Notes and Context

For context about how we ended up here, please check the FAI Strategy page

To check more details about the companies to which we had warm intros so far: ‣

Leads

Note 1: The USPs are more or less ordered from the one that we find most interesting to the least interesting. The only exception is the "Maintenance Optimizer”, which is above what it should be just to be in the same technology group.

Note 2: We spoke with more companies about more use cases, but to here we decided to just consider those to which we had a proper meeting and a proper conversation about the problems they have.

Use Cases Legend:

• AI Mentor = CEOs and employees making decisions often want a kind of mentor to guide them—similar to how many people use ChatGPT today for advice. Examples: A CEO is unsure whether to fire an employee and asks the AI Mentor, which understands the full business context, and provides guidance on the best course of action. A new employee might also use the tool to decide whether to focus on Task A or Task B, without needing to consult a supervisor and take up their valuable time.
• AI Information Simplifier = CEOs and directors often need quick, digestible information to make better decisions. Example: A chatbot where the CEO can ask why sales dropped last month, and it responds that Client X didn’t place an order. With this insight, they can instruct the sales team to reach out to Client X.
• AI Knowledge Base = CEOs worry about being overly reliant on one key person—if something happens to them, the company risks losing critical knowledge and competitiveness. Example: If the lead engineer is suddenly unavailable, or the CEO sells the company, its value may drop significantly without that key individual. An AI Knowledge Base helps preserve and share institutional knowledge.
• Maintenance Optimizer = A particular company sells machines with a multi-year warranty. This is uncommon in their industry and has become their key differentiator. If a machine breaks, they send one of their top engineers to fix it within 48 hours—a service their customers love. They are a Portuguese company selling mainly to Portugal and Spain. All was working well until they started selling to Mexico, where the 48-hour promise became impractical—engineers had to fly there, and fixes took 1–2 weeks, and the costs associated with loosing the engineer during that time were too high. Due to this, they realized they stored all service information on paper and were being inefficient, as many problems could have been solved over the phone—if previous fixes had been digitized and structured. Standard software solutions were too cumbersome for their non-tech-savvy factory workers, who found it difficult to fill out software forms. AI Solution: Use AI to read and understand handwritten reports without changing the current workflow. The AI can identify if a problem has occurred before and suggest the appropriate fix, providing efficiency while maintaining existing processes.

• Quotation Planner = CEOs often allocate different quotes for the same product depending on customer location, size, and other factors. They want a tool to automate this and generate optimal quotes tailored to each client.
• Sales Optimizer = CEOs want to increase sales. Currently, they manually evaluate potential clients by comparing existing customers to potential clients using publicly available data. They’d like to automate much more of this process—sending and responding to emails, identifying prospects, and using contextual data to find new customers more efficiently.
• Accountability Optimizer = In small companies, CEOs often act as CFOs and are overwhelmed by financial tasks. They want to focus more on growing the business than handling accounting. Example: A tool that integrates with the official Portuguese accounting system and helps surface key metrics like revenue and expenses, while automating manual tasks like expense categorization. Tools like Puzzle exist in the U.S., but many European countries—like Portugal—have more bureaucracy, and local solutions are lacking.
• Stock Optimizer = CEOs seek better inventory management to reduce costs. Big companies like Amazon already do this, for example by moving items based on user profiles and antecipating demand. However, this tech is mostly available to large corporations with specific retail use cases. Example: One CEO tried using such tools to cut costs but found them ineffective because his processes, although standard for his type of supermarket, weren’t well-supported by existing solutions. He said we’d “be rich” if we could solve this particular problem.

• Personnel Optimizer = Similar to the Stock Optimizer, but focused on workforce management.
• Production Line Planner = Production line managers need tools that help them plan manufacturing efficiently based on their factory's specific context—available machines, raw materials, and so on. Specific problem: Switching a machine from producing Product A to Product B requires downtime for mold changes, which is inefficient. They currently do monthly planning to minimize these switches, but it’s time-consuming and there are too many unforeseen events, like a priority customer suddenly wanting more of one kind of a product or a machine having an error. This is making them missing deadlines. AI Solution: A planning tool that uses context—like machine availability and mold change costs—to optimize production schedules and reduce waste.