AI models evolve quickly – but a strong data foundation lasts. While models can be replicated, fine-tuned, or even outperformed by newer advancements. In many cases the companies that build lasting value are the ones that invest in a strong data strategy – one that continuously improves and deepens over time.
What makes a dataset valuable isn’t just that you own it, but that it keeps getting better the more it’s used. The best Enterprise AI startups don’t just collect data; they design their businesses in a way that naturally enhances their dataset with every user interaction.
Without a proprietary data advantage, it’s hard to maintain a long-term edge. Founders who rely solely on foundation models or publicly available data risk competing on AI capabilities alone – an advantage that rarely lasts.
If your Enterprise AI startup relies on public or widely available data, competitors can build the same thing.
Successful AI-driven companies develop data that is:
Unique: Structured in a way that no competitor can easily replicate.
Continuously improving: Growing in value over time as more data is collected and refined.
Deeply embedded in workflows: So integral to the user experience that switching costs become high.
A well-designed data strategy isn’t just about collecting information – it’s about thinking ahead to how your dataset can scale over time. Many Enterprise AI companies start with small, manual processes to establish data quality before transitioning to automated methods as they grow. This means designing your product in a way that naturally accumulates better, richer data over time, whether through customer interactions, workflow integrations, or AI-powered feedback loops.
Example
Navina built proprietary medical AI models by structuring domain-specific healthcare data instead of relying on off-the-shelf LLMs trained on general medical texts. Their ability to integrate with medical workflows allowed them to collect continuous, high-quality data, reinforcing their moat.
Not all Enterprise AI startups begin with a wealth of proprietary data. Many gradually develop it over time by finding ways to make their dataset more valuable. Here are some approaches that have worked for successful Enterprise AI companies:
Examples
Protai started with open-source clinical data but had to go through a complex harmonization process to make it usable. By transforming fragmented data into a structured, proprietary dataset, they built an advantage that others couldn’t easily replicate.
OnFire scans public sources like Slack, Discord, and Reddit, leveraging its proprietary entity resolution engine to build a structured database of profiles covering 50 million engineers and technical buyers—providing unique, actionable insights into their tech stacks and buying intentions.
FOUNDER TAKEAWAY: If you’re using public data, focus on how you organize, refine, and enhance it to make it more useful and differentiated.
Example
Nucleai partnered with pathology labs to gain access to proprietary tissue data. In return, their AI-driven insights provided value back to these labs, creating a mutually beneficial cycle where the data and AI capabilities improved together.
FOUNDER TAKEAWAY: AI isn’t just the product – it can also be a tool for scaling and refining your data strategy.
Example
Limitless.CNC is building its own proprietary dataset of CNC machine operations, manually tagging real-world machining data to train its AI agent. This high-fidelity, domain-specific data became the backbone of its autonomous CAM system—enabling capabilities that generic datasets couldn’t provide. The result: a defensible technical moat in an otherwise conservative industry.
FOUNDER TAKEAWAY: If you can’t access the right data, consider building it yourself through domain expertise and proprietary workflows.
ActiveFence used multiple AI models to reduce human labeling needs and improve dataset accuracy. By leveraging AI to assist with data annotation, they scaled their dataset more quickly while maintaining quality.
FOUNDER TAKEAWAY: AI isn’t just the product – it can also be a tool for scaling and refining your data strategy.
The initial effort to build a dataset can be significant, but the real value emerges when a network effect takes hold – where the data improves as more customers use the product.
In many cases, companies can design their systems so that customer interactions naturally enhance the dataset. Some achieve this by enabling customer feedback to refine data quality, while others integrate ways for customers to share their first-party data, making the overall dataset more comprehensive. In both cases, the result is a network effect: each new customer benefits from better data from day one, and the dataset keeps getting stronger over time.
The key is ensuring that as the dataset grows, the cost of collecting and refining data decreases, while the quality and diversity of insights continue to increase.
Example of Data Network Effect in Action:
ActiveFence improved its AI-driven moderation tools as more customers used them – making its product increasingly valuable with every new user. They started with models designed to detect multiple abuse areas. As their data collection processes became more efficient and their customer base and product usage grew, they were able to develop more specialized models for numerous subcategories of harmful content. This increasing granularity and specificity in their dataset wasn’t feasible on day one but emerged naturally as their data collection processes matured and scaled.
OpMed developed a novel algorithm for operating room and broader resource utilization. Their system’s procedure time estimations continuously improve as more real-world data is collected, enhancing optimization results for both existing and future customers.
Nucleai has created a powerful flywheel effect through collaboration with pharmaceutical companies and research partners. By integrating real-world data from clinical trials and translational research into its spatial proteomics platform, Nucleai continuously enhances the quality and diversity of its dataset. Each new partner contributes proprietary data and insights, which refine the algorithms and improve the decision-making for all users. This network effect ensures that every additional customer gains immediate value from an increasingly robust and comprehensive dataset, further solidifying Nucleai’s competitive edge.
Design your product so that every interaction improves the dataset.
Ensure that the cost of data collection decreases over time while the dataset grows in value.
Find ways to incentivize users to contribute data that enhances the system.
Some AI-driven startups win by keeping their data fresher than competitors.
While some Enterprise AI applications rely on data with long-term relevance (e.g., medical imaging), others derive their value from data freshness and real-time insights. In these cases, a company’s advantage comes from how quickly and efficiently they can process and update their data.
The ability to keep data fresh and actionable creates a stickiness factor – users return regularly because they trust the platform to provide up-to-date, relevant information. This dynamic also influences product architecture, requiring Enterprise AI startups to prioritize rapid data ingestion, processing, and presentation mechanisms to ensure insights remain timely.
For startups operating in fast-changing industries, data recency can be a competitive advantage just as strong as proprietary data.
Example:
OnFire addressed a critical need among Go-To-Market (GTM) teams for real-time insights into customer buying behavior. By continuously collecting and analyzing millions of messages from online platforms, OnFire helps sales and marketing teams improve their conversion rate and make better decisions based on more precise and relevant data.
Seamless data collection and updates – ensuring your insights remain current.
A clear feedback loop – allowing customers to interact with and improve the dataset over time.
An architecture optimized for agility – so your product can process and present new data efficiently.
