New Technology, Same Commitment: Our Responsible AI Principles

We're living through one of the most consequential technology shifts of our lifetimes. Generative AI is reshaping how people analyze information, make decisions, and do their work at a pace that would have seemed implausible only a few years ago. For industries like ours, where professionals rely on data to make high-stakes decisions about the physical world, the opportunity is especially exciting. Insights that once took weeks can now surface in minutes. Analytical workflows that once required specialized training can become accessible to anyone.

But that opportunity comes with real responsibility. The same capabilities that make GenAI so powerful also introduce risks such as bias, accuracy, privacy, and misuse - and those risks compound when the underlying technology is moving faster than the norms and regulations around it. The companies building with AI today are, in many ways, writing the operating rules in real time. How we choose to do that matters.

At Placer, we want to be clear about how we choose to do it. Placer doesn't build its own large language models (LLMs). Instead, we use well-established, trusted models from leading providers - the same foundation models that power the most widely adopted AI tools in the enterprise today. That's a deliberate choice. Our value to customers comes from the depth and quality of our data and the analytical expertise built around it, not from reinventing general-purpose AI infrastructure. 

But not building the models ourselves doesn't let us off the hook for how we use them. If anything, it raises the bar. When we embed GenAI into our platform, whether as an analytical assistant, an automated summary, or a future agent that helps professionals move faster through their workflows,  our customers trust us with the outcome. They're trusting us to pick the right models, apply the right guardrails, protect their data, and be transparent about what the technology is and isn't doing.  

That's why we're publishing our Responsible AI Principles today. They're clear, concise, and they reflect how we actually operate.

The four Responsible AI principles address the issues we believe matter most to the professionals who rely on Placer every day:

Fairness and bias mitigation. AI systems can reflect and amplify existing biases in their training data. Our core defense is something we've been doing since long before GenAI: continuously validating our models, monitoring our AI practices and de-biasing outputs where appropriate.

Transparency and accountability. When we use GenAI in customer-facing features, we say so. We build feedback mechanisms into the product and treat that feedback as a real input to how the system evolves. 

Privacy by design. Our AI tools are built to identify patterns about places and brands, not individuals. The same strict privacy measures that govern the rest of the Placer platform apply to every new GenAI feature we ship.

Security and safety. We are responsible custodians of our customers' data and are committed to safeguarding its integrity using industry leading standards.

We've also published a clear statement on how Placer's GenAI capabilities may be used and what restrictions we apply. These aren't new restrictions; they extend the responsible-use commitments that have always governed how our data can be used.

We're excited about the era of GenAI and about the value these new capabilities will create for our customers. The AI principles we're publishing are part of a broader effort across the company that’s grounded in a simple idea: trust isn't something we claim once and move on from. It's something we earn in every feature we ship.