Featured Case Studies
1. Running AI Workloads in Production – Cost Saving MethodsÂ
Cost Efficiency is critical for managing AI workloads effectively. One effective strategy is to use smaller, task-specific models instead of large, generalized ones. Techniques like model distillation, which trains compact models to mimic larger ones, can significantly reduce computational demands while preserving performance. Leveraging open-source models and fine-tuning them for specific tasks further cuts costs by avoiding licensing fees associated with proprietary models.
In addition, Parameter Efficient Fine-Tuning (PEFT) methods such as Low-Rank Adaptation (LoRA) minimize the need for full model retraining by adjusting only a subset of parameters, reducing both computational and operational expenses. Quantization, which reduces model weight precision, and optimizing AI cloud infrastructure through discounted or spot GPU instances also contribute to cost savings. Autoscaling and efficient data storage, including the use of indexed vector databases and caching mechanisms, help manage resources dynamically and minimize waste. By integrating these strategies, organizations can achieve high-performance AI solutions while keeping costs manageable.
2. ATX AI Agent Infrastructure – Powered By Kompliance AI
ATX Labs introduces Kompliance AI, a cutting-edge solution that brings advanced AI and GenAI capabilities to the forefront of enterprise technology, emphasizing reliability, privacy, and cost efficiency. The Kompliance AI stack features three types of AI Agents designed to meet diverse business needs: AI Copilots, which enhance productivity through targeted digital assistance, AI Employees that integrate seamlessly into human workflows to automate tasks, and Autonomous Agents that operate independently to deliver outcome-based services. Notable examples include ChatFi for financial product support and MediaGPT for automated media content creation. Kompliance AI ensures enterprise readiness and effectiveness by combining data engineering, AI/ML algorithms, and user experience design. ATX Labs is committed to helping businesses achieve productivity, profitability, and personalization through these innovative AI solutions.
3. Making of MediaGPT – Poster Generation
Text-to-image synthesis has evolved significantly, now producing detailed, realistic humanoid and animal figures. However, generating readable text within images remains a significant challenge, as current models often struggle to maintain legibility due to the intricacies of text rendering. The difficulty is compounded by the limitations of stable diffusion, especially with smaller fonts.
Our innovative solution addresses this issue with a multi-modal approach tailored for poster generation. Unlike other services dependent on third-party APIs, our in-house technology ensures secure, reliable, and privacy-focused results. By fine-tuning our model specifically for text integration, we streamline the creation process and allow users to input various forms of guidance, from text to sketches. This approach not only reduces costs and time but also offers designers, marketers, and advertisers a flexible tool for rapid, high-quality design creation.
4. Risk Data Analyst – AI Copilot
Risk scoring assigns numerical values to actions to assess their potential risks, using quantified factors derived from historical data. Traditionally employed in areas like cybersecurity and credit lending, older models include binary logistic models for credit assessments and NIST scoring for cybersecurity.
Our approach enhances accuracy by focusing on sustainability risk through a tailored analysis of company-specific information. We categorize risks into three ESG (Environmental, Social, Governance) domains, each with detailed factors such as emissions and waste management. These factors are converted into quantitative data using statistical techniques like log normalization and categorical encoding. The data is then processed through a linear function to produce a preliminary score, which is transformed into a risk score on a 0-10 scale.
For more nuanced analysis, we apply machine learning to refine scoring functions and update them based on new insights. This allows us to adapt our models to reflect complex dynamics, such as increasing emissions intensity or varying penalties for different company types. By integrating these advanced techniques, we provide a more accurate and insightful risk assessment tailored to sustainability.
AI Podcasts
ATX Labs has launched “AI for Your Intelligent Industry Transformation (Season 1),” a podcast series that explores AI’s impact across four key industries: Banking, Manufacturing, Healthcare, and Media. Each six-minute episode features Steve in conversation with ALGO, ATX Labs’ advanced AI agent, providing quick and practical insights into how AI is revolutionizing these sectors. The fully automated series highlights ATX Labs’ capabilities in content creation, offering industry-specific knowledge while maintaining high data security through in-house production.
The podcast is designed for busy professionals, delivering actionable information in a concise format. It serves as a cost-effective tool for businesses to stay informed about AI advancements and their applications. Future seasons are planned to cover additional industries and topics, reflecting the ongoing evolution of AI. Episodes are available on Spotify, offering a glimpse into AI’s potential to drive industry transformation.
Media Coverage

Â
ONDC driving a two-fold strategy: Democratization of commerce and financial inclusion in India. BCG and GFF Report on Building Bridges for the Next Decade of Finance covered ATX Labs for Investment Platform powered by ONDC! here

More Macro Studies

Enterprise GenAI​
Kompliance AI’s Enterprise GenAI solution tackles AI adoption hurdles with explainable, secure, and cost-efficient AI models, leading to enhanced decision-making, efficiency, and reduced costs for enterprises.
Enterprise GenAI
Problem
Enterprises face challenges in adopting AI solutions due to concerns regarding explainability, security, and reliability. Additionally, the costs associated with implementing AI technologies can be prohibitive.Â
Solution
Kompliance AI offers an Enterprise GenAI solution that addresses these challenges. It provides a full-stack GenAI engine that is explainable, secure, and reliable. This solution ensures that AI models are transparent, can be trusted, and comply with regulatory requirements. Moreover, it offers cost-efficient implementation options, enabling enterprises to leverage AI technologies without breaking the bank.Â
Outcome
Enterprises implementing Kompliance AI’s Enterprise GenAI solution experience improved decision-making capabilities, enhanced efficiency, and reduced costs. With transparent and reliable AI models, they can gain valuable insights while ensuring compliance and security.Â

Sustainability Risk Score​
A Sustainability Risk Score, utilizing ESG metrics through ML, enhances investment decisions by providing nuanced insights beyond financial data, aiding companies in performance improvement.
Sustainability Risk Score
ProblemÂ
Knowledge of company sustainability aside from financial indicators has become crucial in determining both its long term and immediate prospects.Â
SolutionÂ
Development of a Sustainability Risk Score based upon accurate and legitimately sourced information on a company’s ESG (Environmental, Social and Governmental) metrics. An ML model is trained and tested on statistically analyzed and properly sampled data.Â
OutcomeÂ
The risk score shows marked similarity with existing scoring mechanisms on tail-ends of both scores while giving new insights on companies near the mean, allowing customers to make better decisions in regards to investment and companies to improve their overall performance

Support AI Assistant
Wealth Management Support AI Assistant streamlines mutual fund investment management through personalized, instant support via LLM chatbot, enhancing CX and simplifying product discovery.
Product Support Copilot
Problem
Investors in mutual funds face challenges in managing their wealth effectively due to the complexity of the financial markets and the vast array of available investment options. Additionally, staying informed about market trends, fund performance, and portfolio diversification can be overwhelming and time-consuming.Â
Solution
Wealth Management Copilot introduces a Support Copilot specifically tailored for mutual funds investors. This Copilot utilizes advanced algorithms and data analytics to provide personalized investment product related support through LLM chatbot. By leveraging cutting-edge fine-tuned LLM technologies, the Support Copilot simplifies wealth management discovery and product support processes , thereby improving the TAT.
Outcome
Investors leveraging Wealth Management Support Copilot for mutual funds experience enhanced Customer Experience (CX). With personalized support for existing or new investments, user gets support queries answered instantly over a chat interface. This Copilot can support recommendations, portfolio optimization strategies, and real-time market insights as next steps also.

InsightsGPT
By efficiently analyzing diverse data sources at different frequencies, InsightsGPT enables business to gain a comprehensive understanding of market, customers, and business operations. The insights provided empower business professionals to drive business growth.
InsightsGPT
Problem
Business struggle to efficiently process and analyze large volumes of diverse data from various sources at different frequencies, both textual and numerical. Traditional methods often lack scalability and fail to provide timely insights, hindering decision-making processes and impacting business performance.
Solution
InsightsGPT introduces an innovative LLM tailored for business goals. This GPT leverages advanced NLP techniques and ML algorithms to process vast amounts of textual and structured data from disparate sources, including market data feeds, financial reports and internal business data. By harnessing the power of technologies such as Langchain for NLP tasks and Neo4j for graph database, InsightsGPT enables institutions to extract actionable insights and uncover hidden patterns from complex datasets. Â
Outcome
By efficiently analyzing diverse data sources at different frequencies, InsightsGPT enables business to gain a comprehensive understanding of market, customers, and business operations. The insights provided empower business professionals to make informed decisions, optimize investment strategies, and drive business growth.

AI for Security
AI for Security utilizes generative AI to automate the creation of test cases for security testing, enhancing system resilience against cyber attacks by generating multiple scenarios tailored to specific testing requirements, thus minimizing business risks.​
AI for Security​
Problem
Security vulnerabilities related to Data and Network breaches such as Network attacks or SQL injection cause business downtime and huge cost implications.
Solution
Generate or augment automated test cases for testing security aspects of the system. In this, LLM agent framework is used to deploy AI agents that help testing the required security testing by generating code to do the network scan/attack such as DDoS and/or data breaches such as SQL injection code for given system.
OutcomeÂ
Since this is generative AI based testing having model trained on security domain, it creates multiple scenarios for ensuring system is foolproof from hacking or cyber attacks as per defined scope of test cases covering respective network and/or data breaches.

ML/LLM Ops
ML/LLM Ops pioneers a paradigm shift in model deployment through state-of-the-art automation and governance solutions, empowering organizations with unparalleled agility, reliability, and performance in navigating the dynamic landscapes of machine learning.
ML/LLM Ops​
Problem
Organizations face challenges in effectively managing and deploying machine learning (ML) and large language model (LLM) models in production environments. Traditional methods lack automation, scalability, and reproducibility, leading to inefficiencies and operational bottlenecks.Â
Solution
ML/LLM Ops introduces a comprehensive solution for managing the lifecycle of ML and LLM models. This includes tools such as MLflow, Kubeflow, and TFX for model training, versioning, and deployment. Additionally, LLMops tools like Hugging Face Hub and Model Asset eXchange (MAX) facilitate the sharing and deployment of pre-trained language models. By implementing MLOps and LLMops practices, organizations can automate workflows, improve model governance, and accelerate time-to-market.Â
OutcomeÂ
With ML/LLM Ops, organizations can streamline their ML and LLM model development processes, reduce deployment times, and enhance model performance. By adopting MLOps and LLMops practices, they can achieve greater agility, scalability, and reliability in deploying and managing models in production environments.Â