During my time working with Jutsu, I gained valuable insights into the complexities of building AI tools that integrate seamlessly into the day-to-day operations of businesses. What stood out to me about Jutsu was their mission to make AI applications reliable, secure, and user-friendly—addressing a significant gap in the AI space as more businesses adopt these tools. I worked on two key features: the knowledge base and the prompt studio, both of which were designed to make AI applications more grounded in real-world business needs.
We need “Perplexity” for private data because so much data is hidden. Whoever gets the plugins and indexing right will take off because the existing search on platforms sucks.
One of the main takeaways from my time at Jutsu was the importance of having a “single source of truth” within a business. Many organizations work with siloed data—sales, customer service, marketing—often disconnected and difficult to manage. Jutsu’s knowledge base sought to solve this problem by consolidating all internal and external data into one unified system. This didn’t just mean collecting data from different sources; it meant making that data actionable, so it could directly inform AI-driven actions. The power of the knowledge base, as I learned, was its ability to process and activate data in a way that was genuinely useful for the business.
The prompt studio, which I was also involved with, helped optimize the reasoning pathways that AI uses to perform business tasks. Instead of simply giving an AI a task, you break it down into smaller, manageable actions that guide the AI step-by-step. The prompt studio allowed us to chain these tasks together in a way that ensured each action was aligned with the business's goals. It wasn’t just about making sure the AI did what it was told; it was about making sure it did the right thing in a way that was aligned with how the business needed to operate. This approach helped the AI feel more like a useful tool than a black box.
Let’s understand how generation works.
1. LLM - Choosing the right Large Language Model to power your application.
✅ It is becoming less of a headache the further we are into the LLM craze. The performance of available LLMs are converging, both open source and proprietary. The main choice nowadays is around using a proprietary model or self-hosting.
What did Jutsu fit in?
Context is all you need.
The hard things for humans are easy for machines. The things we take for granted are insanely hard for machines. This is not a new idea. It is called moravec's paradox in robotics and it is now the main reason why companies are not seeing enough ROI on AI.
AI excels at complex, abstract tasks like chess, but struggles with simple everyday tasks like vacuume cleaning your house.
Cleaning your house, folding your laundry.. None of these things require a PhD, but they require:
This is the AI context paradox. Putting things in the right context, building on intuition we built over many years to make sense. This is the key observation for differentiated value. You need deep interprise expertise context.