Editor’s Note: AIPCon 8, Palantir’s most recent customer conference, featured breakthrough customer implementations that demonstrate what’s possible with enterprise AI today. In part one of this two-part series, we share highlights from the afternoon’s standout demo sessions.
Those who attended AIPCon 8 all walked away with a shared experience — seeing firsthand the power of transformative AI when applied to real-world enterprise challenges. While some of this was captured on the AIPCon 8 livestream, many of these innovations were showcased off screen during the afternoon programming. For those who couldn’t witness it live, we’re sharing a roundup of the standout demos that illustrate how our partners are dominating across industries.
From healthcare systems to motorsport teams, defense manufacturers to construction companies, our customers presented groundbreaking implementations that represent the cutting edge of what’s possible with Palantir Foundry and AIP. Attendees experienced use cases that covered a broad spectrum of product implementations, including a software-enabled de-energizing program that transforms wildfire mitigation, a computer vision workflow built to optimize product placement across retail store shelves, and secure, mission-ready AI for sensitive US government workflows.
Across industries, customers are leveraging our technology to solve complex operational challenges, drive unprecedented efficiency, and create entirely new competitive advantages. Each demo represents a distinct approach to enterprise transformation — and together, they paint a picture of AI’s limitless potential when paired with a rich, enterprise-specific Ontology.
Andretti’s RaceOS — Precision Analytics at Maximum Velocity
Andretti’s RaceOS represents perhaps the ultimate stress test for enterprise AI — supporting rapid, data-driven decisions that transform session data into actionable intelligence. Their implementation transforms the scale and complexity of IndyCar telemetry into a unified operating system, enabling them to excel both on and off track.
High-Frequency Data Architecture and Sensor Integration
“IndyCar is extremely data-rich,” noted Zack Porter from Andretti Global as he kicked off the demo, revealing the technical complexity underlying modern motorsport operations. Car telemetry alone captures tens of thousands of data points per second across thousands of sensor channels. Each sensor generates hundreds of discrete data streams with sampling frequencies ranging from 10 to 1000 Hz, depending on critical parameters across wheel speed, suspension travel, tire pressure, and engine performance.
Beyond telemetry, teams must synthesize contextual intelligence from multiple disconnected data sources containing both structured and unstructured information, such as weather pattern analysis, tire performance degradation models, driver feedback transcriptions, and historical track session archives. When extended across years of accumulated racing data, teams face an exponential data complexity challenge: miles of valuable information that traditionally requires manual analysis and interpretation under extreme time constraints between sessions.
High-Speed Intelligence Integration Through Ontology Architecture
“As my colleagues say, we were big data before big data was a thing,” Porter observed when addressing what their data landscape looked like before Palantir. While Andretti is provably adept at operating in this fast-paced, data-rich environment, they required an intelligent platform that enabled rapid understanding and decision augmentation between sessions. RaceOS integrates car setup configurations, driver feedback, and session telemetry data through Palantir’s unified Ontology, accelerating race engineers’ analytical capabilities and enhancing pre- and post-session decisions throughout the race weekend.
What traditionally required parsing multiple disconnected data sources during high-pressure intervals between track sessions now delivers consolidated, actionable intelligence through automated data synthesis and contextual analysis algorithms.
Rapid Post-Session Strategy Optimization
The platform’s sophisticated intelligence capabilities manifest through streamlined operational workflows:
Ultra-Fast Data Integration: In the minutes it takes the team to walk from the pit lane back to the team truck after a session, RaceOS has already ingested, transformed, and cleansed all session data. The information is contextualized within Andretti’s unique Ontology, with predictive models deployed against this data to identify anomalies, opportunities, and priorities for the next outing.
Contextual AI Analysis: AIP enables immediate contextualization of session data streams, including direct mapping of driver feedback to corresponding telemetry data, setup configurations, and on-track performance metrics. This creates instant assessment capabilities for setup decision impact analysis, car performance issue identification, and strategic optimization opportunities ahead of the next session or critical pit stop windows.
The system represents a breakthrough in unstructured data fusion — coalescing driver radio communications with high-frequency time series telemetry and structured lap-to-lap timing data through Andretti’s proprietary Ontology architecture, all processed and analyzed in minutes rather than hours or days.
Performance Validation and Competitive Impact
“As a driver, you get a very short period [between sessions] to actually focus on the data that’s in front of you… we have so much data to look at, and it’s crucial to pinpoint and find exactly what you need,” driver Kyle Kirkwood explained, underscoring how RaceOS maximizes his most valuable asset: time.
The technical sophistication of the platform also earned recognition from Kirkwood, who noted during the demo: “The software is almost as fast as I am” — a testament to the system’s ability to deliver insights at unprecedented speed while processing exponentially more data variables than any engineer could analyze manually in the brief gaps between sessions.
Strategic Transformation in Data-Intensive Environments
The result represents a fundamental shift in racing team data utilization: from lengthy post-session analysis to near-instantaneous strategic intelligence, and from fragmented insights to unified competitive intelligence platforms delivered within minutes of session completion. RaceOS doesn’t merely process data — it transforms raw information streams into measurable competitive advantages through intelligent automation and predictive modeling.
With Palantir AIP, Andretti is establishing new benchmarks for rapid strategic decision-making in one of the world’s most data-intensive, time-critical operational environments, demonstrating how advanced analytics can deliver tangible performance outcomes in the crucial minutes between track sessions.
→ See Andretti’s live demo during the AIPCon 8 Pre-Show.
Nebraska Medicine’s Latest RCM Innovation — Automated Clinical Documentation Intelligence
AI has dramatically transformed the traditional framework of revenue cycle management (RCM), evolving from reactive systems to an agentic, intelligent platform. What began with payers leveraging AI to automate claim denials has evolved to include appeals management, utilization review, charge capture, prior authorizations, and documentation quality. Now we’re witnessing the next evolution: fully integrated, end-to-end automation architectures that don’t just respond to payer tactics but anticipate them, streamlining workflows, strengthening complex case resolution, and revolutionizing case management from the ground up.
Intelligent Medical Necessity Validation at Scale
Jana Danielson, Vice President of Revenue Cycle Management at Nebraska Medicine, showcased one of their latest innovations in RCM at the AIPCon 8 Demo Expo: intelligent automation for medical necessity validation and reimbursement optimization. She demonstrated the Guideline Evaluation workflow, a breakthrough solution built in partnership with Palantir in just 10 hours that exemplifies how targeted automation can solve critical operational challenges in healthcare administration.
The system addresses the inherently complex, error-prone process of medical necessity validation through sophisticated intelligence. The automated solution performs comprehensive analysis of patient medical records and billing information, cross-referencing clinical data points against dynamic insurance company requirements to identify the most accurate diagnostic codes and maximize reimbursement potential.
Technical Architecture and Operational Impact
This approach significantly reduces claim rejection rates while streamlining documentation processes for clinical staff. By automating what was previously extensive manual review work, the system enhances coding precision, optimizes insurance reimbursements, and reduces administrative overhead costs. This intelligent automation creates measurable improvements in both financial performance and patient care delivery by removing bureaucratic bottlenecks, enabling healthcare teams to redirect their clinical expertise toward treating patients rather than managing complex billing requirements.
The Guideline Evaluation workflow leverages Nebraska Medicine’s unified Ontology infrastructure, which consolidates operations across patient flow management, nurse allocation systems, clinical supplies management, and revenue cycle operations at enterprise scale. This integrated data foundation unlocks cross-functional optimization opportunities spanning clinical research and direct patient care, creating systematic improvements in patient outcomes through enhanced operational intelligence.
Rapid Enterprise Deployment and Strategic Scaling
Nebraska Medicine operates at unprecedented implementation velocities. This workflow represents the latest evolution in Nebraska Medicine’s strategic collaboration with Palantir, which began in January 2024 and rapidly scaled from a single use case to a comprehensive enterprise deployment within just six months. The partnership now encompasses 20+ distinct use cases across the health system, each architected upon Nebraska Medicine’s centralized Ontology with a strategic focus on addressing critical operational challenges in healthcare administration.
This accelerated deployment establishes Nebraska Medicine as Palantir’s fastest time-to-enterprise commitment of any health system partner to date, demonstrating both the technical sophistication of their implementation approach and the measurable operational impact achieved through integrated, AI-driven healthcare intelligence platforms.
Hospital for Special Surgery’s (HSS) Patient Card — Unified Healthcare Intelligence
Hospital for Special Surgery (HSS), ranked #1 in orthopedics for 16 years and #3 in rheumatology, provides specialized care to more than 200,000 patients, performs over 40,000 surgical procedures each year, and treats patients from all 50 states and more than 100 countries. Their high volume of care and dedicated focus on preventing clinical care progression blockers — such as cancellations, delays, and chaotic schedule movements — from disrupting the patient journey presents a complex web of upstream and downstream impacts on projected timelines. Unlike traditional healthcare systems, HSS operates under a differentiated business model where customer satisfaction serves as the primary operational driver, fundamentally reshaping how patient data flows through their care ecosystem.
To keep pace with their operations and synthesize their complex data ecosystem, HSS is building Patient Card, a comprehensive patient journey visualization and proactive care coordination tool that functions as a single sheet of music — capturing clinical attributes that define each patient’s persona and surfacing the most critical patient information to drive their experience at every step of the care journey.
Solving Healthcare’s Data Fragmentation Challenge
Mollie Morelli, who leads the Palantir engagement at HSS, demoed how Patient Card addresses one of healthcare’s most persistent technical challenges: the siloed nature of hospital operations. Today’s healthcare landscape is characterized by scattered patient data that remains difficult to digest and is rarely carried forward between care episodes. This architectural fragmentation creates systemic inefficiencies, redundant patient questioning, and missed opportunities for optimal care delivery.
Patient Card leverages sophisticated AI algorithms to synthesize clinical, operational, and financial data across HSS’s Ontology, creating a unified intelligence layer that proactively identifies information gaps and automates critical care alerts. At each patient appointment, the system automatically performs contextual analysis — highlighting what data is missing, what information has become outdated, and what newly relevant factors have emerged — ensuring the right data is captured at precisely the right time in the care continuum.
Technical Implementation and Clinical Impact
The platform’s intelligent flagging system was demonstrated through a compelling use case: a patient with high BMI. The Patient Card’s algorithmic assessment recognized the BMI score as a clinical anomaly and automatically flagged a missing order for a lifestyle management referral. “This is just one way the Patient Card is helping clinicians gain immediate access to comprehensive patient contexts without navigating multiple systems or undertaking redundant data gathering,” Morelli explained.
Beyond reactive alerts, the system provides proactive care orchestration through timeline-based patient experience mapping. The platform can dynamically generate explanatory timelines that outline upcoming care phases — from pre-operative preparation through post-operative recovery — creating structured interaction frameworks that enhance both provider workflow efficiency and patient understanding of their care journey.
Transformational Outcomes
The transformation extends beyond efficiency gains into measurable care quality improvements. Care teams will be able to make informed decisions faster through consolidated data access, while streamlined information flow delivers more coordinated, personalized care to patients. The Patient Card represents a reimagined paradigm where technology truly enhances both provider capabilities and patient outcomes, setting new standards for connected, intelligent healthcare delivery through unified data orchestration and proactive care coordination.
A Leading Retailer’s Intelligent Product Development — Customer-Centric Innovation at Scale
A leading retail pharmacy chain demonstrated how advanced data integration architectures can revolutionize retail product development workflows from initial concept ideation through customer satisfaction optimization. Their sophisticated implementation creates unprecedented visibility across the entire product ecosystem, integrating comprehensive product lifecycle management with customer-centric intelligence that transforms traditional retail development paradigms.
Unified Data Architecture for Product Intelligence
The platform addresses a fundamental challenge in retail operations: the systematic fragmentation of product development functions across organizational silos. By unifying disparate data sources — including financial KPIs, technical product specifications, design attribute databases, and real-time customer feedback — through a centralized Ontology, this leading retailer has eliminated traditional barriers between cross-functional product development teams.
This integrated architecture enables seamless data flow across operational domains that previously lacked strong connections, creating a unified intelligence layer that bonds market research, product design, quality assurance, and customer experience into a cohesive analytical framework.
Advanced Analytics and Computer Vision Integration
AIP and computer vision technologies deliver sophisticated multi-dimensional analytics through intuitive dashboards, enabling rapid organizational response to evolving consumer preferences and market dynamics. The platform leverages machine learning algorithms to process complex product attribute data, consumer sentiment analysis, and market performance metrics simultaneously, creating actionable intelligence that drives strategic product development decisions.
The computer vision integration provides automated quality assessment capabilities, enabling real-time analysis of product design elements, packaging effectiveness, and visual brand consistency across extensive product portfolios. This technical integration ensures systematic quality control while accelerating product development timelines through automated validation processes.
Predictive Market Intelligence and Agile Development
The platform fundamentally transforms product development from a series of disconnected, reactive processes into a cohesive, data-driven operation that proactively anticipates market opportunities. Teams can rely on clean and high-signal data to surface emerging market needs, optimize product attributes based on comprehensive customer data analysis, and ensure consistent brand quality standards — all while maintaining organizational agility to adapt quickly to dynamic consumer demand patterns.
Strategic Transformation in Retail Operations
The implementation represents a paradigm shift in retail intelligence architectures, systematically connecting all aspects of product development workflows to actual customer needs and real-time market dynamics. This creates measurable improvements in product-market fit, reduces development cycle times, and enhances customer satisfaction metrics through data-driven decision-making frameworks.
The result is intelligent retail product development that doesn’t merely respond to market conditions but anticipates them, creating sustainable competitive advantages through sophisticated data integration and predictive analytics capabilities that redefine how retail organizations approach product innovation and customer experience optimization.
The Enterprise AI Revolution in Action
These implementations represent more than technical achievements — they illustrate how AI becomes truly transformative, driving measurable outcomes when applied to specific industry challenges with precision and depth. Each demo showcases a different dimension of enterprise intelligence: real-time competitive advantage, unified operational visibility, integrated product development, and intelligent process automation.
What makes these solutions remarkable isn’t just their technical sophistication, but their practical impact on the humans and organizations they serve. From race engineers making championship-deciding calls, to clinicians delivering better patient care, these platforms demonstrate AI’s potential to augment human expertise rather than replace it.
The objective of Palantir’s enterprise AI isn’t about flashy demonstrations — it’s focused on building systems that solve real problems, deliver measurable value, and scale across complex organizational environments, evolving and shaping to meet changing demands. These AIPCon 8 demos prove that the future is already here.
Ready to explore what transformative AI implementation could look like for your organization? Connect with our team to discover how these proven approaches might apply to your industry’s unique challenges.
Inside the AIPCon 8 Demos Redefining the Future of Enterprise AI was originally published in Palantir Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.