Fortune Cookie

Fortune Cookie works through a chat interface, so employees interact with it the way they'd message a colleague. They can perform tasks, search for data, run queries, get alerts, and generate content from that single window, across many enterprise applications. Security sits at the center of the design. That matters when the assistant reads payroll, sales, and HR records.

Rather than forcing one model on every customer, the platform lets teams implement customized large language models and select the model that fits their needs. RandomWalk frames this as a way to keep the system efficient and cost-effective. A lightweight model can handle routine lookups while heavier ones take on complex analysis. Pick per task, not per company.

The assistant analyzes large data sets to surface patterns that support decision-making. It plugs into data visualization tools using generative AI, so the output is more than text. Ask a question and get back a chart or dashboard. No wall of numbers to decode. Data that sits in silos is the enemy here. The tool also turns raw tables into views a manager can actually read.

Isolated data is the problem Fortune Cookie sets out to fix. It transforms scattered sources into semantic knowledge graphs and vector databases, which is how it connects the dots between records that live in different systems. That's the plumbing behind accurate answers. Without it, search returns noise.

The assistant improves organization-wide search so people can reach relevant information without knowing exactly where it lives. Instead of guessing file names or folder paths, they describe what they want. No training on folder taxonomy required. That cuts the time spent digging and keeps knowledge accessible across departments.

Fortune Cookie is built on RAG. In plain terms, that means the assistant fetches relevant company data before answering, instead of relying on whatever the model memorized during training. It pairs a vector database with a knowledge graph. Vector databases handle fast similarity searches across big data sets, which suits semantic search and recommendations. Knowledge graphs reveal relationships and dependencies, which helps the model understand context like reporting lines or how a metric rolls up. So what does that look like in practice? Ask about a metric, and the model traces it through the graph before it answers, rather than returning a loose keyword match that happens to share the same words. That's the difference that matters in a large org.

The interface is designed around follow-ups, summaries, and data views. The goal is to improve the employee experience rather than dump raw output on them. For teams rolling this out widely, a familiar chat layout lowers the training burden. People already know how to type a question. Nothing to learn.