Supply Chain Optimization & Text Processing
Data-driven solutions for smarter inventory, demand forecasting, and logistics planning
Predict future demand patterns to optimize inventory levels, reduce overstock, and avoid stockouts. Improve procurement and production planning with accurate demand forecasts.
Time series forecasting uses historical data to predict future demand, incorporating ARIMA, Prophet, and LSTM models. These methods detect patterns and seasonality, enabling accurate inventory planning and reducing overstock or understock risks.
Automate decision-making for logistics and resource allocation. Optimize production schedules, and delivery planning to reduce costs and improve efficiency.
Optimization algorithms like linear programming and genetic algorithms are used to solve complex logistical problems such as vehicle routing, scheduling, and resource allocation. These techniques minimize costs and maximize efficiency in supply chain operations.
Identify hidden patterns in supply chain data to improve decision-making. Enhance demand forecasting, supplier risk assessment, and customer behavior modeling.
Machine learning models, including random forests, gradient boosting, and neural networks, are trained on historical sales, market data, and supplier performance. These models provide actionable insights and predictions to support strategic supply chain decisions.
Detect unusual patterns in supply chain operations, such as unexpected delays, supplier performance issues, or inventory discrepancies, to prevent disruptions.
Anomaly detection techniques such as isolation forests, autoencoders, and statistical thresholding are used to detect deviations in supply chain patterns. This helps identify fraud, disruptions, or inefficiencies in real-time for corrective action.
Quantify the impact of variables like price, promotions, and seasonality on sales. Use insights to improve pricing strategies and inventory planning.
Statistical modeling includes regression analysis, factor analysis, and Bayesian methods. These models help understand relationships in data and quantify uncertainty for better decision-making.
Group products, customers, or suppliers based on shared characteristics. Use these insights for targeted marketing, inventory categorization, and supplier portfolio optimization.
Applies clustering techniques like K-means and DBSCAN to segment products, customers, or regions, allowing for targeted strategies in inventory management and logistics planning.
Extract valuable insights from large volumes of operational and transactional data. Identify opportunities for cost reduction, process improvement, and risk mitigation.
Data mining techniques like association rule learning, principal component analysis, and decision trees extract valuable insights from large datasets. These methods uncover trends, correlations, and patterns that can be used to optimize operations and reduce costs.
NLP techniques for intelligent text-based applications
Enhance AI responses with real-time data retrieval, ideal for building chatbots, documentation tools, and knowledge bases that require contextual accuracy and up-to-date information.
RAG combines retrieval from external data sources with generation to provide accurate outputs. Technologies used in RAG include vector databases, semantic search, and transformer-based models like GPT-4 and LLaMA.
Automatically extract structured information from unstructured text to build knowledge graphs, support semantic search, or integrate data into enterprise systems.
Knowledge extraction uses techniques like named entity recognition (NER), relation extraction, and semantic parsing to convert text into structured data. Tools like spaCy, Stanford NLP, or custom transformer models are often employed.
Optimize AI model performance for specific tasks by crafting effective prompts, enabling developers to customize behavior without retraining the model.
Prompt engineering leverages techniques such as few-shot learning, zero-shot learning, and prompt chaining to guide model outputs. It often involves iterative testing and fine-tuning of input structures and templates.
Adapt pre-trained language models to domain-specific tasks, such as contract analysis, customer support automation, or document classification.
Fine-tuning involves training a pre-trained model on domain-specific data using frameworks like axolotl, Hugging Face Transformers or PyTorch. Techniques include transfer learning, data augmentation, and model quantization for deployment.
Generate realistic, domain-specific training data when real-world data is limited or sensitive, ensuring model robustness and privacy compliance.
To create artificial data, that mimics real world data sets, methods like data augmentation, reflexion and rule based systems can be used. Generated data can be filtered by domain relevant criteria for further use in evaluations and training.
Ensure model reliability and performance by systematically evaluating AI systems using domain-specific metrics and benchmarks.
Evaluation involves using metrics like precision and recall to assess model performance on tasks of interest, such as hallucination rate, coding and mathematical reasoning or instruction following ability.
Jendrik Potyka · CEO & founder