Description:FIELD OF INVENTION
The present invention relates to software deployment. More specifically, it relates to system and method for refining a product roadmap using post-deployment metrics.

BACKGROUND
Using post-deployment metrics is crucial in computer science for ensuring software quality, performance and user satisfaction after release. It provides insight into how the deployed application is behaving in the real world, allowing continuous improvement. A product roadmap is essential to communicating how short-term efforts match long-term business goals. Understanding the role of a roadmap and how to create a great one is key for keeping everyone on your team headed in the same direction.
Conventional system and method faced certain challenges such as difficulties in measuring and evaluating the performance, health and user involvement of software system after it’s been released production. These challenges often arise from the complexities of real-world usage, unexpected user interaction and the limitation. Product deployment today often involves agile, iterative releases, yet decisions about the future roadmap remain largely dependent on intuition, stakeholder priorities, or isolated data sources like sales performance. This fragmented input lacks real-time grounding in actual product usage and impact.
PRIOR ART
202541042711 discloses a system and method relates blockchain-based business analytics system for real-time affective engagement metrics in e-commerce in the rapidly evolving landscape of e-commerce, businesses seek real-time insights into customer engagement to optimize decision-making. This system presents a blockchain-based business analytics system that utilizes effective computing and machine learning to analyze real-time effective engagement metrics securely and transparently. The system integrates FER, SA, and eye-tracking technologies to extract emotional and behavioral cues from customer interactions. These metrics are processed using a hybrid deep learning model combining CNNs and RNNs for effective state classification. To ensure data integrity, security, and decentralization, hyper-ledger fabric, a permissioned blockchain framework, is employed to store engagement metrics, preventing unauthorized access and tampering.
US11442764B2 discloses a system and method relates a computer-implemented method includes, monitoring, by a computing device, performance of currently deployed virtual machines (VMs) that implement particular services; determining, by the computing device, optimal configuration options for deployment of new VMs that implement one or more of the particular services based on the monitoring the performance of the currently deployed VMs; and outputting, by the computing device, information regarding the optimal configuration options to a user requesting the deployment of a new VM implementing one or more of the particular services.
While the prior arts limits only to blockchain based business analytical system. Further, the system possesses privacy concerns like facial expression recognition, real time collection and processing of such data which may raise serious user privacy concern and regulatory compliance issues. Further, the prior system also faces high computational and storage overhead, scalability issues, slower transaction throughout, increased storage coasts another concern is about complexity of system integration, real time processing challenges, user consent and acceptance. Yet another prior art includes computer-implemented method including monitoring performance of currently deployed virtual machines (VMs) that implement particular services, determining optimal configuration options for deployment of new VMs and outputting. While this approach offers efficiency and responsiveness, it also has several potential drawbacks including data quality and accuracy dependence, inflexibility to sudden workload changes, lack of contextual or business aware decisions. Further, none of the prior arts provides an effective solution to overcomes the all the aforementioned challenges face by the existing systems relating to software deployment.
Thus, there is an unmet need for a systematic, intelligent, and adaptive method that connects post-deployment behavior with strategic roadmap definition in a measurable, automated, and scalable manner. The present invention provides such a system and method for refining product roadmap by analyzing post-deployment metrics, business systems and market sentiment.

DEFINITIONS:
The expression “system” used hereinafter in this specification refers to an ecosystem comprising, but not limited to, system for automatically defining post-deployment success metrics with input and output devices, processing unit, plurality of mobile devices, a mobile device-based application. It is extended to computing systems like mobile phones, laptops, computers, PCs, and other digital computing devices.
The expression “input unit” used hereinafter in this specification refers to, but is not limited to, mobile, laptops, computers, PCs, keyboards, mouse, pen drives or drives.
The expression “output unit” used hereinafter in this specification refers to, but is not limited to, an onboard output device, a user interface (UI), a display unit, a local display, a screen, a dashboard, or a visualization platform enabling the user to visualize the graphs provided as output by the system.
The expression “processing unit” refers to, but is not limited to, a processor of at least one computing device that optimizes the system, and acts as the functional unit of the system.
The expression “large language model (LLM)” used hereinafter in this specification refers to a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
The term “deployment metrics” used hereinafter in this specification refers to a quantitative measure used to evaluate the performance of the software deployment process.
The term “product roadmap” used hereinafter in this specification refers to a strategic visual document that outlines a product's vision, direction, and planned development over a specific timeframe.
The term “return on investment” or “ROI” used hereinafter in this specification refers to the benefits gained from testing efforts compared to the costs involved.
The term “CRM” used hereinafter in this specification refers to the process of evaluating a Customer Relationship Management (CRM) system to ensure it functions correctly, meets business requirements, and provides a seamless user experience.
The term “orchestrator” used hereinafter in this specification refers to a tool or system that manages and coordinates the execution of multiple automated tests, ensuring they run in a predefined sequence and with the necessary data and resources. It is like a conductor leading an orchestra, ensuring each instrument (test) plays its part at the right time to create a harmonious performance (successful testing).
The term “clickstream” used hereinafter in this specification refers to a process of tracking, analyzing and reporting data on the pages a user visits and user behavior while on a webpage.
The term “pipeline” used hereinafter in this specification refers to a series of automated processes that take predefined code from initial development to deployment, often involving steps like building, testing, and deploying to production environments. It is a structured workflow that automates and streamlines the software development process.

OBJECTS OF THE INVENTION:
The primary object of the present invention is to provide a system and method for refining a product roadmap using post-deployment metrics.
Another object of the present invention is to provide a system and method for refining product roadmaps by analyzing post-deployment metrics, business systems and market sentiment.
Yet another object of the present invention is to is to provide a system and method that uses statistical and machine learning techniques for market sentiment and business systems.
Yet another object of the present invention is to provide a system and method that uses an LLM-driven orchestrator consolidates the inputs to calculate return on investment (ROI) scores for potential upcoming features.
Yet another object of the present invention is to provide a system and method that operates high-ROI features are then prioritized automatically for future development.

SUMMARY
Before the present invention is described, it is to be understood that the present invention is not limited to specific methodologies and materials described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention.
The present invention discloses a system and method for refining a product roadmap using post-deployment metrics; wherein the system comprises an input unit, a processing unit, and an output unit. The processing unit includes several functional modules that operate in coordination: a post-deployment monitoring module for runtime data collection, a business data ingestion engine for extracting structured business metrics from CRM and sales platforms, a market signal aggregator for capturing external market sentiment and feedback, a feature ROI estimation engine to compute return on investment for future features, an LLM-orchestrator that acts as a reasoning core across all data streams, and a roadmap prioritization engine that ranks and tags candidate features. These components together enable automated, intelligent, and data-informed roadmap planning.
In a preferred aspect, the method begins by collecting post-deployment telemetry to assess feature engagement and stability. It then ingests structured business data, such as revenue impact and customer behavior, and correlates these to specific features. In parallel, it aggregates unstructured market signals from online sources and performs NLP-based sentiment and trend analysis. The LLM-orchestrator processes all gathered inputs to calculate a Feature Impact Index (FII) for deployed features and to predict revenue, cost, and technical debt for candidate features. Using this, the system computes ROI and a prioritization score for each new feature, factoring in urgency and similarity to successful features. Finally, the roadmap prioritization engine ranks the features, assigns status labels, and exports the updated roadmap to external tools like JIRA or Asana.
In yet another aspect, the system and method provides a structured and intelligent approach to roadmap refinement by combining telemetry, business data, and market intelligence. It ensures data-driven, ROI-based prioritization that aligns product planning with user behavior, business impact, and external demand. The inclusion of an LLM enhances contextual reasoning and adaptability, while the use of technical debt analysis encourages sustainable development decisions. The modular design supports automation and integration, reducing manual effort and improving responsiveness to real-world feedback, thereby resulting in more effective, strategic, and market-aware product evolution.

BRIEF DESCRIPTION OF DRAWINGS
A complete understanding of the present invention may be made by reference to the following detailed description which is to be taken in conjugation with the accompanying drawing. The accompanying drawing, which is incorporated into and constitutes a part of the specification, illustrates one or more embodiments of the present invention and, together with the detailed description, it serves to explain the principles and implementations of the invention.
FIG.1. illustrates the structural and functional components of the system.
FIG.2. illustrates the schematic flow of the method employed by the processing unit.
FIG.3. illustrates a stepwise workflow followed by the system.

DETAILED DESCRIPTION OF INVENTION:
Before the present invention is described, it is to be understood that this invention is not limited to methodologies described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention. Throughout this specification, the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the invention to achieve one or more of the desired objects or results. Various embodiments of the present invention are described below. It is, however, noted that the present invention is not limited to these embodiments, but rather the intention is that modifications that are apparent are also included.
The present invention discloses a system and method for refining a product roadmap using post-deployment metrics; wherein the system (10) comprises of at least one input unit (1), a processing unit (2) further comprising a post deployment monitoring module (3), a business data ingestion engine (4), a market signal aggregator (5), a feature ROI estimation engine (6), a LLM- orchestrator (7), a roadmap prioritization engine (8), and at least one output unit (9); such that the modules work in co-ordination to employ a stepwise method or workflow for refining a product roadmap.
In an embodiment of the invention, the post-deployment monitoring module (3) is one of the data sources configured to collect and analyze runtime operational data corresponding to deployed features; which includes but is not limited to feature usage analytics, performance logs, telemetry, user behaviour or interaction patterns such as feature engagement rate, session lengths or error incidence. The module continuously tracks and aggregates the collected data on feature basis to generate quantitative measures such as engagement rates, error incidence, and session lengths, which form the basis for evaluating feature performance in real-world conditions.
In an embodiment of the invention, the business data ingestion engine (4) is another data source responsible for interfacing with external deterministic business systems, such as CRM platforms (e.g., Salesforce, HubSpot), to extract structured business metrics. The engine map features usage to downstream business outcomes, including revenue contribution, pipeline movement, customer churn, custom fields, success logs, or support tickets. Data fields are ingested via APIs or data pipelines and normalized for further processing in ROI estimations.
In a next embodiment of the invention, the market signal aggregator module (5) collects unstructured qualitative and quantitative data from external market-facing sources such as online forums (e.g., Reddit, Stack Overflow, LinkedIn, Twitter), product reviews, social media sentiment, and analyst opinions. The module leverages Natural Language Processing (NLP) techniques to extract relevant insights including sentiment polarity, feature-specific feedback such as likes and dislikes, adoption trends, and recurring themes. This data is subsequently clustered by feature and use-case for downstream analysis.
In a next embodiment of the invention, the feature return on investment (ROI) estimation engine (6) computes the expected return on investment (ROI) for candidate features that are under consideration for inclusion in the product roadmap. ROI is computed using a hybrid approach combining deterministic metrics with probabilistic inference powered by a language model. The engine uses expected revenue, expected cost and technical debt impact to estimate the ROI; where the expected revenue is derived from reasoning over similar historical features and market trend, expected cost includes engineering effort estimations, infra cost, training, rollout, and technical debt impact is computed by codebase-level impact analysis.
In a next embodiment of the invention, the LLM-orchestrator (7) functions as a central reasoning engine similar to a driving orchestrator or a pipeline, that coordinates multi-modal data processing across modules. It prompts a large language model (LLM) to summarize insights, infer feature dependencies, reason over trends, feature dependencies, predict adoption likelihood, sentiment clustering, and identify temporal opportunity windows. The orchestrator also contributes to the estimation of weights (α, β, γ, λ₁, λ₂, λ₃) used in impact and prioritization scoring through prompt-based guidance or fine-tuning over past outcomes. It ensures semantic coherence across the system by aligning analytical steps with strategic business goals.
In yet another embodiment of the invention, the roadmap prioritization engine (8) enables roadmap refinement, where it ranks roadmap items or feature based on a composite prioritization score. This score integrates ROI, similarity to historically successful features, market urgency, and dependency resolution. The engine supports configurable weight vectors and policy constraints to align the ranked roadmap with product and engineering objectives. It enables tagging features with actionable labels such as “high priority”, “experimental”, or “defer”, and provides export compatibility with third-party project management tools like JIRA, Asana, or Roadmunk.
In a preferred embodiment of the invention, the method for refining a product roadmap using post-deployment metrics including the steps as follows:
Step 1: Continuous telemetry collection:
 The system enables collection of runtime metrics post-deployment wherein using a post-deployment monitoring module (3); wherein the metrics include usage analytics, performance logs, telemetry, clickstream data, error incidence, and session behaviours.
 The post-deployment module aggregates and normalizes these metrics on a per-feature basis to derive feature engagement and stability indices.
Step 2: Customer relationship management (CRM) and revenue mapping:
 The system enables business impact data ingestion by interfacing with business systems such as CRM and sales platforms using a business data ingestion engine (4).
 The structured data including revenue attribution, pipeline advancement, churn risk, and customer interaction logs is extracted.
 The product features are correlated to business outcome using custom mappings and historical data.
Step 3: Market intelligence extraction:
 The system scrapes third-party platforms including forums, review websites, social media, and analyst reports using a market signal aggregator (5).
 The aggregator then performs Natural Language Processing (NLP) to extract sentiment, recurring themes, product mentions, and user feedback.
 The aggregator then clusters this market data by feature or use-case to detect external interest and urgency indicators.
Step 4: LLM orchestrated analysis:
 The LLM orchestrator (6) is the driving orchestrator that prompts a large language model (LLM) to summarize insights, infer feature dependencies, reason over trends, feature dependencies, predict adoption likelihood, sentiment clustering, and identify temporal opportunity windows.
 The orchestrator (6) enables a feature impact index computation for each deployed feature fᵢ, where the orchestrator computes a feature impact index (FII) using the formula:  FII(fᵢ) = α · norm(Uᵢ) + β · norm(Bᵢ) + γ · norm(Mᵢ)
  where:
  - Uᵢ is the usage vector
  - Bᵢ is the business impact metric
  - Mᵢ is the market signal score
and the weights α, β, and γ are either learned via gradient descent or inferred by the LLM-orchestrator.
 The LLM-orchestrator (6) is configured to:
predict ExpectedRevenue using historical analogs and trend reasoning;
estimate ExpectedCost including engineering effort, infrastructure requirements, and rollout cost; and
quantify TechnicalDebtImpact via codebase analysis and dependency mapping.

Step 5: ROI estimation for candidate features:
 For each undeployed (candidate) feature cⱼ, the feature ROI estimation engine (7) estimate ROI using the formula:
ROI (cⱼ) = ExpectedRevenue / (ExpectedCost + TechnicalDebtImpact)
Step 6: Prioritization score computation:
 The LLM orchestrator (6) is configured to compute a composite prioritization score for each candidate feature using a formula:
  Score(cⱼ) = λ₁ · ROI(cⱼ) + λ₂ · SimilarityToSuccessfulFeatures + λ₃ · Urgency;
to determine SimilarityToSuccessfulFeatures using embedding-based comparisons of candidate features to past successes, and to compute urgency based on market sentiment clusters, support pain points, or time-sensitive opportunities as inferred by the LLM.
Step 7: Roadmap Generation and Tagging:
 The roadmap prioritization engine (8) ranks all candidate features based on the computed prioritization scores.
 The prioritization engine (8) then assigns status labels to each feature such as “high priority”, “experimental” or “defer”.
 Finally, the engine (8) exports the finalized roadmap to external project management tools such as JIRA, Asana, or Roadmunk, using at least one output unit (9).
Advantages:
In yet another embodiment, the system and method of the present invention provides a comprehensive, data-driven approach to product feature prioritization by combining post-deployment telemetry, business performance data, and market signals. It enables objective decision-making through a hybrid method that evaluates the return on investment (ROI) for each feature, considering both historical impact and predicted future value. By incorporating insights from CRM systems, user behavior analytics, and external sentiment sources, the system ensures that product planning is aligned with real-world usage patterns and evolving market demands. The use of a Large Language Model (LLM) as an orchestrator adds contextual reasoning, allowing for more intelligent handling of feature dependencies, urgency, and opportunity timing. Additionally, the system proactively accounts for technical debt in its ROI estimates, helping teams make sustainable choices. With automated workflows and seamless export to tools like JIRA or Asana, the system streamlines the end-to-end prioritization process, resulting in faster, more strategic, and market-responsive roadmap development. Furthermore, the system may be used in SaaS product teams optimizing feature delivery, growth-stage startups aligning R&D with revenue, B2B platforms needing post-sales intelligence or any roadmap planning tied to adoption analytics.
While considerable emphasis has been placed herein on the specific elements of the preferred embodiment, it is to be noted that the system and method described herein could be standard commercial products or homegrown as many organizations use custom apps for revenue and booking and are specific in most of the cases. Further, it will be appreciated that many alterations can be made and that many modifications can be made in preferred embodiment without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.
, Claims:CLAIMS:
We claim,
1. A system and method for refining a product roadmap using post-deployment metrics;
wherein the system (10) comprises of at least one input unit (1), a processing unit (2) further comprising a post deployment monitoring module (3), a business data ingestion engine (4), a market signal aggregator (5), a feature ROI estimation engine (6), a LLM- orchestrator (7), a roadmap prioritization engine (8), and at least one output unit (9); such that the modules work in co-ordination to employ a stepwise method;

characterised in that:
the method for refining a product roadmap includes the steps of;
enabling telemetry collection; comprising
- collecting runtime metrics post-deployment using a post-deployment monitoring module (3);
- aggregating and normalizing these metrics by the post-deployment module on a per-feature basis to derive feature engagement and stability indices;
enabling customer relationship management (CRM) and revenue mapping; comprising
- interfacing business impact data ingestion with business systems such as CRM and sales platforms using a business data ingestion engine (4),
- extracting structured data including revenue attribution, pipeline advancement, churn risk, and customer interaction logs,
- correlating product features to business outcome using custom mappings and historical data;
enabling market intelligence extraction; comprising
 scraping third-party platforms including forums, review websites, social media, and analyst reports using a market signal aggregator (5),
 performing Natural Language Processing (NLP) by aggregator (5) to extract sentiment, recurring themes, product mentions, and user feedback,
 clustering the market data by feature or use-case using the aggregator (5) to detect external interest and urgency indicators;
enabling LLM orchestrated analysis; comprising
 prompting a large language model (LLM) by the LLM orchestrator (6) to summarize insights, infer feature dependencies, reason over trends, feature dependencies, predict adoption likelihood, sentiment clustering, and identify temporal opportunity windows.
 enabling a feature impact index computation for each deployed feature fᵢ, where the orchestrator computes a feature impact index (FII)
 configuring the LLM-orchestrator (6) to predict ExpectedRevenue using historical analogs and trend reasoning, estimate ExpectedCost including engineering effort, infrastructure requirements, and rollout cost, and quantify TechnicalDebtImpact via codebase analysis and dependency mapping;
enabling ROI estimation for candidate features; for each undeployed feature cⱼ by the feature ROI estimation engine (7) using the formula:
ROI (cⱼ) = ExpectedRevenue / (ExpectedCost + TechnicalDebtImpact)
enabling prioritization score computation; comprising
 computing a composite prioritization score for each candidate feature by the LLM orchestrator (6) using a formula:
  Score(cⱼ) = λ₁ · ROI(cⱼ) + λ₂ · SimilarityToSuccessfulFeatures + λ₃ · Urgency,
to determine SimilarityToSuccessfulFeatures using embedding-based comparisons of candidate features to past successes, and to compute urgency based on market sentiment clusters, support pain points, or time-sensitive opportunities as inferred by the LLM.
enabling roadmap generation and tagging; comprising
 ranking all candidate features by the roadmap prioritization engine (8) based on the computed prioritization scores,
 assigns status labels to each feature such as “high priority”, “experimental” or “defer” by the prioritization engine (8),
 exporting the finalized roadmap by the engine (8) to external project management tools such as JIRA, Asana, or Roadmunk, using at least one output unit (9).

2. The system and method as claimed in claim 1, wherein the post-deployment monitoring module (3) is a datasource configured to collect and analyze runtime operational data corresponding to deployed features; including feature usage analytics, performance logs, telemetry, user behaviour or interaction patterns such as feature engagement rate, session lengths or error incidence, and enables continuous tracking and aggregating the collected data on feature basis to generate measures such as engagement rates, error incidence, and session lengths.

3. The system and method as claimed in claim 1, wherein the business data ingestion engine (4) configured to interfacing with external deterministic business systems, such as CRM platforms to extract structured business metrics, thereby mapping feature usage to downstream business outcomes, including revenue contribution, pipeline movement, customer churn, custom fields, success logs, or support tickets.

4. The system and method as claimed in claim 1; wherein the market signal aggregator module (5) collects unstructured qualitative and quantitative data from external market-facing sources such as online forums, product reviews, social media sentiment, and analyst opinions; and uses Natural Language Processing (NLP) techniques to extract relevant insights including sentiment polarity, feature-specific feedback, adoption trends, and recurring themes.

5. The system and method as claimed in claim 1, wherein the feature return on investment (ROI) estimation engine (6) computes the expected return on investment (ROI) for candidate features that are under consideration for inclusion in the product roadmap where is computed using expected revenue, expected cost and technical debt impact to estimate the ROI; where the expected revenue is derived from reasoning over similar historical features and market trend, expected cost includes engineering effort estimations, infra cost, training, rollout, and technical debt impact is computed by codebase-level impact analysis.

6. The system and method as claimed in claim 1, wherein the LLM-orchestrator (7) functions as a central reasoning engine that coordinates multi-modal data processing across modules, that prompts a large language model (LLM) to summarize insights, infer feature dependencies, reason over trends, feature dependencies, predict adoption likelihood, sentiment clustering, and identify temporal opportunity windows; and contributes to the estimation of weights (α, β, γ, λ₁, λ₂, λ₃) used in impact and prioritization scoring through prompt-based guidance or fine-tuning over past outcomes.

7. The system and method as claimed in claim 1, wherein the feature impact index (FII) for each deployed feature fᵢ is computed by the orchestrator (5) using the formula:  
FII(fᵢ) = α · norm(Uᵢ) + β · norm(Bᵢ) + γ · norm(Mᵢ)
  where:
  - Uᵢ is the usage vector
  - Bᵢ is the business impact metric
  - Mᵢ is the market signal score
and the weights α, β, and γ are either learned via gradient descent or inferred by the LLM-orchestrator (5).

8. The system and method as claimed in claim 1, wherein the roadmap prioritization engine (8) enables roadmap refinement by ranking roadmap items based on a composite prioritization score that integrates ROI, similarity to historically successful features, market urgency, and dependency resolution; thereby supporting configurable weight vectors and policy constraints to align the ranked roadmap with product and engineering objectives.
Dated this 11th day of July, 2025.