Palantir, a technology company specializing in big data analytics and artificial intelligence with a prominent presence in the public sector, has introduced a new platform called the Palantir Artificial Intelligence Platform (AIP). This platform offers organizations large language models and other AI to create a full-fidelity, real-time representation of all the concepts, actions, and decisions within their businesses.
With the AIP, organizations can deploy large language models and other AI within their private networks anchored in private data, allowing them to control which parts of their business the models have access to. Additionally, the platform enables organizations to define ground rules around which data, language models, and AI can and cannot see, and what they can and cannot do on behalf of humans, based on data sensitivity, laws and regulations, and competencies of the models.
In a recent demo, the AIP was used to help a leader at a US-based manufacturing company respond to the forecasted impact of Hurricane Cynthia on one of their major distribution centers in the south. The AIP terminal allowed the leader to ask questions and collaborate with large language model Burrell to determine the impact on customer orders and associated revenue.
Using the AIP, the leader was able to simulate a baseline scenario and determine the impact of shutting down the affected distribution center on backlogs, revenue, and commitments. The AIP offered three simulated courses of action, alongside estimated costs, to charter additional transportation for order fulfillment. Option B, a 20% truck capacity increase, was determined to have the best cost-benefit trade-off and was implemented.
To ensure the LM was grounded in reality, the AIP connected the LM with trusted models across the business, including forecasting optimization, machine learning, and standard procedures. AIP then facilitated a secure handoff between these models as needed, which kept each model in its own lane. All stakeholders approved the plan, and the operational team was authorized to monitor and adjust as necessary.
The AIP Control Panel enabled the organization to set guardrails and limits per monitor, including which data objects they could see, which actions they could recommend on behalf of humans, which tasks and workflows they were trusted to drive, who could use them, and how costs were attributed. All of these capabilities were accomplished without a single line of code, using natural language within the AIP.
