Knox's feature analyzes messaging history to graph relationship closeness over time. While insightful, it can also create somber moments by revealing friendships or romantic relationships that have declined.

AI models can identify subtle emotional unmet needs that human researchers often miss. A properly trained machine doesn't suffer from fatigue or bias and can be specifically tuned to detect emotional language and themes, providing a more comprehensive view of the customer experience.

Recent studies show that Large Language Models can analyze conversational language—including emotional cues—to predict if a consumer will buy a product with up to 90% accuracy. This capability could replace traditional, action-based marketing intent models with more nuanced language analysis.

People use chatbots as confidants for their most private thoughts, from relationship troubles to suicidal ideation. The resulting logs are often more intimate than text messages or camera rolls, creating a new, highly sensitive category of personal data that most users and parents don't think to protect.

AI tools like Claude Code are evolving beyond simple SQL debuggers to augment the entire data analysis workflow. This includes monitoring trends, exploring data with external context from tools like Slack, and assisting in crafting compelling narratives from the data, mimicking how a human analyst works.

The most reliable customer insights will soon come from interviewing AI models trained on vast customer datasets. This is because AI can synthesize collective knowledge, while individual customers are often poor at articulating their true needs or answering questions effectively.

An LLM analyzes sales call transcripts to generate a 1-10 sentiment score. This score, when benchmarked against historical data, became a highly predictive leading indicator for both customer churn and potential upsells. It replaces subjective rep feedback with a consistent, data-driven early warning system.

Wilkinson's Lindy agent records and analyzes his meetings, flagging psychological tactics like narcissism or manipulation. If it detects red flags based on a high-bar analysis, it sends him a text alert, providing an objective second opinion on interpersonal dynamics and helping him vet business relationships.

Using Opus 4.5, Wilkinson built a tool that takes personality tests from him and his girlfriend to generate a deep relationship analysis. The tool accurately predicted their recurring arguments and now helps them build empathy by articulating underlying emotional triggers during disagreements.