Merging technological possibility with human capability, we co-create ground breaking project teams that deliver with confidence.
Our Services
COEX 3X3
Our COEX 3x3 Framework allows for proven consistency & measurable outcomes.
Why
COEX?
Vast Advisory Network
Powered by Orbis Group
Our solutions team have decades of sector knowledge, and have a core advisory network that has been gained on the way.
Zero Dependency
Delivering your outcomes & scaling back, as you need. You're not reliant on us. Scale fast
Coex business model uses associates we are able to mobilise within two weeks.
What We Deliver:
Digital and AI Strategy
Data and Analytics Enablement
Workflow and Process Automation
Operating Model and Organisation Design
Product, PoC and MVP Delivery
Portfolio
Case Studies
Scalable Data Architecture Strategy for Global PayTech Firm
Key Highlights
- Advisory engagement supporting a global payments technology organisation (~5,000 employees).
- Delivered a clear, future-ready data architecture and multi-year roadmap.
- Strengthened internal technical capability through hands-on coaching in Big Data engineering and Apache Spark.
Challenges
- Existing platform struggling to keep up with rapidly expanding data demands.
- Uncertainty around how to evolve the technology stack to support future growth.
- Limited specialist expertise in modern data engineering frameworks.
- No clear plan for building the team required to support the next phase of the architecture.
Solutions
- Performed a structured assessment of the current data landscape and growth expectations.
- Designed a scalable target architecture addressing performance, reliability, and operational needs.
- Worked directly with the internal engineering group to build confidence in modern data tooling and Spark-based processing.
- Highlighted skills and role requirements, enabling the organisation to plan and prioritise team expansion.
Outcomes
- A well-defined architecture and roadmap ready for implementation.
- Engineering teams better equipped to maintain and evolve the platform.
- A clear understanding of the competencies needed to execute the strategy.
- A targeted hiring plan aligned to future data platform goals.
Digital Aviation Platform Build for Ardonagh Specialty (formerly Price Forbes)
Key Highlights
- Multi-year partnership to build a digital general aviation facility that delivers real-time quotes and binds risks with Lloyd’s syndicates.
- Created the EDGE platform, a modern, high-performance solution that transformed the speed and quality of aviation risk placement.
- Established a full Scrum team within six weeks and scaled it to support ongoing delivery.
Challenges
- Absence of a digital workflow capable of supporting rapid aviation quoting or automated placement.
- Existing systems were fragmented, slowing down client response times and limiting market competitiveness.
- A new delivery team needed to be built quickly, with a clear structure and long-term capability in mind.
- No unified technology architecture to guide future platform development or integration efforts.
Solutions
- Completed a review of core business systems and produced a clear problem statement outlining operational bottlenecks and technical constraints.
- Designed the target-state IT architecture and a roadmap detailing how to transition from legacy systems to a modern ecosystem.
- Formed a multi-disciplinary Scrum team (Product Owner, Development Lead, Java and React engineers, DevOps, QA) to deliver the platform iteratively.
- Developed EDGE using a microservices architecture, modern UI components, and streamlined automation tooling to support scale and future enhancements.
Outcomes
- A real-time digital aviation facility that significantly improved client service and reduced placement time from days to minutes.
- A scalable architectural foundation enabling ongoing feature development and broader digital ambitions.
- An established delivery function with the skills and structure needed for sustainable, long-term ownership of the platform.
Agentic AI Workflows for KYC Orchestration, London Insurer
Key Highlights
- Delivered a two-phase programme introducing an AI-enabled workflow engine to replace a largely manual onboarding process.
- Built a functional prototype and progressed it into a production-ready platform with UI, automated checks, and system integrations.
- Demonstrated significant efficiency gains, improved data accuracy, and consistently faster turnaround times.
Challenges
- The existing workflow relied on email intake, spreadsheets, and re-keying, resulting in slow turnaround and inconsistent quality.
- High operational effort due to sequential handoffs and a large team needed to manage daily volume.
- Limited visibility of case progress, making stakeholder tracking and prioritisation difficult.
- Frequent rework caused by incomplete or inaccurate submissions.
- Inadequate audit traceability, increasing compliance overheads.
Solutions
- Phase 1 Prototype – Built a lightweight version of the workflow engine to validate logic, data capture, automation potential, and user experience, testing extraction accuracy, exception handling, and activity logging to ensure the approach was viable for scaling.
- Phase 2 Platform Build – Introduced a user interface and connected the engine to live systems and data sources, strengthening the architecture with monitoring, security controls, and detailed observability, while delivering incremental releases with regular feedback loops and stakeholder alignment.
Outcomes
- Turnaround time reduced from five days to a target of two days.
- High proportion of straight-through processing for standard cases, reducing manual touchpoints.
- Improved accuracy and completeness of data through structured capture and automated validation.
- Enhanced visibility across the onboarding journey, reducing escalations and delays.
- Expected 35% reduction in operational costs through reduced manual effort.
- A scalable platform foundation suitable for broader adoption across other business areas.
- A scalable platform foundation suitable for broader adoption across other business areas.
M-Files Document Management Implementation Lockton Re, London
Key Highlights
- Delivered a 9-month M-Files implementation for a leading reinsurance broker, completed on time and within budget.
- End-to-end delivery including business analysis, solution design, build, UAT, rollout, and training.
- Successfully deployed the solution to 250+ users across London operations.
Challenges
- Varied documentation practices across nine broking teams required alignment into a unified, consistent structure.
- Key data sat across multiple legacy systems, creating complexity in defining a single source of truth for document metadata.
- Introducing a new platform to a large, busy user base while keeping business disruption to a minimum.
- Delivering a full lifecycle project under a fixed timeline.
Solutions
- Conducted targeted business analysis to define documentation workflows, metadata needs, and user requirements.
- Designed and configured a tailored M-Files environment to standardise document handling and support consistent processes.
- Analysed and incorporated essential data from legacy systems to support accurate document classification and retrieval.
- Ran structured UAT with representation across teams, followed by a phased rollout and comprehensive Superuser and end-user training.
Outcomes
- A fully deployed, London-wide M-Files solution supporting streamlined document management across all broking teams.
- Greater consistency and accessibility of documentation through improved metadata structure and aligned data sources.
- Smooth adoption across 250+ users due to structured testing, phased rollout, and targeted training.
- A repeatable implementation model now being considered for a wider Lockton rollout and applicable to similar needs across other sectors.
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