Everest Technologies Inc

Intelligence that powers your AI use cases

Corporate office in Columbus, OH (show on map)

4.4

★★★★☆

7 reviews

Enterprise IT & Intelligent Automation provider with 29 years of experience delivering mission-critical solutions to Fortune 500 brands.

Call (614) 436-3120

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About

Everest Technologies, established in Columbus, Ohio in 1997, specializes in enterprise IT solutions, AI, supply chain, retail applications, integration, data, and quality engineering. With a global delivery model spanning 17+ countries and 29 years of experience, the company serves 150+ enterprise clients, including Fortune 500 brands such as DHL, JPMorgan Chase, Americold, Abercrombie & Fitch, Deckers, Express, Total Wine & More, Zurich, Crown Holdings, Costco Wholesale, Designer Brands, Masonite, Big Lots, Patagonia, PNC, Coach, Banana Republic, J.Crew, Michael Kors, Capri Holdings, James Hardie, ODW Logistics, Old Navy, Best Buy, Maurices, Ann Taylor, Gap, SpaceX, and Helen of Troy.

Everest's five practice areas—Everest AI, Supply Chain, Retail Applications, Integration, Data & Analytics, and Quality Engineering—each led by domain experts, deliver mission-critical solutions across retail, supply chain, and enterprise data landscapes.

Domain-native thinkers

Seamless client integration

AI-driven baseline

Global, agile pods

Continuous domain mastery

Services

Everest AI

Partners with organizations to unlock enterprise value through strategic discovery, high-impact use cases, and scalable AI solutions.

AI-Powered Forecasting Gen AI & Agentic Workflows Intelligent Document Processing Platform AI Activation

Supply Chain

End-to-end supply chain solutions powered by Manhattan, Infios, Made4Net, and custom platforms.

Manhattan WMS (Platinum Partner) Korber / Infios WMS Transportation Management Warehouse Automation

Retail Applications

Build and modernize retail technology platforms from POS and eCommerce to merchandising and customer engagement systems.

POS & E-Commerce Platforms Merchandising Systems Customer Engagement Omnichannel Integration

Integration, Data & Analytics

Build governed, AI-ready data foundations for BI dashboards, self-service analytics, and ML pipelines on a modern data stack.

Data Lake BI & Self-Service Analytics Data Governance ML Data Pipelines

Quality Engineering

Agentic AI QA solutions customized for your applications and environment, including automated testing frameworks, performance engineering, and continuous validation.

Test Automation Performance Engineering Regression Suites CI/CD Quality Gates

Credentials & Certifications

17+ Countries globally

Since 1997

150+ Enterprise clients

Since 2015

Reviews

Rating

4.4

★★★★☆

Reviews

Frequently Asked Questions

How long does a Manhattan Active® WMS implementation typically take for a multi-DC retail network? +

For a mid-to-large retailer with multiple distribution centers, a full Manhattan Active® WMS implementation usually takes 9-15 months. This includes design, configuration, integrations, testing, and phased rollout. Timelines depend on complexity, number of DCs, automation, and integrations with OMS/TMS. We typically recommend a pilot DC before scaling across the network.

Can you support a WMS go-live close to peak season without risking operations like Black Friday? +

Yes, but it requires careful planning and risk mitigation. We typically recommend avoiding first-time go-lives during peak unless there's strong stabilization from prior phases. If timing is fixed, we implement parallel runs, fallback strategies, and performance validation under peak loads. Our focus is to ensure stability before volume hits.

What does a typical WMS migration from a legacy system (AS/400 or similar) involve? +

A migration includes process mapping, system configuration, data migration, integrations, and extensive testing. Legacy systems often have hidden dependencies, so discovery is a critical phase. We also focus heavily on user training and change management to ensure adoption. The goal is not just replacing the system, but improving how the warehouse operates.

How do you ensure inventory accuracy during and after implementation? +

We implement cycle counting strategies, real-time inventory updates, and validation rules within the WMS. During go-live, we conduct reconciliation checks between legacy and new systems. Post go-live, monitoring dashboards help track discrepancies early. Achieving 99%+ accuracy is typically a combination of system design and disciplined operations.

How do you handle integrations between WMS, OMS, and transportation systems? +

We design integrations as part of the core architecture, not as an afterthought. This includes defining data flows for orders, inventory, shipments, and status updates. We use APIs or middleware depending on the ecosystem. Testing focuses heavily on end-to-end scenarios to ensure systems stay in sync under real-world conditions.

What kind of operational improvements should we realistically expect from a WMS implementation? +

Most clients see improvements in order processing speed, inventory accuracy, and labor productivity. Typical outcomes include 25-40% faster fulfillment, reduced errors, and better visibility across DCs. However, results depend on how well processes are redesigned alongside the system. Technology alone does not solve operational inefficiencies.