Tickr AI Research & Engineering

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Tickr’s AI and Data Science teams are pushing the boundaries of artificial intelligence and machine learning, with a focus on large language models, time series analysis, natural language processing, retrieval augmented generation, causal inference, pricing, and distributed systems. Join us and check in regularly for the latest discoveries.

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SmartForecast for CPGs: Revolutionizing Decision-Making with AI
This article explores how Generative AI (genAI) can enhance traditional Machine Learning (ML) in solving complex problems faced by CPGs and Retailers. By providing common sense summaries of the patterns uncovered by ML as well as providing a way to dialogue with ML models, genAI can build your (human) team's intuition about root causes, deepen trust between CPGs and retailers around forecasts, and increase alignment on complex pricing and promotional strategies.
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The AI-Augmented CPG, Part 2: What to think about when adopting AI
This article delves into the critical aspects of Security & Data Management and Choosing the Right AI Partner for CPGs looking to adopt AI. We emphasize the importance of safeguarding data with robust security measures and selecting an AI partner with deep domain expertise, experienced in building tailored AI solutions, and a forward-looking approach to AI, data science, and technology. Tickr is positioned as a leader in integrating Generative AI and Data Science into the CPG industry, offering secure, customized, and scalable AI solutions to drive strategic decision-making and operational efficiency.
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The AI-Augmented CPG, Part 1: What to think about when adopting AI
Integrating Generative AI and Data Science is pivotal for innovative decision-making and enhanced market understanding. This empowers CPGs with capabilities like predictive analysis, explainable forecasting, and efficient data-driven strategies through AI assistants. However, it's crucial to understand the nuances of data sourcing, the distinct roles of Generative AI versus Data Science, and the importance of choosing the right data providers. Tickr's expertise highlights the transformative potential of these technologies in CPG operations, offering a roadmap for effective and responsible adoption.
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Creating Synthetic Data to Train Your Own LLM
Large language models (LLMs) have shown great promise in being able to simplify business analytics workflows. Powerful third-party foundational models are being equipped to perform analytic tasks such as data cleansing, model selection and optimization, chart generation, etc. However, there are barriers that must be overcome before there is a large scale enterprise level adoption.
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Identifying Substitutable Goods using Large-scale Shopping Cart Basket Data
This post addresses the challenge of identifying substitutable goods in consumer behavior analysis by leveraging large-scale shopping cart data. We introduce a sequential probabilistic model called "SHOPPER" developed by Ruiz, Athey, and Blei, to analyze hundreds of thousands of shopping trips encompassing millions of transactions. Our approach captures the complexity of consumer purchase behavior, considering various factors such as shopping purpose, seasonal variations, in-store promotions, and personal preferences.
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Improving the Wait for LLM Inference with Server Sent Events (SSE)
This article emphasizes the crucial role of managing wait times in software UX design, particularly in the context of the competitive modern web where user engagement can be significantly impacted by loading delays. It highlights the irony that Large Language Models (LLMs), despite being a breakthrough in software technology, inherently introduce longer wait times, challenging traditional UX principles.
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Distributed Workflows at Scale
Queues are a pattern that have been used at Tickr since the early days of the company. Much of the work we do at Tickr is in the background: data retrieval, alerting, processing incoming webhooks, and many more activities that don't follow the typical request-response pattern found in many typical applications.
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Predicting the Impact of Marketing Campaigns with Artificial Intelligence
Tickr’s Marketing Causal Impact Analysis uses state-of-the-art machine learning to extract the true effect of a marketing campaign on sales, ROI, or any business metric relevant to our customers. This empowers them to make confident and better marketing decisions that lead to increased sales or ROI.
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Transaction Data with TimescaleDB
Time series data forms a critical component of business analytics, and in our pursuit of efficiency, we chose to evaluate TimescaleDB.
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The Impact of Temperature on the Performance of Large Language Model Systems and Business Applications
In today's data-driven world, businesses are increasingly turning to advanced technologies to gain a competitive edge. Large language models ("LLMs") have emerged as a game-changer, enabling businesses to have intelligent conversations with data and extract valuable insights. In this study we explore the effects of LLM temperature, a concept borrowed from statistical physics and thermodynamics, on the impact of LLM business applications.
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Transformational Efficiencies: Tickr AI vs. CPG Human Experts
Large language models (LLMs) are revolutionizing and automating the field of data analysis & knowledge-intensive tasks. The realm of Consumer Packaged Goods (CPG) is ripe for large transformational gains leveraging large language models. In this study we run a double-blinded experiment to assess the transformational capabilities of LLM systems.
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