ERP and validation consulting with automation and AI expansion
Canus Tech is a mid-sized consulting firm specializing in ERP implementations (SAP, Oracle, Microsoft, Salesforce) and regulated-industry validation work, now pivoting toward AI-driven automation and sales acceleration. The tech stack reveals a dual operating model: legacy MATLAB/Simulink expertise for embedded and simulation work, paired with modern low-code platforms (n8n, Zapier, Retool, Appsmith) and generative AI tools (OpenAI, Gemini)—suggesting internal investment in scaling consulting delivery beyond traditional hand-crafted engagements. Active hiring across engineering and data roles, with projects spanning ELN implementation, AI sales automation, and legacy modernization, indicates an effort to productize and systematize their consulting playbook.
Canus Tech advises mid-market and enterprise clients on ERP strategy, implementation, and validation across SAP, Oracle, Microsoft Dynamics, Workday, and Salesforce. The firm operates across several service lines: digital transformation, ERP consulting, validation and compliance (CSV, GxP, 21 CFR Part 11 for regulated industries), test automation (Worksoft, TOSCA, Selenium), cloud and DevOps, data and AI, and cybersecurity. Founded in 2008 and based in Parsippany, New Jersey, the company employs 51–200 people and maintains distributed hiring across the United States, Canada, and India. Revenue model combines fixed-scope project delivery with staff augmentation and fractional consulting roles.
SAP, Oracle, Microsoft Dynamics, Workday, and Salesforce, plus specialized validation and compliance work (CSV, GxP, 21 CFR Part 11) for regulated industries like life sciences.
Projects include an ELN implementation initiative, AI-driven sales automation, AI-assisted recruiting, internal KPI dashboards, and modernization of legacy applications—indicating a shift toward automation and AI tooling in delivery and sales.
Canus Tech's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.