The retail chain previously relied on manual analysis and historical sales to forecast demand for non-food products, a task complicated by niche items with location-specific demand. This approach frequently led to overstocking or stockouts, driving customers to competitors or clogging valuable store space with excess inventory.

The client developed an AI agent for product demand forecasting and ordering, blending historical and real-time sales data with geolocated factors. Tailored to each store, it predicts demand accurately and places orders autonomously, adapting to market changes. This cuts waste, optimizes stock, and keeps products available where and when customers want them, boosting efficiency and satisfaction.

The client still processes over 30% of wire transfer requests on paper forms delivered at branch locations, often handwritten. As labor costs for handling these requests continue to rise, the commission earned per transfer is decreasing.

The client deployed an AI agent powered by a custom SLM trained on their proprietary data. Running in production for several years, the agent autonomously processes handwriting recognition, signature validation, bank account details, AML compliance, and transaction descriptions—with human intervention reserved only for edge cases. This agent has processed millions of dollars in financial transactions with no issues, exemplifying the maturity only a decade of deployments can deliver.

Small credit underwriting is currently handled by the client’s human workforce, who evaluates credit applications, attached documents, and credit records to assess the end-customer’s creditworthiness based on the underwriting risk profile. This process is labor-intensive, often requiring the identification of potentially fraudulent documents and interactions with the client to request missing or incorrect information.

The client deployed a fully autonomous AI agent powered by a custom SLM trained on their underwriting data. Refined through years of production use, the agent analyzes documents, detects fraud, cross-references public records, and interacts with customers via email—all without human intervention. The custom SLM delivers the consistent accuracy that only battle-tested platforms can provide.

The client deployed an AI agent powered by a custom SLM trained on senior agents' expertise—one of Loop's earliest production deployments, running since 2016. The SLM, trained on proprietary data without data scientists, helps junior agents find documents and responses instantly, standardizing expertise across the workforce with years of proven reliability.

Previously, the client used an internal FAQ for each insurance product, but this method still required considerable time for agents to find the correct answer.

The client aimed to develop an AI Agent to optimize the performance of its 1,000-person back-office workforce by routing each inbound request to the most expert agent for the specific topic. The human agent’s expertise was automatically assessed based on past performance in handling similar tasks, analyzing total handling time and the number of interactions required for successful resolution.

In a previous approach, the client had used routing based on competence centers and manually updated skill-based routing within each center.

The client deployed an AI agent powered by a custom SLM trained on historical medical records—diagnoses, vital signs, treatments, and outcomes—while ensuring patient privacy. Running inference on-premises within the hospital's firewall, the agent benefits from Loop's decade of healthcare AI deployments to deliver enterprise-grade accuracy in mission-critical diagnosis support.

By continuously analyzing a patient’s vital signs, existing conditions, and the effectiveness of ongoing therapy, the AI supports the delivery of the most effective treatment options for each patient.

The project aimed to identify existing therapeutic candidates with well-established risk and toxicity profiles that could be repurposed as treatments for COVID-19. By leveraging machine learning and computational transcriptomics, our research lab analyzed gene expression signatures of both COVID-19 and various drugs using publicly available gene expression datasets. This approach enabled a more efficient identification of promising therapeutic candidates. Unlike traditional drug development, which often requires extensive testing and long timelines, this method accelerated the repurposing process, providing a faster response to the rapidly evolving COVID-19 pandemic.

The client’s marketing team successfully implemented a real-time competitor dashboard that enabled them to gain actionable insights by continuously analyzing key aspects of their competitors’ strategies. The dashboard tracked competitor websites, social media discussions, customer issues, content strategies, and email marketing efforts, offering a comprehensive view of competitor activities. This innovative approach allowed for faster, more accurate actionable insights of competitors, replacing the previous method, which relied on slow and costly phone surveys. By adopting the real-time dashboard, the client stayed ahead of competitors, responding swiftly with targeted campaigns while also tracking their own customer base.

The client aimed to gain real-time insights from multilingual dealer repair data to detect defects early, identify root causes, and provide timely warnings for design and manufacturing improvements. The data, coming from dealers across 53 countries, is in local languages with regional terminology and industry-specific jargon, creating challenges for analysis.

Previous approaches using traditional NLP struggled with unstructured text across 53 countries. The client deployed a custom SLM using Loop's language-agnostic training—proven across 15+ languages since 2015, requiring no dictionaries or grammar rules. The AI agent now detects defects in real-time with the reliability only a decade of multilingual deployments can deliver.

The client successfully enhanced their Condition-Based Maintenance system by integrating sound sensors to detect anomalies from the vehicle. While the previous CBM approach, relying on common sensors, was limited to specific devices, the new sound sensor technology provided a more comprehensive data set when combined with structured sensor data. Internal research had shown that changes in vehicle sounds could signal underlying issues before they escalated into major problems. With the implementation of this cognitive application, the client achieved a more holistic approach that enabled earlier detection of defects, allowing proactive intervention before issues became critical.

The client aimed to boost revenues in its IPTV pay-per-view business by enhancing the performance of their recommendation system through automated movie tagging based on public audience reviews in multiple languages. Previously, the client relied on metadata such as genre, MPAA rating, and cast for categorizing movies.

They then deployed an AI agent powered by a custom SLM trained on multilingual public reviews—leveraging Loop's multilingual capabilities validated in production since 2015. The SLM automatically tags movies at scale, replacing expensive manual tagging while maintaining the 100% revenue increase. No data scientists required.

The client adopted a more data-driven approach to scale store openings while minimizing risks related to location selection. They enriched location data by combining structured data (such as POS history) with dark data (such as business descriptions and reviews of potential store locations). This allowed them to predict and assess the value and risks associated with both new and existing restaurant locations, based on historical data from their most successful stores.
Previously, the client relied on traditional demographic research data, which was typically updated only for the most popular locations every few years.