CHALLENGES • A visionary start-up consulting agency built an early-stage carbon footprint management tool for the food industry that had passed the PoC stage and was gaining market traction. • The goals were twofold: • Design and implement the architecture for commercial cloud SaaS delivery, where keywords were multi-tenant, automated ingestion, scalability, security and data governance. • Elevate the business offerings to the next level by employing and demonstrating the usefulness of AI.

RESULTS • Cloud architecture to support AI-powered SaaS delivery. • AI-powered, forward-looking carbon footprint management, enhancing a unique value proposition in the market. • Data strategy to guide future development and funding.

CHALLENGES • A large financial company handled thousands of daily customer interactions with no systematic understanding of what customers were calling about. • Decisions on product improvements and retention were made on intuition and manual spot-checks, leaving the business without a reliable foundation. • The business needed a data-driven view of contact drivers and wanted to understand how call behaviour connected to the customers most likely to churn. • Working with Danish-language call data added another layer of difficulty, as most off-the-shelf NLP tooling is built and benchmarked on English, with limited support for Danish language processing and insurance-specific terminology. • Within the confines of GDPR, this also required strong anonymization techniques.

RESULTS • End-to-end pipeline with transcription, anonymization, processing, NLP analysis and insights. • Increased and systematic visibility into call center issues and performance.

CHALLENGES • A large retail brand was operating with siloed, Excel-based reporting across a fragmented data landscape. Data arrived from many different SaaS platforms and internal systems, each requiring a lot of manual processing and effort. • Leadership had no single version of the truth. Each team had its own numbers, its own definitions, and its own spreadsheet. • Moreover, the business had simply outgrown Excel in sheer data volumes and needed more sophisticated technologies.

RESULTS • Cloud data architecture to handle large data volumes and a growing business. • Automated data ingestion, replacing manual work and reducing the risk of errors. • Unified data model with consistent definitions and one source of truth. • Improved insights and analytics across the business.

CHALLENGES • An omnichannel fashion retailer had no forward-looking view of where inventory would land at season end. • Markdown and replenishment decisions were made too late, which would result in stockouts during campaigns or forcing lower margins during clearance sales. • Many of the products were manufactured in China with lead times of up to 6 months, exacerbating any errors. • The nature of the fashion industry was also characterized by distinct seasonal patterns and trends, making prediction extremely difficult.

RESULTS • A dedicated, forward-looking tool answering one simple question: how much inventory do I risk ending up with for clearance sales if I do not do anything now? • Ability to reorder or markdown products in a timely manner, resulting in higher sales and better margins.

CHALLENGES • A consulting agency specializing in agriculture machinery and equipment wanted to provide intelligent, ML-driven price predictions offered as a suite across e.g. inventory and trade-in offerings. • The data was highly dimensional with a lot of optional add-ons and a lot of missing values that had to be inferred. Some information would also be unknown from physical inspection at inference time. • To build trust, it was essential to explain the price predictions in a simple way for dealers and buyers alike. • The solution was offered as a SaaS, implemented in cloud, which required strong scalability and generalizability in model performance and design in general.

RESULTS • Ability to offer two packages focused on getting the optimal price across the business, resulting in increased margins and lower depreciation risk. • Inventory valuation predicting the optimal sales price on SKU-level and which period of the year to obtain the best price, accounting for seasonality.