2 owns · 1 partial of 5 — a pattern, not a score
AI adoption: deepPalantir's quarter ended June 30, 2026 shows 1,049 trailing-twelve-month customers, up from 849 a year earlier, on quarterly revenue of $1.94 billion, a 93% increase, with US commercial revenue of $764 million up 149%. The depth sits in expansion rather than logo count: average trailing-twelve-month revenue across the top twenty customers reached $124 million, up 67%, and $407 million of the $495 million commercial revenue increase came from customers already on the books at December 31, 2025. AIP, deployed since 2023, adds an agent and automation toolchain and an evaluations framework for governing AI workflows in production atop the Foundry Ontology. The installed base stays narrow at roughly a thousand customers, and remaining performance obligations stood at $4.9 billion. source: sec.gov
Palantir owns the customer relationship and the workflow, holds a partial position on evaluations, and deliberately owns neither the data nor the models. It sells direct to 954 customers and positions its platforms as the central operating system for their daily work, while acting as a data processor over corpora walled off per engagement and routing to third-party or open-source LLMs. The edge is the Ontology, permissioning and deployment layer between those customer-owned inputs and the operational decisions.
| Customer relationship | owns | Palantir sells through a "Direct Sales Force": the FY2025 10-K says it has "invested and may continue to invest in an account-based sales force to identify and capture new customers" and that "We embed directly with customers across numerous industries", with AIP bootcamps that "deliver real workflows on actual customer data in days". It reported 954 customers as of December 31, 2025, a customer being "an organization from which we have recognized revenue during the trailing twelve-month period". Two caveats sit against full first-party control: Japan and Korea joint ventures that "limit our ability to independently sell our platforms", and "channel sales relationships and strategic alliances with various global system integrators" whose partners may take on "some portion of sales, marketing, implementation services". source: sec.gov |
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| Proprietary data | none | Palantir disclaims ownership of customer data: "With regards to customer data, we act as a data processor, not a data controller", "We do not collect, store, or sell personal data", and "We don't use personal data to train proprietary AI or machine learning models to share or resell to other customers." That post dates from 11 November 2020, but the current AIP security documentation says the same today: Palantir "secures strict technical and contractual guarantees from third-party-hosted model service providers to ensure that no customer data submitted in prompts or contained in completions is used for model training". The FY2025 10-K is consistent — the platforms "integrate our customers' data" and deploy "in customer networks, on-premises at customer sites, on edge devices". Any retraining stays in the customer's deployment. source: blog.palantir.com |
| Evals | partial | Palantir ships a real first-party evaluation harness, named in the 10-K as "a broad evaluations framework for governing AI workflows in production", but the criteria are authored per customer. Users "Set up tests manually to create an empty evaluation suite" in a test editor "where you can configure inputs for each test case", and the shipped evaluator is generic: teams "use the built-in Exact string match evaluator", while "depending on the nature of your function, you may need to use other evaluators or write custom ones." AIP can bootstrap a starting point — "select Generate evals, if your Logic function is eligible, to have AIP bootstrap useful tests and evaluators for you" — but that is scoped to one customer's function rather than a published domain standard. source: palantir.com |
| Workflow | owns | The Q2 FY2026 10-Q, for the quarterly period ended June 30, 2026, states that "The Company builds and deploys software platforms that serve as the central operating systems for its customers", and the FY2025 10-K says "our platforms have become central operating systems for entire industries and sectors". The product description backs this at the level of daily work: "data engineers can integrate new data sources, analysts can clean and transform data, data scientists can write models, business users can conduct daily workflows, and senior leaders can make critical decisions". Gotham "facilitates the hand-off between analysts and operational users, helping operators plan and execute real-world mission plans." "Central operating system" is the filing's own characterisation, so the band rests on that task list rather than the phrase. source: sec.gov |
| Domain-specific model | none | AIP routes to models Palantir did not train: it "supports a wide range of LLMs (large language models) and text embedding models from leading providers, including xAI, OpenAI, Anthropic, Meta, and Google", covering Grok, GPT, Claude and Gemini alongside an "Open source (Palantir-hosted)" tier including Llama 3.1/3.3, Mixtral 8x7B, Whisper Large V3 and Document Information Extraction. The 10-K describes AIP as providing "secure connectivity to third-party-provided large language models (“LLMs”), a development toolchain for building AI-powered agents and automations", with no reference to fine-tuning or proprietary training data. Customers can also connect "their own LLMs or accounts". The value sits in the Ontology, permissioning and deployment layer rather than in weights. source: palantir.com |