At Pure Accelerate 2026, Everpure unveiled a major shift in how AI infrastructure is built. The company introduced a data-primacy architecture that puts data — not applications — at the center of the stack. CEO Charles Giancarlo described the move as a transition from app-centricity to data primacy, and with it comes a new set of data primacy tools designed to help you rethink your enterprise data strategy. These tools aim to simplify how AI systems access, manage, and use data at scale, marking a clear departure from traditional approaches that prioritize applications over the data they run on.
This isn’t a minor update. It’s a fundamental rethinking of data architecture for the AI era, one that could reshape how you approach AI infrastructure going forward.
Understanding Data-Primacy Architecture vs. Traditional App-Centric Approaches
So what does this shift actually look like in practice? The core difference comes down to where your data lives and how it behaves. In a traditional app-centric architecture, each application manages its own data. A CRM system holds customer records in its own database, a marketing tool stores engagement data separately, and a finance platform keeps its own ledger. This setup creates silos, leading to data fragmentation where the same customer information might exist in three different formats across three different systems. You end up spending more time reconciling data than actually using it.
Data-primacy architecture flips this model. Instead of data serving individual applications, it becomes the central hub. As DBTA quotes Everpure’s Giancarlo, embedding context, semantics, and governance directly at the data layer reduces data fragmentation. This means your data carries its own meaning and rules wherever it goes. When a new AI model needs training data, it pulls from a single, governed source rather than stitching together messy exports from five different apps.
The Core Difference: Data as the Center of Gravity
Think of it this way: with app-centric design, your data is a passenger. With data primacy tools, data becomes the driver. This architectural change directly impacts your enterprise data management strategy. You gain consistent data governance policies across every system, because the rules live at the data layer, not inside each application. For AI workloads, this consistency is critical. Models trained on fragmented data produce unreliable results.
Why Enterprises Must Shift to Data Primacy
The urgency here isn’t theoretical. According to DBTA, Giancarlo warns that enterprises that do not shift from app-centricity to data primacy will fall behind. As AI becomes more embedded in business operations, the complexity of managing fragmented data multiplies. You can’t build intelligent systems on a chaotic data foundation. Making the switch now prepares your infrastructure for the demands of modern AI, where data quality and accessibility directly determine success.
Everpure Data Intelligence: Universal Discovery and AI-Ready Governance
That’s where data primacy tools like Everpure Data Intelligence come into play. Announced at Pure Accelerate 2026, this platform—previously known as 1touch.io—gives you a single, unified view of your entire data landscape. Instead of juggling separate tools for discovery, governance, and context, you get one system that handles all three. The goal is straightforward: make your data ready for AI without the usual chaos.
From 1touch.io to Everpure Data Intelligence: A Unified Platform
You might recognize the technology under its former name, 1touch.io. Everpure rebranded and expanded it into a full data intelligence platform. The shift isn’t just cosmetic. It brings together universal discovery, automated governance, and AI-ready context under one roof. That means you no longer need to stitch together separate solutions for finding your data, keeping it compliant, and adding the business meaning that makes it useful for AI models.
Capabilities for Structured and Unstructured Data Across Clouds and SaaS
One of the biggest headaches in data management is the mix of formats and locations. You have structured databases alongside unstructured documents, spread across multiple clouds and SaaS applications. Everpure Data Intelligence handles both types without special configuration. It discovers where your data lives, applies automated governance rules to keep it secure and compliant, and adds the AI readiness context that machine learning models need. For you, that means less time hunting for data and more time putting it to work. The platform simplifies a process that otherwise requires constant manual effort and separate point solutions.
Data Stream: Reducing Raw Data Preparation from Months to Minutes
That simplification extends beyond just finding data. Once you have it, the real bottleneck often begins: preparing that raw information for actual use. Traditional data preparation can involve weeks of manual cleaning, formatting, and moving files between systems. Everpure’s new Data Stream capability, introduced at Pure Accelerate 2026, tackles this head-on. It is designed to shrink that timeline from months down to minutes, using real-time processing to handle data as it arrives.
How Data Stream Technically Accelerates Preparation
Instead of waiting for batch jobs to finish overnight, Data Stream processes information continuously. This stream processing approach means your data ingestion pipeline can handle incoming feeds without piling up delays. For you, that translates to faster access to clean, structured data for your AI models or analytics dashboards. The tool handles the heavy lifting of transforming raw logs, sensor outputs, or database exports into a usable format on the fly.
Stream-Level Access Controls for Enterprise Security
Speed alone isn’t enough when sensitive data is involved. Data Stream enforces stream-level access controls, which is a practical safeguard for enterprises. Rather than granting broad permissions to entire datasets, these controls let you define who can read, write, or process specific streams of real-time data. This granularity means you can share prepared data with teams or external partners without exposing the whole pipeline. It’s a security measure that keeps your data preparation fast, but also locked down where it matters most.
Enterprise AI Use Cases Enabled by Everpure’s New Tools
With that level of control over data granularity and security, Everpure’s Data Intelligence and Data Stream modules become practical enablers for several enterprise AI workloads. The architecture is designed to reduce data fragmentation, which often slows down AI readiness. That means you can move from raw data to production AI faster, without the usual integration headaches.
Real-Time Analytics and Model Training
For teams running machine learning pipelines, Data Stream offers a faster route to prepare and feed data into training loops. Real-time analytics become feasible when data flows continuously without bottlenecks. You can update models on the fly, respond to changing conditions, and reduce the time between data ingestion and insight. According to the company’s CTO Robert Lee, the tools are built for scale and production readiness, meaning they handle the volume demands of modern AI training without breaking down.
Governance for Large Language Models (LLMs)
LLM data preparation presents unique challenges: you need to ensure data quality, avoid biased sources, and maintain audit trails. Data governance for AI is baked into Everpure’s approach. The granularity you saw in earlier sections extends to metadata tagging and access controls, so you can prepare datasets for LLM fine-tuning while keeping compliance teams happy. Data Stream also supports versioning and lineage tracking, making it easier to reproduce results and prove data provenance. These capabilities turn Everpure’s new tools into a foundation for responsible, production-ready AI.
Market Position and Enterprise Adoption: Everpure’s Growing Traction
If these capabilities sound like the kind of foundation you’d want for your own AI workflows, you’re not alone. Everpure’s new data primacy tools are already gaining real traction across the data infrastructure market. The company recently reported annual recurring revenue of $2.04 billion and a net promoter score of 84 across nearly 15,000 customers — figures that signal both financial strength and strong customer loyalty. An NPS of 84 is exceptionally high, especially for an enterprise data cloud provider, and it suggests that organizations using Everpure are not only staying but actively advocating for the platform.
Financial Strength and Customer Loyalty
That $2.04 billion in annual recurring revenue reflects a steady growth trajectory. Meanwhile, the net promoter score of 84 indicates that a large majority of customers would recommend Everpure to peers. For you as a potential buyer, these metrics offer a practical gauge: a high NPS often correlates with responsive support, reliable uptime, and a product roadmap that aligns with real-world needs. The nearly 15,000 customers span industries from finance to healthcare, signaling broad applicability.
Comparison with Other Data Infrastructure Vendors
In the crowded data infrastructure market, few competitors can match that combination of recurring revenue and customer sentiment. Many players focus on raw performance or storage capacity, but Everpure differentiates by connecting data management directly to AI trust — exactly where the industry is heading. The company also used Pure Accelerate 2026 to update its Enterprise Data Cloud, reinforcing that these tools are part of a broader, integrated platform rather than isolated features. This strategic positioning makes Everpure’s data primacy tools a compelling choice if you’re evaluating long-term infrastructure investments for AI.
Frequently Asked Questions
How does Everpure’s data-primacy architecture differ from traditional app-centric approaches?
Traditional app-centric architectures prioritize individual applications over the underlying data, often leading to silos and redundant data handling. In contrast, Everpure’s data primacy tools treat data as the central asset, allowing you to manage, govern, and serve it independently from specific applications. This shift reduces duplication and makes your data ready for AI workloads without heavy reengineering.
How does the new Data Stream capability reduce data preparation time?
Data Stream provides a continuous, real-time pipeline that ingests and transforms data as it arrives, cutting out the batch-processing delays common in traditional setups. You can define stream processing rules once and apply them automatically, so data is cleaned and structured before it reaches your analytics or AI models. This eliminates repetitive manual steps and accelerates your time to insight.
What challenges does Everpure’s architecture aim to solve for enterprises?
Enterprises often struggle with fragmented data across multiple systems, inconsistent governance, and high costs for moving or copying data. Everpure’s architecture tackles these by offering a unified data layer that simplifies access, enforces policies, and reduces storage overhead. The result is a more efficient foundation for AI readiness, helping you scale without hitting the usual data bottlenecks.
