Review of GEP, Supply Chain and Procurement Software Vendor
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GEP, founded in 1999 in New Jersey and led by industry veteran Dr. Subhash Makhija, has established itself as a major player in the procurement and supply chain software arena. The company’s integrated approach—combining software, consulting, and managed services—targets global enterprises eager to streamline operations, optimize spend, and drive operational efficiency. Underpinned by its proprietary GEP QUANTUM platform, GEP champions a cloud‐native, modular, and low‐code environment that leverages strategic acquisitions (such as OpusCapita for e‐invoicing and COSTDRIVERS for cost analytics) to bolster its AI‐driven procurement and supply chain capabilities. Although the company touts an “AI-first” approach featuring generative AI, natural language processing, and predictive analytics, a closer technical examination reveals that many of these claims remain high-level and merit a healthy dose of skepticism from the operationally rigorous supply chain executive.
Company Background and Acquisition History
Founding and Leadership
GEP was founded in 1999 in New Jersey under the leadership of Dr. Subhash Makhija, whose technical and operational expertise laid the foundation for a mission centered on customer‐centricity and sustainable innovation in procurement and supply chain management 1. The company has continually sought to build transformative solutions that balance authenticity with performance, aiming to “build a beautiful company” that truly understands its clients’ operational challenges.
Acquisitions
In recent years, GEP has strategically expanded its capabilities through acquisitions. In July 2024, GEP acquired OpusCapita—a recognized leader in e‐invoicing and accounts payable automation in Northern Europe—to enhance its flagship procurement platform 2. Earlier, in March 2022, the acquisition of COSTDRIVERS and Datamark further empowered GEP to integrate advanced big data analytics and machine learning for cost forecasting and procurement intelligence 3.
Technology Architecture and Deployment Model
The GEP QUANTUM Platform
At the core of GEP’s offering lies the GEP QUANTUM platform—a comprehensive, AI‐first, low‐code development environment that underpins solutions such as GEP SMART (for procurement), GEP NEXXE (for supply chain management), and GEP GREEN (for sustainability) 4. Designed as a cloud‐native system running on Microsoft Azure, the platform employs microservices and highly modular components to ensure rapid deployment, scalability, and seamless integration with major ERP systems via pre‐packaged APIs 56. This architecture allows even citizen developers to tailor applications quickly while maintaining a robust, enterprise‐class solution.
Deployment and Roll-Out Model
GEP delivers its software as a cloud‐based Software-as-a-Service (SaaS), significantly reducing on-premises infrastructure requirements and IT overhead. Its modular, microservice-driven approach ensures that deployments can be incremental and agile. Integration is further enhanced with hybrid connectivity solutions that bridge the gap between legacy ERP systems (such as SAP or Oracle) and GEP’s advanced procurement and supply chain applications.
AI and Machine Learning Components
AI-First Approach and Claims
GEP markets its solutions as “AI-first,” incorporating generative AI and machine learning across a broad spectrum of functions ranging from sourcing and procurement to accounts payable automation 7. The platform is designed to integrate capabilities such as natural language processing, conversational interfaces, and predictive analytics to supplement decision-making processes.
Detailed Use Cases in AI/ML
Within procurement and spend analytics, machine learning techniques are applied for demand forecasting, supplier evaluation, and inventory optimization, with an aim to extract actionable insights from extensive datasets 8. Similarly, on the supply chain front, AI-driven algorithms support route optimization, real-time visibility, and risk mitigation—purportedly reducing manual interventions and improving efficiency through automated workflows.
Skeptical Perspective on AI Claims
Despite bold marketing narratives, many of GEP’s AI/ML claims are articulated in broad strokes. The technical disclosures provided in public materials remain high-level, and it is possible that some functionalities—such as predictive analytics and NLP search—rely on proven statistical methods or rule-based processes, repackaged under modern “AI” terminology. For prospective clients, it is advisable to pursue detailed technical demonstrations and proof-of-concept validations to ensure that the promised innovations translate into tangible operational benefits.
Insights from Job Postings and Company Culture
GEP’s career pages and recruitment materials highlight a global focus on expertise in cloud platforms, data analytics, and low-code development, reflecting an internal culture of rapid innovation and agility 9. This emphasis on attracting top talent aligns with its commitment to staying competitive in a fast-evolving technological landscape, even though granular technical specifics about its backend operations remain relatively sparse.
GEP vs Lokad
When comparing GEP’s approach with that of Lokad, notable differences emerge. GEP’s platform is built on a cloud-native, low-code, microservices architecture that emphasizes modularity and rapid deployment—bolstered by strategic acquisitions such as OpusCapita and COSTDRIVERS to enhance its breadth in procurement and analytics. In contrast, Lokad has pursued an organic growth path founded on a rigorously engineered, custom-built system focused on quantitative supply chain optimization. Lokad’s platform leverages an in-house domain-specific language (Envision) developed in F# and C#, accompanied by a lean stack with minimal external dependencies 1011. While GEP promotes broad AI-first capabilities across procurement and supply chain management, Lokad is distinctly oriented toward mathematically driven, predictive optimization with deep integration of probabilistic forecasting and decision automation. These differing philosophies underscore GEP’s aim to deliver an integrated, enterprise-ready solution via strategic partnerships and low-code tools, whereas Lokad caters to organizations seeking highly specialized, numerically rigorous supply chain optimization.
Conclusion
GEP offers an end-to-end, cloud-native solution for procurement and supply chain management, characterized by its GEP QUANTUM platform that blends AI/ML technologies with low-code development and modular microservices. Its strategic acquisitions underscore a commitment to expanding its technological breadth and enterprise reach. Nevertheless, while GEP’s promotional materials project a vision of advanced, AI-first innovation, technical specifics often remain at a high level—warranting thorough technical demonstrations and pilot implementations before full-scale adoption. In juxtaposition with niche players like Lokad, which prioritize deep quantitative optimization via a custom-engineered approach, GEP’s methodology reflects a balance between comprehensive integration and market-ready ease of deployment. For supply chain executives, the choice between these paradigms will hinge on organizational readiness to invest in tailored, internally driven innovation versus adopting a broad, easily deployable, and integrated suite of procurement and supply chain management solutions.