulously review each and every quote to ensure the accuracy. This level of scrutiny, while necessary, meant that businesses often had to wait several days before a quote could be approved, finalized, and sent to the customer. The sheer length of time taken for this review process had a ripple effect on other areas of the business. For instance, when a quote is delayed, it could hold up other operations, such as project initiation, product delivery, or service provision, all of which were dependent on accurate and timely quotes. These delays could result in a loss of business or customer dissatisfaction. Furthermore, extended delays often impacted cash flow, especially in cases where inaccurate quotes led to billing errors or delayed invoices, preventing the timely collection of revenue. The traditional quote generation process is prone to human error. Human oversight is inevitable, especially when dealing with complex pricing models, a multitude of terms and conditions, or when quotes involve intricate calculations. The risk of error was particularly high when the workload was heavy, and staff were pressed to meet deadlines. Under such conditions, small mistakes in pricing, discounts, or terms could occur, resulting in significant financial implications for the business. For example, a miscalculation in the quote could mean underpricing a product or service, which would lead to reduced profit margins. Conversely, overpricing due to an error could make the company less competitive in the market, leading to lost business opportunities. In the traditional quote generation process the review of terms and conditions are usually done manually. The terms and conditions outlined the agreed-upon price, deliverables, timelines, and any other contractual obligations. Ensuring that the correct terms and conditions were applied to each quote was an essential part of the process. However, because this aspect of QC is handled manually, it is subject to frequent errors. Incorrect terms and conditions could easily slip through the cracks, especially if the team reviewing the quotes is unfamiliar with the specifics of certain agreements or if they were dealing with an overwhelming volume of work. The misapplication of terms and conditions could lead to a host of problems. In some cases, customers could receive favorable terms that were not intended for them, resulting in lower revenue for the business. In other situations, customers might be overcharged or subjected to unfavorable terms, leading to disputes and a potential loss of trust. The manual QC process required a considerable amount of human resources. Finance teams, or teams specifically designated for quality control, had to dedicate a significant portion of their time to reviewing quotes, which prevented them from focusing on other critical tasks that could add more value to the organization. These tasks might include strategic financial planning, identifying cost-saving opportunities, or working on process improvements. Instead, highly skilled individuals were often bogged down by the repetitive and time-intensive task of reviewing quotes for errors. The high failure rate in QC for quote generation is another critical issue in the traditional method of quote generation and QC. When the QC failed to catch the issues in time, it could result in serious consequences, including revenue leakage, customer dissatisfaction, and potential legal liabilities. The manual review process was further complicated by the need to collaborate with other departments or stakeholders. For example, the finance team might need to cross-check certain elements of a quote with the legal department to ensure compliance with contractual obligations, or with the sales team to confirm that the pricing was in line with the customer's negotiated terms. This back-and-forth communication often caused additional del