Many businesses overlook their most valuable existing data assets. Years of unstructured data, like support tickets detailing integration issues and customer problems, are invaluable for training specialized AI models. This 'boring' data can become a key source of competitive advantage when activated with an LLM.

AI is only as good as the public data it's trained on. An expert coach provides proprietary, real-time frameworks and data that AI cannot access. For AI expert Callan Faulkner, paying for coaching is a strategy to 'collapse time' and gain an unbeatable competitive edge that generates massive ROI.

Instead of building a full-fledged AI product first, launch a manual service for a target industry, using local AI tools behind the scenes. The recurring problems you identify and solve manually become the proven, high-value checklist for your future software product.

For sophisticated AI tools requiring deep business context, a purely self-serve onboarding often fails. Plurium validates its PLG motion by initially using human consultants for setup to ensure data accuracy and gather context, then building those learnings into an automated, self-service flow over time.

Turing operates in two markets: providing AI services to enterprises and training data to frontier labs. Serving enterprises reveals where models break in practice (e.g., reading multi-page PDFs). This knowledge allows Turing to create targeted, valuable datasets to sell back to the model creators, creating a powerful feedback loop.

Early enterprise customers won't invest time to become proficient with a complex data tool. Founders must join their meetings, operate the software for them, and surface insights to demonstrate value. This manual "data monkey" role is crucial for driving initial adoption.

A successful strategy for AI startups is to initially leverage state-of-the-art foundation models to acquire users and data. Once sufficient high-quality, domain-specific data is collected, they can train their own specialized models to drastically cut costs and latency.

Selling a novel deep-tech platform involves more than data. The path to the first contract requires securing proof-of-concept funding, actively seeking critical feedback beyond your friendly network, and ultimately leveraging long-term, trusted relationships to find a partner willing to take the initial risk—often incentivized by significant discounts.

Top AI talent wants to work for AI companies, not legacy SaaS businesses. To compete, sell them on your unique advantages: a massive, proprietary dataset for model training and an existing distribution channel that ensures their work gets used by thousands of customers on day one—something AI-only startups lack.