Systems | Development | Analytics | API | Testing

AI and analytics are only as powerful as the data they rely on. Without accurate, consistent, and governed data, even the most advanced AI models can produce misleading results. That’s why data quality isn’t just nice to have — it’s essential. At Qlik, our customers across the globe rely on our solutions to drive AI-powered insights, optimize decision-making, and mitigate risk. And it all starts with trusted data.

APIs often represent the cutting edge of the technology space. This is especially true with Artificial Intelligence – as AI has evolved from speculative technology to mass adoption, it has shown up significantly in APIs as a modality and mechanism. However, as with all new technologies, using AI APIs comes with significant challenges.

Much of your organization’s most important data is unstructured. Automated data integration allows you to access it, and AI to analyze it.

Many financial services companies are experimenting with AI through pilot programs, but several challenges remain for adoption. Key concerns include data security, the accuracy of large language models (LLMs) and the rigorous scrutiny from regulators regarding AI’s role in financial decision-making. Current use cases are largely internal, with some customer-facing chatbot solutions addressing noncritical service inquiries.

AI agents are transforming business operations by automating processes, improving decision-making and unlocking new efficiencies. However, their effectiveness depends on how well they are trained. AI Agent Training is the structured process of teaching AI models to perform multi-step assignments, make decisions and adapt to real-world scenarios.

In today’s fast-moving digital economy, organizations need real-time intelligence to power AI, analytics, and increasingly fast paced decision-making. But to successfully deploy AI and advanced analytics, businesses must operate on trusted, up-to-date data streams that provide an accurate picture of what’s happening right now.

Opening disclaimer: I am going to make claims here and provide limited proof. Fear not, fellow truth seekers. Future blog posts will provide numbers, evidence, and elaboration.

Unstructured text is everywhere in business: customer reviews, support tickets, call transcripts, documents. Large language models (LLMs) are transforming how we extract value from this data by running tasks from categorization to summarization and more. While AI has proved that real-time conversations in natural language are possible with LLMs, extracting insights from millions of unstructured data records using these LLMs can be a game changer. This is where batch LLM inference becomes essential.

Does your AI solution help maintain and write tests in an extensible way? If not, then it's not really helping.