Case Study

Case Study Post Type

Excavator
Industrial

Machine Learning Model to Predict Likelihood of Mineral

Client Background: Client is a multinational metals and mining corporation, producing iron ore, copper, diamonds, gold and uranium Business Objective: In order to meet current or future demand and explore new business opportunities, client makes efforts to find new mineral sites Client wants to process remote sensing image data to identify new mineral occurrence locations (of commercial interest) Solution  We developed a Machine Learning model to identify new mineral occurrence locations by scoring sites on likelihood (using band ratios and other such combinations of band reflectance values) Key steps involved: Re-factor code to python and access AWS processing infrastructure Test and validate re-factored python code in current process Information extraction on RS scenes to create prediction variables Use the information gained from the above analysis to create Machine Learning model Validate Machine Learning model against hold out data Score incoming RS scene data to identify new potential mineral sites Outcome Our solution achieved an accuracy of 95% on round 1 of Images.

Happy young Asian businessmen and businesswomen meeting brainsto
Financial Services

Assessing The Effectiveness Of Communication Methods and Content Through A/B Testing

Client Background Client is a mobile app based FinTech organization in the Indian market, operating in the space of personal loans, housing loans, health insurance, and mutual funds. Its aim is to make personal finance simple and accessible to a wider set of audience. Business Objective Client is a start-up company in their growing phase. As part of the journey, the client was working on figuring out different things that work best in the market and kept experimenting to acquire this learning. We worked with the Growth and Marketing teams, and the business objective was; to identify how efficient are different modes of communication (SMS/ Push Notifications/ Emails/ Performance Marketing) and what type of messaging content helps in higher customer engagement. Solution We leveraged data from different customer engagement and communications platforms like MoEngage, AppsFlyer, Kaleyra and Gupshup; which were being used by client The, we performed series for A/B tests to assess the performance of different modes of communications and different types of messaging contents The user base was divided into different cohorts with an uniform distribution across the cohorts based on user attributes (demographic and bureau info) Different success metrics were defined, to track the effectiveness of these campaigns, viz. user reachability, user engagement (views/clicks) and user conversions (from on-boarding to conversion journey) User engagement via clicks was studied using AppsFlyer data, which helped in tracking the engagement across different deep links that were incorporated into these campaigns Defined the attribution time window to link any success event to a particular event, by looking at the data distribution and studying industry standards Built a dashboard on Superset (which was later migrated to Tableau), to help the different stakeholders keep a track of the performance of these different campaign experiments over a period time Outcome These activities helped the clients to identify the appropriate communication channels and right set of content that worked best for different set of customers. This helped clients in enhancing their marketing strategy and channelizing their budget accordingly. Success metrics were defined in terms of proportion of users who engaged on the platform after receiving communication and then till what stage of the product journey did they proceed. And eventually how many of them ended up getting converted to paid customers.

Marketing target audience concept
Financial Services

Creating An Automated System To Monitor The Effectiveness Of Customer Engagement Initiatives

Client Background Client is a mobile app based FinTech organization in the Indian market, operating in the space of personal loans, housing loans, health insurance, and mutual funds. Its aim is to make personal finance simple and accessible to a wider set of audience.  Business Objective Client had setup new business unit in the housing finance sector. As part of expanding into this sector, client was planning to start growth activities to drive business. They also wanted to keep a track of the referral programs. which they were planning to execute, so that they could reward the winners accordingly. Another objective was, to enable the growth team to be able to enter the new campaign details into a Google sheet, which should start reflecting on the dashboard, avoiding regular intervention of analytics/BI team. Solution We leveraged data from different customer engagement and communications platforms like MoEngage, AppsFlyer, Kaleyra and Gupshup; which were being used by client A Superset dashboard was setup for stakeholders to track the progress of newly on-boarded customers via different the different steps of a housing loan journey A functionality was built for the Growth team to enter the campaign (that they set up using MoEngage) details into a Google sheet. Using Python scripts, this Google sheet was set to be imported into the database on Athena data lake on a daily basis Based on the details entered into Google sheets, performance metrics for new campaigns started showing up on the dashboard Response data of the referral program forms was also integrated into the database. In case any new customer who was referred by someone would make any progress into the product journey, it would show up on the dashboard. And in case of a successful conversion of a referred customer, the details of referrer were showcased on other summary dashboards Outcome These dashboards were used by lending business head and housing loans business teams to track the impact/success of their business initiatives It was also used by the business stakeholders to reward their customer correctly and on time., for those whose referrals were getting converted into client’s paid customers This also helped in reducing the Growth team’s daily dependency on analytics/BI team to incorporate all new campaigns in the summary dashboards

Man-Animal-Coflict-Case-Study-Image
Public Sector

Guardians of Harmony: The Virtual Wall Solution for Human-Wildlife Coexistence

Client Background: Sitarampeth Village, nestled in the buffer area of Tadoba Andhari Tiger Reserve (TATR), stands as a testament to the delicate balance between human habitation and wildlife. The region, known for its rich biodiversity, has faced challenges with human-animal conflicts, particularly incidents of tiger attacks on locals. With the village perched on the edge of the reserve, the need for a robust solution to mitigate these conflicts became imperative. Business Objective:  The primary goal was to implement an effective human-animal conflict mitigation system that not only alerts authorities to animal movements near human settlements but also enhances the safety of both wildlife and the local community. The challenges included instances of tiger attacks on residents and the potential danger of encountering wildlife, especially tigers, during nightly activities. Our Solution:  Virtual Wall – A Revolution in Conflict Mitigation: Our team developed the Virtual Wall, a revolutionary human-animal conflict mitigation system tailored for the specific needs of Sitarampeth Village. This system encompasses a sophisticated installation setup, featuring lights, hooters, cameras, and a comprehensive power and connectivity panel. Placed strategically at the periphery of villages, the cameras utilize AI image detection to identify the movements of various wildlife species, including tigers, leopards, bears, and elephants. Our Installation set and collaboration with the forest officials and villagers: The AI algorithms, boasting an impressive near-zero error rate, proficiently identify animals even in low-light or dark conditions. When an animal is detected, alerts and notifications are swiftly sent to relevant authorities, enabling immediate intervention. This real-time response minimizes the risk of potential mishaps and ensures the safety of both the community and wildlife. Here are some of the animal photographs taken from our cameras Outcome: The system goes beyond mere detection; it facilitates meticulous tracking and monitoring of individual tigers. By identifying specific behavioral shifts, such as the breaking of canine teeth or the onset of aging, the system predicts when a tiger might become more prone to hunting humans or livestock (such as cows and sheep). This proactive approach enables timely measures for the safety of both wildlife and communities. Since the implementation of the Virtual Wall system, the positive impact has been significant. Over 175  triggers have been sent in the past 7 months to authorities, preventing animal attacks and safeguarding the local population and livestock. Notably, there have been zero casualties reported among residents or animals. The system’s efficiency is highlighted by its quick detection time, taking only 3 seconds for the camera to identify an event, such as an animal approaching the designated area. This rapid response time is crucial in preventing potential conflicts. The predictive capabilities of our solution have proven invaluable in understanding conflict patterns and wildlife movements. In a nearby village, where human-wildlife conflicts were once a prevalent concern, approximately 10 individuals, including livestock, fell victim to tiger attacks However, post the implementation of Virtual wall, no such incidents have been reported, underscoring the system’s efficacy in fostering peaceful coexistence between humans and wildlife. The Virtual Wall has emerged as a beacon of hope for Sitarampeth Village, showcasing the potential of technology in mitigating human-animal conflicts. By seamlessly integrating AI and comprehensive monitoring systems, we have not only saved lives but also contributed to the harmonious coexistence of the local community and the magnificent wildlife in the Tadoba Andhari Tiger Reserve. The success of this project serves as a model for other regions grappling with similar challenges, offering a scalable and effective solution for human-wildlife conflict mitigation. The implementation has been well received by the locals and authorities alike. Check out our media coverage here

Fiber-Prediction-Parameters
Industrial

Fibre Quality Parameters Prediction

About the Client The client is a leading global producer of viscose staple fibers (VSF) and viscose filament yarn (VFY), holding significant market shares in both global and domestic markets. They are renowned for their pioneering efforts in fiber production, with several plants across India and Southeast Asia utilizing their solutions. Problem Statement The fiber bale, composed of hardwood and softwood, is evaluated on 8 quality parameters,with key focus areas including Oil pick-up, Whiteness, Splinter and moisture. Each of these parameters is critical for fiber quality. The current manual method of evaluating these parameters is time-consuming and often leads to delays and inaccuracies. The client needed a real-time monitoring system to predict quality parameters every 10 minutes to give recommendations to achieve the target quality parameters. Solution We developed a comprehensive solution featuring data-driven soft sensors that use real-time instrumentation data to predict quality parameters every 10 minutes. Key components of the solution include: Real-Time Data Integration and Analytics Pipeline: Implemented a pipeline to gather and analyze data in real-time, ensuring that predictions for quality parameters are available every 10 minutes. Soft-Sensor Health Index Monitoring: Developed soft-sensor health index monitoring to identify data or process anomalies, ensuring the accuracy and reliability of the predictions. Scalable Deployment: Scalable deployment across multiple plants and production lines to ensure consistency and efficiency across the client’s operations. Real-Time Quality Parameter Predictions: Utilized predictive analytics to provide real-time quality parameter predictions, enabling prompt operational adjustments and recommendations. Sensitivity Profiler: Developed the sensitivity profiler to show how each input sensor affects the quality parameter. By isolating one sensor at a time and plotting its impact on the output, the plant team could observe how changes in that sensor affected quality. This helped us validate the model and build confidence in its accuracy. Recommendations to achieve target quality parameters: For any quality parameter, there were controllable and non-controllable factors. For example, controllable factors for the Splinter quality parameter included Ripening Age, Spin Bath Acid, etc. If the quality parameter went out of range, we set the desired limits for these controllable factors and the target quality value. Using optimization techniques, we then received recommendations for adjusting the controllable factors. Tech Stack Deployment: AWS EC2 server Sensor data Storage: ASPEN Historian UI: Streamlit Backend and APIs: FastAPI, Aspen IP 21 Database: InfluxDB Dashboard: Grafana Machine Learning model management: MlFlow Data Science Environment: Python, Tensorflow, etc Notification: Email, Whatsapp Outcome Reduced Prediction Intervals: Reduced prediction intervals from 4 hours to every 10 minutes, allowing for more frequent monitoring and timely adjustments. Increased Accuracy in Predictions: Achieved greater than 95% accuracy in prediction models, significantly improving the reliability of quality assessments. Enhanced Quality Management: Improved quality management by providing timely insights, maintaining desired quality ranges, and enhancing overall operational efficiency. This case study highlights our commitment to leveraging advanced analytics and real-time monitoring to drive operational excellence and quality assurance in fiber production. The successful implementation underscores our expertise in delivering scalable, data-driven solutions that meet the complex needs of our clients.

enhancing public engagement
Public Sector

Enhancing Public Engagement Through GenAI-Powered Multilingual Access

Client Background The official mobile application of a prominent national leader offers a dynamic platform for enhanced government-citizen interaction. This app stands out by delivering real-time updates, facilitating direct communication, and hosting interactive forums. Among its notable features are regular news updates, personalized messages from the national leader, and the introduction of a Public Survey, aimed at collecting public opinions to shape policy decisions. Available on major mobile operating systems (android and IoS), the app boasts over 10 million downloads and serves as a vital channel for knowledge dissemination, without necessitating user registration. Business Objective The primary goal was to broaden the scope and accessibility of the survey by attracting a wider segment of the population. This was to be achieved by integrating Gen AI-powered multilingual text and voice support, thereby making the survey more comprehensive and inclusive. Solution The enhancement focused on multilingual support for the survey, initially covering four major languages: Hindi, English, Marathi, and Gujarati. This approach encompassed several key features: Features Language selection: Users can choose their preferred language for the survey interface and responses. Multilingual support: Text-to-voice: Questions are read aloud in the chosen language. Voice-to-text: Users can respond using voice commands in their selected language. User-friendly interface: Intuitive design promotes a smooth user experience AI-powered input handling: An advanced AI engine interprets user input accurately and contextually, regardless of the language used A robust data storage solution captures and stores user responses, query logs, and chat history for analysis and improvement. Flow The enhancement focused on multilingual support for the survey, initially covering four major languages: Hindi, English, Marathi, and Gujarati. This approach encompassed several key features: User selects their preferred language. The survey questionnaire is displayed in the chosen language, with the option for text-to-voice reading. Users respond to the survey, either through text input or voice commands in their chosen language. Survey results are captured and stored in the database for analysis. This solution aims to enhance the reach and inclusivity of the Jan Man Survey by offering multilingual support powered by Gen AI technology.     Outcome The integration of GenAI-powered multilingual support in the Jan Man Survey resulted in a significant increase in participation from diverse regions across India. Key findings: Increased participation: The number of survey responses increased compared to the previous survey conducted solely in Hindi and English. Enhanced inclusivity: Citizens from regions with languages like Marathi and Gujarati were able to participate more actively, leading to a more comprehensive picture of public opinion across the country Improved user experience: The user-friendly interface and voice-based options offered a more accessible and convenient way for individuals to engage with the survey. Valuable insights: Analysis of captured data, including query logs and chat history, provided valuable insights into public sentiment and areas for further improvement in future surveys. Overall, the implementation of multilingual support demonstrated the potential of Generative AI to enhance the comprehensiveness and inclusivity of public engagement initiatives.

bridging information
Public Sector

A Leap Forward In Governance: Bridging Information Gaps with GenAI

Client Background Our client, established in 2015, is the premier public policy think tank of a national government, with the goal of fostering economic growth and social progress. Led by the nation’s Prime Minister, the organization is pivotal in shaping transformative national development strategies. It offers technical guidance across governmental levels and promotes synergistic efforts among federal, state, and local entities, positioning itself as the cornerstone of the country’s policy-making and development initiatives. Business Objective The project aimed to develop and deploy an extensive knowledge infrastructure for the client’s initiative targeting central, state, and local government layers. The primary goal was to fortify government stakeholders at different echelons by providing them with accessible and actionable insights in pivotal sectors, notably Skilling and Agriculture. This initiative was designed to cultivate a centralized knowledge hub that would facilitate state, district, and local government officials with Gen AI-enhanced tools, offering immediate access to information and best practices in these critical areas, thereby enabling informed decision-making and fostering impactful regional developments. Solution The core challenge was to empower government officials nationwide with readily available knowledge to aid in informed decision-making, specifically in the vital sectors of skills development and agriculture. Our solution entailed the creation of the GenAI Help Desk, a chatbot-driven web application, which serves as a central repository for pertinent information. Features Targeted Sectors: Focuses on Skills and Agriculture, providing tailored information.   Comprehensive Content: Includes over 3,000 documents on schemes, policies, and best practices, emphasizing quick access and clarity. User-Friendly Design: Customized UI/UX for efficient navigation and ease of use. Multilingual Support: Offers content in 11 Indian languages using Bhashini APIs, enhancing accessibility.   Features Interactive Experience: Supports both voice and text interactions for user convenience. Continuous Enhancement: Regular updates based on user feedback ensure the solution keeps improving. Benefits Knowledge Empowerment: Provides officials instantaneous access to essential information, significantly reducing dependency on conventional search methods. Informed Decision-Making: The facility to access detailed schemes, policies, programs, and best practices enables stakeholders to make well-informed choices in policy and administration, leading to more effective sector-specific strategies. Operational Efficiency: Centralizes information access, eliminating the need for extensive searches and thereby increasing overall productivity. Outcome The GenAI Help Desk initiative has notably succeeded in: Elevating Knowledge Access: Officials at various administrative levels now enjoy round-the-clock access to critical information in skilling and agriculture, streamlining the information retrieval process and conserving significant time. Refining Decision Processes: The platform’s ability to provide immediate access to detailed and relevant information supports stakeholders in making enlightened decisions, thereby enhancing the quality of policy formulation and administration. Boosting Efficiency:By centralizing resource access, the platform reduces the need for dispersed searches, thereby improving efficiency. Expanding Linguistic Reach: With support for 11 languages, the initiative ensures inclusivity, catering to a broad spectrum of officials across the nation and bridging potential language barriers. Ongoing Platform Evolution: Continuous feedback-driven refinements ensure that the platform remains responsive to the changing needs of its users, maintaining its relevance and effectiveness in facilitating government operations.

GenAI in Agriculture
Public Sector

GenAI in Agriculture: Empowering Indian Farmers Against Modern Challenges

Client Background Our esteemed client operates at the helm of India’s agricultural sector, with a mission that extends to safeguarding food security, enhancing farmer welfare, and advocating for sustainable agricultural practices. Faced with challenges such as fragmented landholdings, the unpredictability of climate change, market inefficiencies, and the digital divide in technology adoption, the organization is at the forefront of transforming the agricultural landscape. Business Objective To empower Indian farmers, this project seeks to address four key challenges: the inability to access real-time crop prices in mandis, unpredictable weather impacting planning, a gap in receiving crucial updates, and language barriers hindering resource access. By providing real-time data, localized weather forecasts, a centralized information platform, and multilingual support, the project aims to bridge these gaps and empower farmers to make informed decisions and improve their agricultural practices. Solution Leveraging the power of Generative AI, the Kisan Sahayak App stands as a testament to our commitment to integrating advanced technology for the betterment of the farming community Features Real-time market data: Leverage reliable and comprehensive market data sources to deliver up-to-date pricing information to farmers directly through the app. Personalized price suggestions: Utilize AI-powered insights to suggest optimal prices for crops based on the farmer’s location and historical data.     Hyperlocal weather forecasting: Offer accurate and location-specific weather forecasts through AI and weather data integration. Interactive chatbot: Implement a conversational AI chatbot to answer farmer queries in multiple languages (Hindi, English, Marathi, Kannada, Malayalam, Gujarati, Telugu, Tamil, and Bengali) through voice or text. Multilingual support: Ensure inclusivity by providing the entire app interface and chatbot support in various languages, removing language barriers   Impact and Future Prospects While the full impact of this initiative is yet to be quantified, early indicators suggest a transformative effect on the agricultural sector: Information Accessibility: The initiative aims to significantly increase farmers’ access to vital information, enhancing their ability to make informed decisions. Confidence in Decision-Making: Preliminary feedback points to a noticeable improvement in farmers’ confidence levels concerning crop pricing and agricultural strategies. Inclusive Engagement: The multilingual capabilities of the platform have broadened its reach, engaging a wider spectrum of the farming community previously hindered by language barrier. Operational Efficiency: The application of real-time data and AI-generated insights is expected to drive operational efficiencies and yield improvements, contributing to the overall prosperity of the agricultural sector.

Optimizing-Scrap-Utilization-In-Aluminium-Production-1
Industrial

Optimizing Scrap Utilization in Aluminium Production

Client Background The client is a major player in the aluminium industry, specifically focusing on the production of flat rolled products. The plant in question is a critical component of their downstream operations, producing a diverse range of aluminium alloys. This facility consists of two primary units: the Recycle unit, which processes scrap aluminium, and the Remelt unit, where the recycled alloys are mixed with primary metals for further production. Business Problem Operators in the Remelt unit primarily recycle the same scrap alloy, resulting in the underutilization of certain alloy types. This practice arises from a reluctance to experiment with multiple scrap alloy combinations due to the risk of improper element quantities in casting, which could lead to increased inventory and operational inefficiencies. The goal was to optimize the usage of various scrap alloys to reduce waste and inventory costs while maintaining the quality of the final products. Solution To address this problem, we implemented a comprehensive Scrap Optimization project with the following key steps: Data Collection: Collected detailed data on all products manufactured over the last three months, focusing on the alloy composition of the scrap collected. The data included concentrations of crucial elements such as Copper (Cu), Iron (Fe), Silicon (Si), Titanium (Ti), Magnesium (Mg), Manganese (Mn), Zinc (Zn), Lead (Pb), and Aluminium (Al). Data Manipulation and Conversion: Converted the collected data into averages by categorizing the element concentrations into specific ranges. For example, Copper concentrations were categorized into ranges such as 0.5-1.0%, 1.1-1.5%, etc., based on the observed distribution of values. The most frequently occurring range for each element was then determined, and the median value within this range was selected as the representative concentration. Optimization Algorithm: Developed an optimization algorithm using Pyomo, a Python-based optimization modeling language. This algorithm was designed to determine the optimal combination of scrap and primary metals to use, ensuring that the element concentrations in the final alloy met the required specifications. Execution: Utilized Streamlit, a web-based application framework, to create an intuitive user interface. This interface enabled operators in the Recycle and Remelt units to input data and receive optimized recommendations for scrap and primary metal usage, facilitating better decision-making and improved alloy production. Outcome The implementation of the Scrap Optimization project yielded significant benefits: Improvement in Scrap Usage: Enhanced the utilization of scrap metals by incorporating a wider range of alloy types into the remelt process. This not only reduced the dependency on specific scrap alloys but also improved the overall efficiency of the recycling process. Cost Savings: Achieved substantial cost savings by optimizing scrap usage. The project resulted in an estimated years savings of nearly $100k, primarily through reduced inventory costs and more efficient use of available resources. By systematically addressing the challenge of underutilized scrap alloys and leveraging advanced data analysis and optimization techniques, the client was able to improve operational efficiency and achieve significant cost reductions. This project underscores the potential for data-driven optimization in industrial processes, particularly in the aluminium industry, where precision and resource management are critical. The successful implementation of this solution highlights the importance of innovative approaches in enhancing productivity and sustainability in manufacturing operations.

harnessing genAI
Others

Harnessing GenAI for Smarter Lead Qualification in the Real Estate Sector

Client Background Established in 2016, our client has rapidly emerged as a notable entity within the real estate sector, showcasing a diversified portfolio that spans commercial and residential properties across key Indian metropolises including Mumbai, Bangalore, and the National Capital Region (NCR). Leveraging the substantial land holdings of its parent conglomerate, the organization is poised for significant growth through strategic developments and collaborative ventures in major cities. Business Objective The client’s sales division is faced with the critical task of enhancing the efficiency and effectiveness of their pre-sales activities. A primary challenge lies in the accurate categorization of pre-sales calls into hot, warm, or cold leads, which is essential for prioritizing follow-up actions and optimizing sales strategies. Furthermore, there is a pressing need for a comprehensive analysis of call data to glean insights on sales interactions, including the assessment of the sales team’s communication skills and the identification of areas for improvement in sales pitches. Currently, the classification of leads relies solely on the subjective judgment of individual salespersons immediately following each call, which may not consistently reflect the true potential of the leads. The organization is seeking innovative solutions to systematize the lead categorization process, enhance the quality of customer interactions, and ultimately, refine their sales approach for better conversion rates. Scenario Planning   Phases in propensity modeling   Solution Phase 1: Integration with Customer Relationship Management (CRM) System The initial step involved the integration of the client’s call center audio recordings, stored on Amazon Web Services (AWS) S3 storage, with the CRM system. To facilitate this, a dedicated S3 bucket was established specifically for the Propensity Modeling project, ensuring a streamlined process for data handling and analysis. Phase 2: Audio Call Transcription Utilizing the computational power of an NVIDIA DGX A100 VM, the project team employed Python scripts to download and preprocess the audio call recordings. The preprocessing included several critical steps to ensure data quality and relevance: Data Sanity Checks: Brief calls, lasting less than a minute, were excluded from analysis to focus on more substantive interactions Voice Activity Detection (VAD): A sophisticated deep learning model was applied to filter out irrelevant audio segments, such as ring tones and filler words, enhancing the clarity and focus of the data Speech to Text (STT) Conversion: The refined audio data was then processed through OpenAI’s Whisper large-v3 model for speech-to-text conversion. This model was selected for its superior performance across multiple languages, including English, Hindi, Kannada, and Marathi, among others, making it well-suited for the diverse linguistic landscape of the target market. Phase 3: Analyzing Conversion Propensity The transcripts generated from the STT process were analyzed using Azure’s OpenAI Language Learning Models (LLMs) through an API. This phase involved extensive testing with various prompts to determine the most effective approach for predicting the likelihood of lead conversion. The finalized prompt, coupled with continuous feedback mechanisms involving business insights, enabled the dynamic refinement of the GPT model for improved accuracy and relevance. Value Proposition This innovative solution leverages the cutting-edge capabilities of OpenAI’s Language Learning Models to transform raw audio calls into insightful dialogues between salespersons and potential customers. By categorizing these interactions into hot, warm, or cold leads, complete with scores and reasons for categorization, the system offers a robust framework for prioritizing sales efforts and enhancing conversion rates. The use of Microsoft Azure’s OpenAI Cognitive Studio further enriches this process by providing a versatile platform for experimenting with different language models, including the latest GPT 3.5 Turbo, ensuring scalability and cost-effectiveness tailored to the project’s specific needs. Outcome Achieved human-equivalent accuracy on the training dataset. Successfully processed calls with audio quality that challenges human comprehension. Preliminary evaluation of 100 calls showed very promising results Plans for a production phase include: Implementing a feedback system for continuous model optimization. Experimenting with multiple models to enhance performance. Developing a business dashboard for real-time metrics and insights.

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