How Reyem Tech helped Metrix Group secure funding and accelerate AI delivery | Case Study

Metrix Group brings deep domain expertise in pharma behavior-change and sales training, and wanted to evolve from a traditional services firm into an AI-enabled platform business. What it did not have was a technical foundation to build on:

• No CTO or technical function — technology decisions were ad hoc.
• No in-house developers — work was outsourced with no architectural oversight.
• Zero cloud infrastructure — no dev environments, CI/CD, or scalable hosting.
• Regulated-industry constraints — pharma clients require strict per-client data isolation; cross-client data sharing is legally prohibited.
• A government digital-maturity funding opportunity on the table that required a credible technical strategy and documentation to unlock.
• AI ambitions with no way to execute — no roadmap, platform, or team.

Reyem Tech embedded as a hands-on Fractional CTO (~15 hrs/week) for roughly a year, building Metrix's entire technology function from zero and concluding with a clean handover to permanent in-house leadership.

Pillar 1 — Securing funding. Led the DMAP (Digital Maturity Assessment Program) submission to the Ontario Centre of Innovation end-to-end — process-mapping every business area (Delivery, Sales, Marketing, Account Management, HR, and Finance), then authoring the assessment report, the AI-platform documentation, and the AI/data-architecture schematic. From process mapping in August 2025 to approval in January 2026, the submission secured government digital-maturity funding and validated the technical strategy. A second, non-dilutive channel was opened by structuring and supporting SR&ED R&D tax-credit applications for the AI work.

Pillar 2 — Accelerating AI delivery. At the core is a proprietary AI platform: a Neo4j knowledge graph as the analytics backbone — modeling behavior-change frameworks, stakeholder gaps, and evidence chains as a graph — on an ontology-first GraphRAG architecture, with a Model Context Protocol (MCP) server on Kubernetes (Python/FastMCP) exposing it to AI agents via ~20 tools, and per-client data isolation at every layer for pharma compliance.

From 18 identified AI workflows, the engagement drove a deploy-and-measure focus and shipped production tools that accelerate delivery:

• An AI pipeline that turns interview transcripts into structured behavior-mapping tables via the knowledge graph — cutting analyst time by 50–70%.
• AI-generated workshop materials (facilitator guides, activities, and slides) that take workshop creation from days to hours.
• An AI-powered sales-training builder that assembles tailored learning paths, deployed for a global pharmaceutical client.
• Augmented-reality training characters, deployed to production with reusable deployment tooling.
• A learning-impact analytics dashboard for measuring training outcomes.

The platform runs on infrastructure built from zero — an Azure AKS production cluster, AWS for partner/tenant isolation with VPC peering, Airbyte ingestion into a medallion (Bronze/Silver/Gold) data lake, and a full Microsoft 365 migration — on a stack of self-hosted n8n, Azure OpenAI, Neo4j, Qdrant, and MongoDB.

To run it, Reyem Tech hired an AI developer and led a 6-person cross-functional team (developers, designers, and data specialists) on daily standups, Scrum, and a full SDLC — managing external suppliers and reducing bus-factor risk with knowledge-transfer plans, alongside security controls, DR planning, and pharma data separation validated by legal counsel.

Over roughly a year, the engagement built a durable, self-sufficient technology function — and left it running after a clean handover.

• Government digital-maturity funding secured (DMAP approved, January 2026), plus a second channel opened via SR&ED R&D tax-credit applications.
• 50–70% less analyst time on behavior-insights report generation; workshop creation cut from days to hours.
• A full cloud + AI platform built from zero in ~1 year — Azure AKS + AWS, a medallion data lake, vector databases, a workflow engine, and an MCP server.
• A Neo4j knowledge graph + MCP server in production — a proprietary, defensible capability inside a services business.
• 18 AI workflows identified with a phased, deploy-and-measure rollout — 6+ in production by the end of the engagement.
• A 6-person cross-functional AI team hired and productive on a structured SDLC.
• Pharma compliance enforced architecturally — per-client isolation validated by legal counsel.
• A durable handover — the engagement concluded with a clean transition to permanent in-house technical leadership; the platform, team, and processes kept running without disruption.