A pharma marketing agency serving global pharmaceutical brands needed to transform from a traditional services company into an AI-powered platform business. They had no technical leadership, no development team, no cloud infrastructure, and no strategy for how to get there — but they had deep domain expertise in pharmaceutical behavior change and training.

Key challenges included:

• No CTO or technical function — all technology decisions were ad hoc with no strategic direction
• Zero cloud infrastructure — no development environments, no CI/CD, no scalable hosting
• No in-house developers — technical work was outsourced with no oversight or architectural consistency
• Regulated industry constraints — pharma clients required strict data isolation; cross-client data sharing was legally prohibited
• Supplier sprawl — an IT managed service provider handling Microsoft 365 and SharePoint with an unfavorable contract, plus external product partners requiring integration and oversight
• Government funding opportunity — a digital maturity assessment program required a credible technical strategy and documentation

Reyem Tech embedded as Fractional CTO (hands-on, 15 hrs/week) to build the entire technology function and a proprietary AI platform from scratch.

Knowledge Graph & AI Architecture

• Designed and deployed a Neo4j knowledge graph as the backbone for AI-powered analytics — modeling domain ontologies (behaviors, stakeholder gaps, evidence chains) as a graph rather than flat data
• Built an ontology-first GraphRAG architecture where domain node types and relationships are defined before extraction, ensuring consistent, queryable knowledge structures
• Deployed a Model Context Protocol (MCP) server on Kubernetes — a Python/FastMCP service exposing the knowledge graph to AI agents (Claude, n8n workflows, custom applications) via ~20 tools for create, query, update, and export operations
• Implemented per-client data isolation at every layer (vector database collections, graph database boundaries, cloud account separation) to meet pharma compliance requirements

AI Platform & Workflow Engine

• Identified and prioritized 18 AI-powered workflows across the business, with a phased rollout plan
• Built and deployed the first production AI tools:
  • Insights Report Automation — a two-flow system that generates behavior mapping tables from interview transcripts using the knowledge graph, reducing analyst time by 50–70%
  • Workshop Builder — AI-generated workshop content (facilitator guides, activities, slides) reducing creation from days to hours
  • Learning Path Generator — AI-powered pharma sales training tool deployed for a global pharmaceutical client
• Established the AI workflow stack: n8n.io (self-hosted on Kubernetes), Azure OpenAI, Neo4j (graph + vector), Qdrant (vector search), MongoDB (workflow state)

Cloud Infrastructure & DevOps

• Built Azure AKS (Kubernetes) infrastructure from scratch — production-grade cluster hosting all AI services, workflow automation, and MCP servers
• Stood up AWS infrastructure for partner tenant isolation with VPC peering between accounts
• Deployed Airbyte for data ingestion and designed a medallion architecture (Bronze/Silver/Gold) data lake on Azure
• Migrated the organization to Microsoft 365, modernizing collaboration and productivity

Team Building & Management

• Hired 1 AI developer and managed a 6-person cross-functional team including developers, designers, and data specialists
• Ran daily standups, implemented Scrum, and established a full SDLC with tickets, code review, and structured delivery
• Managed external suppliers — renegotiated an IT managed service provider contract (flagging unfavorable annual increase clauses), and oversaw integration of an external product partner's deployment into the platform architecture
• Identified and mitigated knowledge concentration risks (bus factor) in external contractor relationships

Compliance & Governance

• Implemented security controls: 1Password rollout, open source compliance audit, disaster recovery planning
• Ensured all AI architecture enforced pharma data separation — per-client isolation validated by legal counsel
• Produced the technical strategy and documentation to secure government digital maturity funding (DMAP)
• Supported SR&ED (Scientific Research & Experimental Development) tax credit applications for AI R&D work

In 9 months, the agency transformed from having zero technical capability into operating a proprietary AI platform with knowledge graph architecture:

• Neo4j knowledge graph in production — domain ontology modeled, MCP server deployed, AI agents querying the graph for insights generation and workshop content
• 3 AI tools deployed or in final testing — Insights Report Automation, Workshop Builder, and Learning Path Generator, with 15 more workflows identified and prioritized
• 50–70% time reduction in behavior insights report generation — from manual analyst work to AI-assisted, graph-powered automation
• Full cloud infrastructure built from zero — Azure AKS, AWS tenant accounts, data lake, vector databases, workflow engine, all production-grade
• 6 team members hired and productive — a cohesive AI development team with structured SDLC and daily delivery cadence
• Government funding secured — DMAP digital maturity program approved
• Pharma compliance enforced architecturally — per-client data isolation at every layer, validated by legal counsel
• Supplier relationships optimized — MSP contract renegotiated, external partner integration managed with knowledge transfer plans

The engagement demonstrated that a fractional CTO can build a differentiated AI product capability inside a services company — not just infrastructure and team, but a proprietary knowledge graph platform that becomes a competitive moat in a regulated industry.