Palantir achieves 85% revenue growth by building a business ontology layer that integrates enterprise data semantics, enabling reliable AI decisions in high-stakes environments, unlike fragile RAG or wrapper approaches that fail when models commoditize.
Palantir's August 18 AIP Analyst update introduces Skills and Analysis Lookup, adding a method-memory layer that lets agents reuse analytical workflows and tool chains instead of just retrieving past content, all grounded in the Ontology framework.
This article dissects Palantir's ontology into six mechanism layers—semantic transparency, constrained query, controlled action, organizational unification, scenario generalization, and governed evolution—showing how each solves a specific AI deployment failure mode, why alternatives fall short at scale, and when the investment pays off.
Palantir provides templates for Claude, OpenAI, and Google agent SDKs that share Ontology resources, authentication, MCP interfaces, and deployment pipelines, standardizing the enterprise integration layer while letting each framework retain its native reasoning loop, revealing that business objects, permissions, and action boundaries—not models—are the enduring foundation for production agents.
This article analyzes eight Palantir Foundry deployments across aerospace, consumer goods, energy, and insurance, showing how ontology-based data integration enables real-time decision-making, cuts costs by millions, and accelerates digital transformation without replacing existing ERP or MES systems.
This article dissects Palantir's Ontology, a semantic operational layer integrating data, logic, actions, and security, explaining its three core primitives (Objects, Links, Actions), layered architecture, differences from knowledge graphs, and demonstrating via Airbus Skywise case how LLMs use OSDK to query and execute actions safely.
Palantir's August 18 update to AIP Analyst introduces Skills and Analysis Lookup, adding a "method memory" layer that lets agents reuse analytical workflows and tool chains rather than just retrieving past content, all grounded in the Ontology framework.
Palantir CEO Alex Karp argues that AI-era competition splits enterprises into those with domain-specific AI-enhanced infrastructure and those without, warning that generic AI adoption creates no competitive advantage while specialization of proprietary knowledge builds unbeatable moats.
Palantir's August 27 update adds Automate tools to AI FDE, enabling agents to create and modify business automations — defining triggers, actions, retries, and fallbacks — within a governed branching and approval system, moving enterprise AI from question-answering to workflow orchestration.
This analysis compares three proven enterprise AI context-building approaches: Glean's knowledge-centric Enterprise Graph, Palantir's decision-centric Ontology, and OpenAI's task-centric Harness framework, showing how each addresses different organizational needs and why context—not models—is the lasting competitive advantage.
Palantir’s August 27 update adds Automate tools to AI Forward Deployed Engineer, letting agents configure business automation, manage conditions and effects, and integrate with Ontology, Action, and Function, while introducing governance via branching and approval, yet still with clear capability limits.
The article analyzes how Palantir turned the commoditization of large‑language models into a competitive advantage by replacing shallow RAG wrappers with a deep ontology‑based semantic network, illustrating the three‑layer AI competition, high‑risk validation, and resulting 85% revenue growth with zero churn.
Palantir's August 18 update to AIP Analyst introduces Skills and Analysis Lookup, turning reusable analysis procedures into callable commands and using historical analyses as templates, thereby extending agent memory with method reuse tightly integrated with the Ontology framework.
In his AIPCon keynote, Palantir CEO Alex Karp argues that the AI era will split firms into two camps—those with domain‑specific, AI‑enhanced infrastructure and those without—emphasizing unfair advantage through deep integration, specialized solutions over generic tools, and the necessity of measurable value creation.
Palantir’s ontology redefines data platforms by centering on decisions, modeling real‑world objects, relationships and actions as a computable network, outlining a step‑by‑step methodology, core principles, and a thinking framework that can be applied beyond the Palantir stack.
The article analyzes Palantir’s 2026 product roadmap—highlighting Ontology, AIP Analyst, Global Branching, and Pro‑code Agent updates—to show how the company is shifting AI budgets from pure model capability to engineered decision‑process automation, and why this matters for Chinese enterprises seeking control of the decision layer.
Based on Tencent's 83-page 2026 report, this article explains why AI projects stall after demos, introduces the Frontline Deployment Engineer (FDE) role that uses bidirectional distillation to turn field experience into reusable assets, and shows how falling costs make the FDE model commercially viable with Palantir as proof.
The article explains how Palantir’s ontology is not pre‑designed but gradually emerges from a massive, long‑running data‑engineering foundation that continuously ingests structured business systems, accumulates common patterns, and turns raw data into a platform‑wide semantic layer that drives actions.
Palantir’s Q2 report shows 93% revenue growth and a 225% profit jump, but the deeper signal is that enterprise AI value now hinges on embedding models into business objects, rules, and workflows through Ontology, a capability that differentiates vendors beyond raw model performance.
Palantir’s August 18 update to AIP Analyst introduces Skills and Analysis Lookup, letting agents store reusable analysis commands and treat past analyses as templates that re‑execute tools on current data, thereby extending Agent Memory from content recall to method reuse within the Ontology‑driven enterprise AI platform.
This article explains the Forward Deployed Engineer (FDE) role, tracing its origins from Palantir to its current critical function in deploying AI models into real enterprise systems, detailing the workflow, required skills, and why FDEs command high salaries in the AI deployment era.
Palantir’s August 18 update to AIP Analyst introduces Skills and Analysis Lookup, adding a method‑memory layer that lets AI agents reuse analysis procedures rather than just past content, while still supporting semantic search and Ontology‑driven data operations.
Palantir's new Agent Stack shifts AI agents from short‑lived model‑prompt loops to a production‑grade architecture that adds state, events, effects, durable execution, observability and ontology, turning agents into reliable, governable software components for real‑world business tasks.
Palantir’s July 2026 update adds three Agent SDK templates for Claude, OpenAI, and Google, unifying them under a shared Ontology layer and scoped permissions, while keeping each framework’s native loop, and explains how this architecture standardizes business objects, actions, and security boundaries for enterprise AI deployment.
Palantir’s new Object Timeline feature aggregates token usage, runtime, waiting time and Agentic Coverage for each business object, turning enterprise AI agents into observable work units and revealing how much work they actually perform, where bottlenecks occur, and what remains human‑driven.
Palantir CEO Alex Karp argues that spending on AI tokens yields no real value, emphasizing that true enterprise AI success hinges on deep, secure infrastructure, data sovereignty, and a robust application layer rather than merely buying model access.
Palantir’s July 2026 update introduces three Agent SDK templates that share Ontology resources, authentication, and MCP interfaces, standardizing business objects, permissions, and action boundaries across Claude, OpenAI, and Google agents, and turning agents into asynchronous, permission‑controlled enterprise functions.
This article explains why Palantir requires ontology binding as the first step for AI agents, describes the OSDK and Ontology MCP toolchain that provide type‑safe access and a standard protocol for external agents, and walks through a minimal agent example with code, permissions, and a key pitfall.
Palantir’s new Object Timeline feature moves AI observability from model‑level traces to business‑object lifecycles, exposing token usage, runtime, waiting time and Agentic Coverage for each object, allowing enterprises to quantify how much work agents actually perform, where bottlenecks lie, and why simple automation percentages can be misleading.
The article explains how treating an ontology as a first‑person digital twin—where entities act, report status, and compute internally—creates production‑grade solutions, while the prevalent third‑person, static view limits implementations to demos, causing usability, performance, and depth problems across global markets.
Palantir’s new SuperRepo embeds Ontology definitions, TypeScript‑declared objects, Functions and React applications into a single monorepo, enabling versioned, testable, and deployable business models that let AI agents operate within a governed enterprise runtime, though the feature is still in beta with notable limitations.
Palantir’s new Object Timeline feature aggregates token usage, runtime, waiting time and Agentic Coverage into a single view, letting enterprises quantify how much work AI agents actually perform, identify bottlenecks, and evaluate automation impact beyond traditional model benchmarks.
Palantir’s SuperRepo extends a traditional monorepo by embedding Ontology definitions, TypeScript‑based Functions and React applications into a single versioned codebase, enabling business models to be authored, tested, reviewed and deployed like software while exposing a controlled runtime for AI agents, albeit still in beta with limited capabilities.
The article analyzes Palantir’s 2026 product roadmap—AIP Analyst, Ontology MCP, Global Branching and Pro‑code Agent—to show how the company is shifting from selling model capabilities to building an engineered decision‑system platform that lets enterprise agents act safely, a trend that reshapes AI budgets and competition, especially in China’s market.
The article argues that enterprise AI agents can access integrated data yet still fail to understand business reality because companies lack a unified organizational operating system—an ontology‑driven common language that aligns objects, relationships, and rules across disparate systems.
The article examines how Palantir’s Foundry and AIP platform unify heterogeneous mortgage data, regulatory rules, documents, and customer interactions through an ontology, enabling AI to move from answering questions to driving traceable, executable business actions within a 90‑day pilot.
The article analyzes Freedom Mortgage's deployment of Palantir Foundry and AIP, showing how a unified ontology links heterogeneous loan data, regulatory rules, documents, and calls so AI can move from answering questions to driving real‑world mortgage processes within about 90 days.
The article analyzes how Freedom Mortgage leveraged Palantir Foundry and AIP to unify heterogeneous mortgage data, embed regulatory rules, and integrate unstructured documents and calls into a traceable, AI‑driven operational workflow, illustrating a shift from isolated models to end‑to‑end enterprise AI.
The article analyzes how Freedom Mortgage leveraged Palantir Foundry and AIP to unify heterogeneous loan‑related data, make regulatory rules traceable, ingest unstructured documents and calls, and evolve AI prototypes into an end‑to‑end operational system within about 90 days.
The article analyzes Palantir’s 2026 Q2 results and explains how its decision‑centric Ontology, forward‑deployed engineering feedback loop, AI Platform (AIP) and result‑oriented, high‑autonomy culture together form a moat that competitors can’t easily replicate, even if they copy individual components.
Palantir’s SuperRepo moves Ontology from a static business‑modeling layer into a version‑controlled code artifact, letting developers define objects, relationships, actions and functions in TypeScript, preview them locally, and ship the whole business semantics together with the application, while still being a beta product with notable limitations.
Palantir’s new SuperRepo merges Ontology definitions, TypeScript functions and React applications into a single monorepo, turning business semantics into versioned, testable code that AI agents can safely execute, while still in beta with notable limitations.
The article analyzes how Palantir's Foundry and AIP platform enable a mortgage lender to unify heterogeneous data, make regulatory rules traceable, and embed unstructured information into workflows, transforming AI from a standalone model into an executable component of core business operations.
Palantir’s Q2 report reveals 93% revenue growth and strong profitability, but the deeper insight is that enterprise AI value now hinges on embedding models into business objects, rules, permissions, and action flows—where Ontology serves as the critical, yet invisible, competitive barrier.
The article analyzes how Palantir’s neuro‑symbolic, ontology‑based AI platform overcomes the fragmentation, broken reasoning chains, and lack of explainability of conventional RAG systems, delivering semantic modeling, auditable multi‑step reasoning, and dynamic business adaptation for enterprise decision‑making.
Palantir’s Q2 2026 results show a 93% revenue jump, but the real story is how its Ontology and AIP platforms turn powerful models into actionable enterprise agents, shifting AI competition from raw model strength to the ability to understand, govern, and execute real business processes.
Palantir's July 2026 release adds three Agent SDK templates—Claude, OpenAI, and Google—while standardizing the Ontology integration layer, credentials, and publishing flow, highlighting that the true enterprise stability comes from modeling business objects, permissions, and action boundaries rather than the underlying AI models or frameworks.
The article analyzes how Freedom Mortgage leveraged Palantir Foundry, AIP, and an Ontology‑driven approach to unify heterogeneous mortgage data, encode regulatory rules as traceable objects, and transform documents and calls into actionable business events, illustrating a path from AI prototypes to end‑to‑end operational systems.
The article dissects why Palantir’s ontology—far beyond simple entity‑relationship diagrams—remains difficult to copy, outlining four barriers (cognitive shift, engineering closed‑loop, decades of extreme‑scenario feeding, and organizational change), tracing its philosophical roots, AI research evolution, and comparing domestic attempts.
Palantir's July 2026 update adds three Agent SDK templates—Claude, OpenAI, and Google—while standardizing the Ontology access layer, scoped permissions, and MCP interfaces, showing that the lasting stability for enterprise Agents lies in business objects, actions, and permission boundaries rather than the underlying frameworks.
The article analyzes Palantir's approach to embedding AI in Freedom Mortgage's core loan processes, showing how Foundry, AIP, and an Ontology layer unify data, rules, and unstructured information into a traceable, executable system within just 90 days.
Palantir’s 2026 roadmap shows the company moving beyond stronger AI models toward a comprehensive engineering system that lets enterprise agents safely access business data, execute permission‑guarded actions, and integrate into decision‑making processes—a shift that reshapes AI budgets and offers a clear lens on the competitive landscape, especially for Chinese firms.
The article analyses how SAP and Palantir embed an AI Agent into the full ERP migration lifecycle to create a closed‑loop of discovery, mapping, transformation and continuous validation, reporting 99% verification accuracy and 70% cost reduction, and argues that Chinese enterprises should build their own intelligent migration control layer rather than merely copying vendor solutions.
Palantir’s mortgage AI case shows how its Foundry and AIP platforms use Ontology to unify heterogeneous data, link regulatory rules, and transform documents and calls into actionable business objects, enabling AI to move from simple answers to end‑to‑end operational support within 90 days.
The article analyzes Palantir’s Agent Stack—Orchestrator, observability, optimization, and Ontology—explaining how moving AI agents from chat interfaces to long‑running production tasks raises challenges of state management, fault handling, permission control, and trust, shifting the focus from model capability to enterprise‑grade infrastructure.
The article analyzes Palantir's Agent Stack, showing that while building capable AI agents is now easy, enterprises hesitate to deploy them at scale because trust, state persistence, observability, and governance must be engineered through durable orchestration, ontology mapping, and continuous optimization.
The article analyzes why less than half of enterprise AI initiatives succeed—citing unrealistic boss expectations, misaligned IT and business goals, and unchanged incentives—and explains how the Forward Deploy Engineer (FDE) model, originally from Palantir, can reshape productivity, business systems, and organizational structures with three concrete solutions.
The article analyzes Palantir's Foundry and AIP approach in a mortgage‑lending case, showing how unified business context, rule traceability, and ontology‑driven document and call processing turn AI from a standalone model into an end‑to‑end operational system.
The article explains how Palantir’s Foundry and AIP combine data integration, ontology‑based business context, and rule management to turn large‑model AI into executable intelligence, illustrated by Freedom Mortgage’s 90‑day rollout of compliance, document, and call‑handling applications that link rules, documents and customer interactions into a unified, actionable system.
Enterprises often have large models and data platforms, yet integrating AI into core operations hinges on unifying business context, rules, and real data; Palantir’s Foundry, AIP, and Ontology approach, demonstrated by Freedom Mortgage’s 90‑day rollout, shows how AI can become a traceable, executable part of workflow.
The article analyzes Palantir’s AI‑driven closed‑loop system—illustrated by World View’s stratospheric platform—showing how real‑time perception, decision making, execution, ontology‑based memory, and swarm‑scale orchestration transform AI from a data analysis tool into a core operational infrastructure.
At Palantir AIPCon 9 the US Navy unveiled Ship OS, an AI‑native operating system that injects commercial AI into shipyards, automates change‑notification workflows, creates an intelligent inbox, and drives dynamic production planning, cutting decision cycles from days to minutes.
At Palantir AIPCon 9 the US Navy unveiled Ship OS, an AI‑native operating system that injects commercial AI into shipyards, automates change‑notice processing and communication, and compresses multi‑day decision cycles into minutes while preserving existing industrial workflows.
Between May and July 2026, Anthropic, OpenAI, AWS, and Microsoft each poured billions into Forward Deployed Engineering, revealing that as AI models grow stronger, enterprises still face on‑site process, data, and organizational challenges that only embedded engineers can resolve.
Palantir CEO Alex Karp argues that the AI era divides companies into two camps—those with AI‑enhanced, domain‑specific infrastructure and those without—emphasizing that true advantage comes from embedding unique tribal knowledge into specialized AI rather than relying on generic large models.
Palantir CEO Alex Karp argues that in the AI era firms must embed specialized, AI‑enhanced infrastructure into their core processes or face extinction, emphasizing that generic AI is a trap and that true competitive advantage comes from domain‑specific knowledge and practice‑driven methodology.
The article argues that the coming years will be a golden period for Agentic AI as intelligent agents evolve into an AI operating system that can decompose tasks, coordinate multiple agents, and fundamentally transform enterprise productivity, supported by emerging token economics, ontology‑driven infrastructure, and predictions from Gartner and industry leaders.
Palantir CEO Alex Karp argues that the AI era splits enterprises into those that embed AI‑enhanced infrastructure and those that don’t, warning that relying on generic tools or “parasitic” software leaves companies without a sustainable competitive edge and risks being hollowed out.
The article examines costly data‑platform failures, contrasts traditional data‑middle‑platforms with Palantir’s ontology‑driven decision system, showcases real‑world ROI examples, and breaks down the three‑layer semantic‑dynamics‑decision architecture that turns data into actionable business outcomes.
The article examines why costly data middle platforms often become "data swamps," cites real‑world failures, and shows how Palantir's ontology‑driven three‑layer architecture (semantic, dynamics, decision) can transform read‑only data warehouses into automated decision engines delivering triple‑digit ROI.
The author reviews a 15‑year journey from low‑level industrial communication protocols through platform‑level object models to AI‑driven knowledge graphs, concluding that Palantir’s ontology builds on concepts that have existed in industrial data modeling for over a decade.
The article analyzes Palantir's shift from traditional OWL knowledge representation to a dynamic, secure, and AI‑enabled enterprise operating system, detailing philosophical, architectural, capability, security, AI, and business layers, and highlighting concrete upgrades and real‑world examples.
The article examines why ontology has become a buzzword in China's AI era and how a rigorous ontology‑driven approach can unify semantics, bridge business object hierarchies, and influence the technical, data, application, and security layers of high‑end ERP systems, contrasting domestic solutions with SAP and Palantir.
Palantir’s AIP platform tackles the four rigid constraints of classified warfare—data silos, network isolation, strict permissions, and irreversible consequences—by delivering a compliant, auditable large‑model framework that fuses data, enforces fine‑grained access, and provides full‑traceability, demonstrated through an end‑to‑end Eastern‑European border combat simulation.
The article presents a six‑level capability ladder for ontology‑based intelligence, compares Palantir Foundry’s strengths at each level with OntoFlow’s features, and explains how OntoFlow’s World Runtime and emerging World OS aim to move beyond data visualization toward dynamic, time‑aware, causal simulation for supply‑chain, logistics, risk and military scenarios.
The article examines Palantir’s 85% Q1 2026 revenue growth, argues that commoditized large‑model capabilities erode traditional AI moats, and explains how Palantir’s ontology‑based infrastructure layer creates a durable competitive advantage in high‑risk enterprise scenarios.
The article analyzes how the rapid commoditization of large‑model capabilities, illustrated by Palantir’s 85% Q1 2026 revenue growth, reshapes AI competition into three layers—model, wrapper, and infrastructure—highlighting ontology as the hard‑to‑copy moat for enterprise AI in high‑risk scenarios.
The article argues that in fast‑moving AI markets, a firm’s organizational shape—not its product or technology—has become the most durable competitive advantage, illustrating this with examples from OpenAI, Anthropic and Palantir.
The article analyzes how Palantir leverages its defense‑originated data platform, frontline deployment engineers, and generative AI to achieve 120% commercial growth, illustrated by a Mixology Clothing case that turned a $9 loss per item into a $9 profit, while emphasizing strict data‑governance and value‑filtering as a competitive edge.
The article analyzes Palantir's 2026 Q1 surge and argues that as large‑model capabilities become cheap commodities, true competitive advantage now lies in deep ontology‑based infrastructure that makes AI outputs trustworthy in high‑risk enterprise scenarios.
The article chronicles Palantir’s two‑decade saga of high‑profile setbacks—from a $5 billion, six‑year military AI project and a failed financial platform to stalled consumer data alliances—showing how advanced algorithms falter when detached from real‑world business needs.
The article compares Anthropic's lightweight, knowledge‑externalizing AI approach with Palantir's heavyweight data‑semantic and governance platform, arguing that Chinese B‑end firms should initially adopt Anthropic‑style quick‑value layers and later integrate Palantir‑style controls to build a sustainable enterprise AI operation layer.
Palantir’s Q1 2026 revenue jumped 85% while many AI firms saw valuations collapse, and the company attributes its success to replacing cheap‑token LLM wrappers with a deep ontology‑driven semantic network that secures high‑risk AI deployments, creates a durable moat, and delivers unprecedented net‑retention.
Palantir’s Q4 2025 revenue jumped 70% to $14.07 billion, with U.S. commercial revenue soaring 137%, driven not merely by AI hype but by its Ontology‑centric approach that tightly integrates data, business logic, actions, and security, locking large enterprises into a deeply embedded decision‑making stack.
The article analyzes how the rapid commoditization of large‑model capabilities reshapes AI competition, arguing that the true moat lies not in the models themselves but in deep ontology‑driven infrastructure that can guarantee trustworthy outcomes in high‑risk enterprise scenarios, as illustrated by Palantir’s strategy.
Amid rapidly commoditized large‑model capabilities, Palantir achieved an 85% YoY revenue surge and zero churn by replacing generic RAG approaches with a deep enterprise ontology that unifies business semantics, creating a durable infrastructure moat while other AI firms see valuation collapse.
As large‑model capabilities become commoditized, Palantir argues that the true moat lies in its ontology‑driven infrastructure, which integrates business semantics to ensure reliable AI in high‑risk contexts, a strategy reflected in its 85% Q1 revenue jump and a three‑layer AI competition model.
The article examines costly failures of conventional data middle platforms—such as a $40 million payroll system flop and a chemical firm’s data‑cleaning bottleneck—then shows how Palantir’s ontology‑driven approach delivers triple‑digit ROI for BP, 98% R&D efficiency for Novartis, and $14 million annual savings for General Mills, highlighting the three‑layer semantic, dynamics, and decision architecture that turns data into actionable decisions.
The article argues that while Palantir’s technical stack can be emulated, its Forward‑Deployed Engineer model relies on scarce talent, political capital, and decades of industry know‑how, making true replication impossible.
In a 90‑minute round‑table, experts from banking risk control and cloud observability explain how Palantir’s ontology—viewed as the skeleton and memory that structures massive, heterogeneous data—bridges three data gaps, enables large‑model reasoning, and offers concrete steps for building practical knowledge graphs in enterprises.
The article dissects why China cannot replicate Palantir by analysing the four‑layer ecosystem—political, institutional, capability and cultural—that enabled Palantir’s subscription‑based, long‑term AI platform, and proposes alternative paths suited to China’s market and regulatory soil.
Healthpeak transformed its commercial‑real‑estate operations by replacing fragmented spreadsheets with Palantir’s AI Platform (AIP), using a four‑layer architecture and ontology‑driven modeling to automate billing, detect anomalies, and orchestrate workflows, dramatically cutting manual effort, errors, and scaling costs.
In a 90‑minute round‑table, experts from banking risk control and cloud observability explain how Palantir’s ontology bridges three data gaps, turns raw logs into a graph of entities and relationships, and works with large models as a skeleton and memory to make AI trustworthy and scalable.
A panel of industry experts dissected Palantir’s rapid growth, revealing that its advantage lies in a systematic ontology‑driven methodology rather than exclusive technology, and argued that Chinese firms can adopt the same approach if they first resolve data governance, semantic consistency, and management challenges.
Palantir leverages its Ontology‑driven data integration and AI platforms—Gotham, Foundry, and AIP—to transform fragmented data into actionable intelligence, delivering decision‑making advantages in government, aerospace, food, and energy sectors, while shifting from custom‑heavy services to an open, platform‑based ecosystem.
The article dissects Palantir’s rise—backed by In‑Q‑Tel, F‑class shares, and a subscription model—showing how the U.S. political‑capital ecosystem created a unique AI powerhouse that China’s project‑based procurement, legal constraints, and capital structure cannot emulate, and proposes a vertically‑focused, long‑term AI strategy suited to China’s own soil.
The article analyzes how Palantir’s ontology‑driven platforms—Gotham, Foundry, and the 2023 AI Platform (AIP)—break data silos, enable real‑time decision making, and shift the company from custom‑heavy solutions to a low‑code, AI‑agent‑centric ecosystem, illustrated with military, aerospace, and retail case studies.
The article analyzes Palantir’s core platforms—Gotham, Foundry, and the AI Platform (AIP)—explaining how its ontology‑driven data integration and low‑code AI Agent capabilities break data silos, accelerate decision‑making, and create measurable business value across government, defense, and commercial sectors.
Healthpeak, a large medical‑real‑estate REIT, replaced fragmented spreadsheets and manual data entry with Palantir AIP’s ontology‑driven AI operating system, achieving automated billing, voice‑driven workflows, reduced errors, and a scalable, data‑centric operation that frees managers to focus on tenant relationships.
In a 90‑minute round‑table hosted by DataFun, experts from banking risk control and cloud observability dissect how Palantir’s ontology—structured as a graph that links entities, metrics and logs—complements large‑model AI, solves data chaos, and becomes the practical backbone for trustworthy enterprise AI.
