Three Reasons Why Skypoint Tailored the “Palantir Model” for Enterprise AI in Production

In the world of enterprise AI applications struggling to get to production and the rise of compound AI systems there’s a growing need for more than just data & AI tools ("logo soup"), CIOs are tired of logo soup thrown at them by vendors and a vast majority of SaaS tools companies spend 40% of their revenues on sales & marketing, not engineers.

Enterprises are looking for trusted partners who not only provide AI solutions but also commit to achieving tangible outcomes. This is why Skypoint AI Platform (AIP) has chosen a path inspired by the Palantir Technologies model— a high-touch, product-oriented, deeply integrated to customer and industry processes approach that goes beyond the traditional “one-size-fits-all” software as a service (SaaS) model.

What is the Palantir Model?

The Palantir model differs from traditional Software-as-a-Service (SaaS) by focusing on customized, outcome-focused solutions designed specifically for complex, data-heavy industries and use cases. In addition to offering a standardized software platform, Palantir works closely with clients to tailor solutions that fit their unique requirements. This approach includes embedding software engineers, product managers, data engineers, and AI engineers within customer teams to ensure seamless integration and alignment with their business goals. Palantir’s model is known for its deep partnerships, long-term relationships (less sales & marketing people, more engineers with customers), and commitment to outcomes rather than just product functionality.

At Skypoint , we’ve embraced this model for enterprise AI, as it aligns with the unique demands of regulated industries like healthcare, public sector, and financial services. Here’s why we believe the a tailored version ("Service as Software") of Palantir model is the best approach for deploying AI in production.

1. The Horizontal SaaS Model Doesn’t Scale for Enterprise AI

In the typical SaaS model, companies offer a broadly applicable solution designed for ease of scalability, supporting clients across diverse industries. However, when it comes to enterprise AI, this standardized approach often falls short, especially when it comes to managing and integrating unified data. Enterprise clients in sectors like healthcare need AI solutions that interact seamlessly with complex, siloed data systems. In traditional SaaS, data often remains disjointed, creating barriers to effective AI applications.

By adopting an outcome-centric, service-as-software ("Skypoint model"), Skypoint AI Platform goes beyond a generic SaaS solution. We work directly with our clients to understand their data landscapes and unify their data to drive accurate, actionable AI insights. This high level of customization is essential for enterprise AI, where unique workflows, regulatory requirements, and data complexity are the norms rather than the exceptions. Our approach ensures that Skypoint AI Platform (AIP) solutions are tailored, reliable, and impactful from day one, helping clients achieve meaningful outcomes that standardized SaaS tools simply cannot deliver.

2. AI Now Writes 25% of All Code - Accelerating Customization at Scale

With AI now able to handle up to 25% of coding tasks (forecasted to increase up to 50%), we’re in an era where development cycles can be significantly shortened. This allows us to adopt a customized approach of Skypoint AI Platform (AIP) without the typical time or resource constraints. In the Skypoint’s model, we use AI-driven coding to produce robust solutions quickly, which allows our engineers to focus on tailoring these solutions to meet the unique demands of each client and continue to enhance our product for each industry and use case.

The Palantir model’s value shines here with something we call forward-deployed engineering. In this approach, our engineers don’t just develop software in isolation—they work directly with our clients, building and refining solutions and enhancing the product that align precisely with the client’s needs. These forward-deployed engineers are partners in problem-solving, helping our clients harness AI for their specific goals. The result is not just faster development but smarter solutions that deliver tangible, enterprise-grade outcomes. AI-driven coding boosts our efficiency, but it’s this forward-deployed, hands-on work that makes the impact truly meaningful.

3. SaaS v2: Moving from “Software as a Service” to “Service as Software”

As the market for AI matures, enterprises are seeking more than software. They want partnerships where vendors take accountability for outcomes, not just software functionality. Traditional SaaS provides a platform but often leaves clients to figure out how to derive value from the software. By contrast, the Skypoint model, is about delivering or being a factory for “service as software” applications - we don’t just deliver a tool; we commit to achieving results alongside our clients.

Under the service-as-software model, Skypoint integrates support and expertise directly into software delivery, creating a trusted partnership. This model aligns our incentives with the client’s success, allowing us to adjust, iterate, and improve as we go. By standing beside our clients and taking accountability for real outcomes, we build lasting relationships rooted in shared success. This approach not only aligns with our values but also creates an environment where clients feel confident they are getting value from their AI investments.

Why the Skypoint Model is the Right Fit in the AI Era

With AI’s capabilities accelerating, the Skypoint / Palantir model has emerged as the most effective way to deliver enterprise AI in production. The traditional horizontal SaaS model may be ideal for mass-market software, but it simply doesn’t provide the level of customization, accountability, and partnership that today’s enterprises demand for AI. With forward-deployed engineers, customized AI solutions utilizing Skypoint AI Platform (AIP), and a focus on accountability, Skypoint’s outcome-centric model—built around empowering forward deployed engineer, not sales people selling SaaS tools - ensures that clients get not only the technology they need but the strategic partnership required to turn AI into real value.

By choosing a model that emphasizes tailored solutions that scales and accountability, Skypoint is ready to meet the challenges of deploying AI in complex, regulated industries. This isn’t just a business decision; it’s a commitment to helping our clients unlock the full potential of AI in a way that drives measurable, meaningful outcomes.