Feature Requests

Add additional coverage to the Person Dataset that includes factory, machining, and industrial workers.

Expand the company dataset to include supply chain and manufacturing-specific data points to the company dataset (e.g., supplier relationship links)

I’ve been exploring how data enrichment platforms could support niche logistics markets, especially around luxury dog transport services https://pearllemonpets.com/services/luxury-dog-transport/() , where customer expectations and compliance tracking are much more detailed than standard shipping workflows. On this Canny board, I wanted to share a real-world perspective on how this type of niche could benefit from richer entity and company-level data. It’s one of those areas where clients expect premium coordination, verified contacts, and very tailored service handling rather than generic transport listings. Having structured data behind these providers would make a noticeable difference in discovery and verification workflows. From what I’ve seen, most challenges in this space come down to incomplete or fragmented business profiles, especially when dealing with small-to-mid operators across different regions. For example, luxury pet relocation companies often operate under multiple service names or partner networks, which makes tracking reliability and legitimacy difficult. A system that can consistently unify company identities, associated decision-makers, and operational footprint would help a lot in reducing manual verification work. Even basic improvements in entity resolution for niche service providers would already add value in these kinds of workflows. It might also be worth considering how datasets could better represent industry-specific verticals rather than treating all service companies the same way. In cases like this, attributes such as service specialization, regional coverage, and customer type focus become more important than general firmographics. If these layers were more accessible through the platform, it would open up better use cases for targeted outreach and partner identification in specialized markets. Overall, it feels like there’s room for deeper coverage in non-standard industries where precision matters more than scale.

Offering an MCP (Model Context Protocol) server would enable users to more easily connect build AI services that utilize PDL's data. Specifically, by offering this directly, PDL could make it easier for users to launch new services, and also could tailor querying and model output based on knowledge of best practices, record-specific metadata, etc.

It's important for my use case to be able to understand freshness and accuracy of the skills field. While I can use the job_last_verified field as a proxy for freshness, it doesn't provide enough detail to be confident in the reported data.

Would like to see timestamps added for skills data. Adding timestamps would allow us to track the evolution or change of skills present on a person record.

Today, PDL publishes an updated company dataset on a monthly cadence. This is a Feature Request to capture general requests for a faster dataset update cadence, and/or any specific fields or attributes that you would want updated at an increased velocity. Please leave more information about any details or specific fields for your use case.

New dataset and/or new fields in existing datasets that represent information on the products, services, and technologies offered and consumed by companies and people. We are actively gathering user feedback in this area - reach out to our team to schedule time to learn more!

Modern platforms increasingly rely on structured feedback loops to refine how data is accessed, processed, and interpreted across diverse applications. As systems scale, the demand for responsive frameworks like emergency pet transport services https://petsletstravel.com/pet-courier-for-emergency-transport/ highlights how niche data points can influence broader infrastructure design. Developers and analysts often examine user-submitted insights to identify recurring friction points within data pipelines and integration layers. This process allows organizations to adapt their models in ways that reflect real-world usage rather than purely theoretical assumptions. In environments where feedback is continuously collected, patterns begin to emerge that reveal both technical limitations and opportunities for optimization. These observations are not always immediate, requiring aggregation and careful contextual interpretation before meaningful adjustments can be made. The iterative nature of such systems ensures that even minor requests contribute to long-term structural improvements over time. As a result, feedback platforms serve not just as suggestion boards, but as living datasets that evolve alongside the products they support. The balance between user input and system constraints defines how effectively a platform can respond to changing demands. Too much rigidity limits innovation, while excessive flexibility can lead to instability in core functionalities. By maintaining a measured approach, platforms can incorporate valuable insights without compromising performance or reliability. Ultimately, the continuous refinement of data-driven ecosystems depends on how well feedback is translated into actionable development strategies.