Palantir AIP (Artificial Intelligence Platform) connects large language models to an organization’s data and operational context inside Foundry. Rather than reasoning over disconnected text, AIP lets models query and act on real business objects — customers, assets, orders — through Foundry’s Ontology, and supports building AI agents, AIP Logic-driven workflows and applications on top of that grounding.

AIP is built on top of Foundry’s data platform and Ontology. Foundry connects and governs enterprise data and models it around real business objects and relationships; AIP gives AI models access to that same governed context, so AI-powered workflows and applications reason with accurate, permissioned, up-to-date information instead of operating in isolation.

Generative AI refers broadly to models like LLMs that can generate text, code or other content. AIP is a platform for putting generative AI to work inside a specific enterprise’s data and Ontology — it’s the layer that connects general-purpose models to an organization’s real business context, governance and workflows.

Yes. PAL4C LABS designs and builds custom generative AI applications, AI agents and Copilot experiences around specific business processes, connecting them to an organization’s existing data platforms, applications and workflows rather than shipping a generic chat interface.

Yes. AI is most useful when it’s connected to the systems an organization already runs on — ERP, CRM, data platforms, document stores and internal applications. PAL4C LABS designs the integration, retrieval and governance layer that lets models reason over that data safely and accurately.

RAG (retrieval-augmented generation) is a technique that grounds an AI model’s responses in trusted external information — retrieving relevant documents or data at the time of a query and providing them to the model as context, rather than relying only on what the model learned during training.

AI agents are AI-powered workflows that can reason over context, take multi-step actions and, where appropriate, involve a person in the loop before completing a task. In an enterprise setting, agents are typically scoped to specific operational tasks — research, triage, drafting, analysis — rather than open-ended autonomy.

Copilot becomes more useful when it’s connected to an organization’s own data and scoped around specific roles and workflows. That typically involves identifying high-value use cases, connecting relevant data sources, designing the experience around how a role actually works, and putting governance and adoption support in place.

Securing enterprise AI means controlling what data a model can access, enforcing existing permissions rather than bypassing them, evaluating model outputs before they reach production, and monitoring usage on an ongoing basis. PAL4C LABS designs these controls as part of the AI architecture, not as an afterthought.

Moving from proof of concept to production typically requires connecting the model to real (not sample) data, adding evaluation and monitoring, addressing security and governance requirements, and designing the application around how people will actually use it day to day. PAL4C LABS scopes that path during discovery rather than assuming a demo is production-ready.