Finding Needles in Haystacks

Data and Knowledge Management

Most organizations don't lack data. They have more than enough. The bottleneck is rarely collection, it's making the right piece of information findable, trustworthy, and usable at the moment someone actually needs it. This is where AI is genuinely strong. Finding the proverbial needle in a haystack, spotting the pattern across millions of records no human would have the patience to trace, reading a document and pulling out what matters - these are tasks where well-built machine learning outperforms manual effort by a wide margin.

“Knowledge is of two kinds. We know a subject ourselves, or we know where we can find information upon it.”

Samuel Johnson (1709–1784)

How is your chatbot doing?

The 9senses Chatbot Audit evaluates the performance of your chatbot from a user perspective.

Talk to Eliza

Talk to Joseph Weizenbaum's Eliza in a replica of the 1966 version.

With Generative AI, we risk stealing from our own future

When we are using generative AI, to create new output we are exploiting knowledge previously generated by humans. But who is rebuilding knowledge for the next generation?

AI chatbots in the automotive industry – promise or hype?

9senses Market Analysis: Only 16 out of 129 automotive providers in the DACH region use AI chatbots for customer service - with significant variations in quality

The lost generation

AI lets us skip the slow, clumsy, error-prone work of becoming competent. The bill for that shortcut arrives in the future, when the people who were supposed to replace today's experts never built the ability to judge.

The AI Feedback Lottery

Large Language Models judge your work differently every time you ask. That is not just a quirk of the technology - it is a fundamental challenge to how we create, validate, and trust ideas.

A confident confabulator

AI hallucinations aren't random - they cluster, systematically and predictably, in the topics you cannot independently verify.

Lost in Translation

Conversational AI is dominated by English, with serious consequences for other languages that are structural and can only be resolved with significant effort.

But there is a catch that gets overlooked in every demo: an AI system is only ever as good as the data underneath it. Feed it incomplete, contradictory, or poorly structured information, and it won't tell you it's struggling. It will answer with the same confidence it shows when it's right.

That is why we treat the unglamorous foundation work - clean, well-structured, governed data - as the part that actually decides whether an AI project delivers value or just looks good during a demonstration. Get the foundation right, and the applications on top become reliable. Skip it, and no amount of model sophistication or UI polish will save it.

Key Topics

Knowledge Management

Making knowledge readily available for employees and customers alike creates better outcomes.

Data Lakes / Warehouse

Making large data easily available across systems provides the ability to efficiently run a business.

Document processing

Automatically processing incoming documents with high accuracy streamlines business processes.

Customer Insights

Understanding and prioritizing your customers better, finding opportunities and detecting fraud.

Coming soon...