Mphasis, an AI-led technology solutions provider, has acquired Theory and Practice Business Intelligence Inc., a Vancouver-based technology company known for its Continuum AI decision intelligence platform, in a deal valued at C$10 million upfront with additional milestone-based contingent consideration of up to C$20 million.
Founded in 2018, Theory and Practice built a platform that spans descriptive analytics, predictive modeling, and optimization, enabling enterprises to move from raw data to actionable decisions in real time. Continuum AI combines artificial intelligence with behavioral economics to help organizations better understand customer behavior and optimize decisions across pricing, demand forecasting, marketing, and supply chain operations. The platform also allows organizations to harmonize intelligence across departments while accounting for nuanced customer behavior.
The acquisition strengthens Mphasis’ NeoIP platform by adding a decision intelligence layer designed to improve enterprise decision-making and enable agentic workflows. Mphasis views the deal as a key step in moving enterprises beyond isolated AI pilots toward scalable, repeatable business outcomes, noting that over 80% of AI spending is projected to be directed toward business transformation initiatives. The company plans to expand its capabilities in financial services, retail, and consumer packaged goods through the integration, while also scaling AI-driven decisioning across additional industries. The combined offering is expected to accelerate time-to-value for clients by leveraging advanced AI models, reusable ontologies, and causal modeling techniques.
As part of the deal, Theory and Practice founder and CEO Dr. Rogayeh Tabrizi will join Mphasis as Executive Vice President, CPG and Head of Decision AI, contributing to the company’s leadership and strategic direction in AI-driven decisioning. The acquisition also brings a team of experts in AI, data science, and behavioral economics into Mphasis.
KEY QUOTES:
“We are excited to welcome the TAP team, clients, and partners to Mphasis. TAP’s Continuum AI will be a key catalyst for NeoIP, introducing a critical decision intelligence layer that can drive measurable economic outcomes for Enterprises. Over 80% of the AI spending is projected to be directed towards business reimagine and this extends Mphasis’ reach into a critical segment of AI spend initiatives. Built on advanced AI and deep behavioral economics capabilities, this combination allows us to move beyond task automation, towards systems that can reason over business objectives, constraints, and domain context, to deliver these outcomes.”
Nitin Rakesh, Chief Executive Officer and Managing Director, Mphasis
“We are excited to join the Mphasis family and bring Continuum AI into a larger platform and engineering ecosystem. TAP has shown how advanced modeling, causal inference, and optimization can materially improve decision-making. Combined with Mphasis’ scale, industry vertical expertise ontology capabilities, and execution infrastructure, we now have the opportunity to turn these domain-specific successes into reusable decision assets, that can be deployed, governed, and scaled across industries. Together, we are building a path for Enterprises from experimentation to repeatable and scalable value and business reimagine using AI. Our combined capabilities will enable clients to move beyond isolated pilots and unlock faster, more meaningful business decisions with intelligence, speed, and measurable impact.”
Dr. Rogayeh Tabrizi, Founder and CEO, Theory and Practice
“Even when predictive models exist, many organizations lack a robust layer that structures context, links concepts consistently, and enables higher-order reasoning and decisioning. Through this acquisition, Mphasis adds to the context engineering layer, that is foundational for agentic workflows, a decision intelligence beyond point solutions, so outcomes can be designed, executed, measured, and continuously improved.”
Ramanathan Srikumar, Chief Solutions Officer, Mphasis
