XOPS Verified Customer Reviews (2026)

Pipefy is the Enterprise-Grade Business Orchestration and Automation Technologies (BOAT) platform. It serves as a central orchestration layer that connects people, AI agents, and legacy systems into a unified operation. While traditional BPM solutions require months of engineering and consulting to deploy, Pipefy is architected to deliver AI-driven results in days. This speed enables IT leaders to solve the "backlog crisis" and modernize operations without the high cost of changing ERPs. Why Enterprise IT chooses Pipefy: 1. Elimination of Shadow IT: Unsanctioned tools create security risks and data silos. Pipefy’s "Adaptive Governance" model allows IT to set strict guardrails ("Safe Zones"). This empowers business units to build their own workflows—reducing the IT ticket backlog—while Technology teams maintain full visibility and control over data security and architecture. 2. Legacy Modernization (Two-Speed IT): Pipefy extends the capabilities of rigid legacy stacks (Systems of Record). By acting as an agile "System of Engagement" on top of SAP, Oracle, or Mainframes, it allows companies to deploy modern digital experiences and complex process logic without touching the delicate core code. 3. Agentic AI & Automation: The Pipefy Agent Studio moves beyond simple chatbots. It enables the deployment of specialized AI agents capable of executing tasks, reading unstructured documents (IDP), and routing requests based on complex rules. It creates a "Human-in-the-Loop" environment where AI handles the volume, and humans handle the exceptions. 4. Proven Economic Impact: Verified by a Forrester TEI study, Pipefy delivers a 260% ROI and a payback period of less than 6 months. It allows organizations to process high volumes of service requests (HR, Finance, Procurement, CS) with greater accuracy and less manual overhead. Compliance: SOC2 Type II, ISO 27001, ISO 42001 (AI Management), and SSO (SAML/OIDC) ready.

Most contact centers are stitched together from tools that don't talk to each other — a phone system here, a chatbot there, a support queue that loses context the moment it changes hands. Dialpad Contact Center replaces that patchwork with one AI-native platform where voice, digital, and human agents work from the same intelligence. The difference is agentic action. Rather than summarizing a call after the fact, Dialpad's AI agents reason through the issue in real time and carry it to resolution on their own — no handoff required unless one actually adds value. Voice and data stop living in separate silos, so every channel feeds the same connected picture of the customer. That connected picture gets smarter with use. Dialpad is already past 775 million AI recaps, and every conversation adds to a base of intelligence that keeps improving resolution speed, agent output, and customer satisfaction over time. It's all run through Dialpad's Guardian layer, which keeps AI behavior secure, auditable, and within the boundaries enterprises expect. The result: up to 80% of tickets resolved without a person touching them, and a support team that spends its time on the cases that actually need human judgment — intelligence doing the routine work, people handling what matters. Skeptical an AI contact center can deliver on that? Dialpad's Proving Ground lets you pilot and measure real ROI before you commit, rather than adopting on promises alone.

RelationalAI stands at the forefront of database technology, specifically designed to optimize advanced data applications utilizing relational knowledge graphs. By emphasizing a data-centric approach to application development, it seamlessly integrates data with logic into modular structures. These advanced applications are adept at understanding and leveraging every relationship within a given model. Utilizing a knowledge graph framework, the system articulates knowledge through executable models that can be easily expanded with declarative programs, which are both accessible and user-friendly. The versatile declarative language offered by RelationalAI allows developers to significantly reduce the amount of code needed, achieving reductions between 10 to 100 times, which in turn accelerates application development and improves quality by enabling non-technical users to participate in the creation process while simultaneously automating intricate programming tasks. By harnessing a flexible graph data model, users can establish a strong data-centric infrastructure. Moreover, the integration of various models facilitates the discovery of new relationships, effectively breaking down barriers that separate different applications. This pioneering methodology not only enhances the efficiency of development but also encourages collaboration across multiple domains, ultimately creating a more interconnected ecosystem for innovation. In this way, RelationalAI is redefining the standards for how data applications are built and utilized.