How we managed to recover a stolen bike thanks to my AI-powered platform

See how AI integrations helped recover a stolen bike for a subscription company. This talk will demo the platform's capabilities.

AI LLM RAG AWS ETL

Overview

I’ll demo all the AI integrations I did for our subscription bike company.

Links

https://rideclover.com/en
Clover offers e-bike subscriptions in Valencia, including Riese & Müller bikes, unlimited battery swaps, maintenance, and theft replacement.

Tech stack

AI

AI: The computational system driving human-level problem-solving (e.g., GPT-4, AlphaGo), actively transforming sectors like healthcare and finance with predictive analytics.

Artificial Intelligence (AI) is the system's ability to simulate human cognitive functions: learning, problem-solving, and decision-making. Key models like OpenAI's GPT-4 and Google DeepMind's AlphaGo demonstrate rapid capability expansion across diverse domains. This technology is actively deploying across critical sectors: healthcare uses AI for diagnostic image analysis (often achieving 90%+ accuracy), finance employs it for real-time fraud detection, and autonomous vehicles (Level 4) rely on its processing power. Global investment validates this impact: the AI market is projected to exceed $1.8 trillion by 2030 (a clear indicator of scale). Focus now shifts to responsible scaling and robust governance (e.g., data privacy, bias mitigation) to manage widespread integration.

https://aaai.org

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LLM

Large Language Models (LLMs) are deep learning models, built on the Transformer architecture, that process and generate human-quality text and code at scale.

LLMs are a class of foundation models: massive, pre-trained neural networks (often with billions to trillions of parameters) that leverage the self-attention mechanism of the Transformer architecture (introduced in 2017) to predict the next token in a sequence. Trained on vast datasets (e.g., Common Crawl's 50 billion+ web pages), these models—like GPT-4, Gemini, and Claude—acquire predictive power over syntax and semantics. They function as general-purpose sequence models, enabling critical applications such as complex content generation, language translation, and automated code completion (e.g., GitHub Copilot). Their core value: generalizing across diverse tasks with minimal task-specific fine-tuning.

https://en.wikipedia.org/wiki/Large_language_model

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RAG

RAG (Retrieval-Augmented Generation) is the GenAI framework that grounds LLMs (like GPT-4) on external, verified data, drastically reducing model hallucinations and providing verifiable sources.

RAG is a critical GenAI architecture: it solves the LLM 'hallucination' problem by inserting a retrieval step before generation. A user query is vectorized, then used to query an external knowledge base (e.g., a Pinecone vector database) for relevant document chunks (typically 512-token segments). These retrieved facts augment the original prompt, providing the LLM (e.g., Gemini or Llama 3) the specific, current, or proprietary context required. This process ensures the final response is accurate and grounded in domain-specific data, avoiding the high cost and latency of full model retraining.

https://en.wikipedia.org/wiki/Retrieval-augmented_generation

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AWS

AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from 33 geographic Regions.

AWS is the global leader in cloud infrastructure, delivering over 200 fully featured services. We operate across 105 Availability Zones within 33 geographic Regions, ensuring high availability and low latency for your applications. Core services like Amazon EC2 (virtual servers), Amazon S3 (scalable object storage), and AWS Lambda (serverless compute) provide the foundational building blocks for any workload. This platform allows customers (from startups to Fortune 500s) to innovate faster, reduce operational costs by moving from CapEx to OpEx, and scale instantly. Security remains paramount: we offer 300+ security, compliance, and governance services, meeting standards like ISO 27001 and SOC 1/2/3. Simply put, AWS provides the secure, flexible, and proven foundation you need to build anything.

https://aws.amazon.com/

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ETL

ETL (Extract, Transform, Load) is the data integration pipeline that moves raw information from fragmented sources into a unified warehouse for analysis.

ETL is the backbone of modern data engineering. It executes a three-step sequence: extracting data from diverse sources (like MySQL databases or Salesforce APIs), transforming it through cleaning and schema mapping (converting currency or deduplicating records), and loading it into a target system (such as Snowflake or Amazon Redshift). This process ensures high data quality and consistency, allowing teams to run complex SQL queries and BI dashboards against a single, reliable source of truth. By automating these workflows, organizations eliminate manual data entry and reduce the latency between data generation and actionable insight.

https://www.ibm.com/topics/etl

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How we managed to recover a stolen bike thanks to my AI-powered platform

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