iOpt is a Glasgow-based PropTech and IoT company helping UK social housing providers monitor environmental conditions across tens of thousands of homes. Its sensors capture temperature, humidity, and CO2 data, while its algorithms identify risks linked to damp, mould, occupancy, and energy performance - issues now closely tied to Awaab’s Law and wider housing compliance requirements.
As the business scaled, iOpt’s leadership recognised an opportunity to evolve how insight was delivered to clients. The team had already built a strong reporting capability and wanted to create a more scalable analytics platform that could support both continued growth and more advanced predictive insight over time.
The Challenge
iOpt had developed a reporting model built around close client support and bespoke operational insight.
Housing providers relied on scheduled reporting delivered on a four-to-six week cycle, prepared by specialist analysts working directly from the underlying sensor data. The approach gave clients tailored visibility into portfolio risks and performance while allowing iOpt to stay closely connected to customer needs as the platform matured.
As adoption accelerated, leadership saw the next stage clearly: to evolve from a reporting-led service model towards a scalable analytics capability that could give clients direct access to trusted insight, without losing the rigour that had made the existing approach successful.
That required several operational foundations to be created, including:
- Shared metric definitions
- Rules for usable data
- Consistent anomaly handling
- Clear operational ownership
The goal was not simply to introduce self service dashboards, but to build a trusted analytics layer capable of supporting long-term scale, predictive capability, and stronger operational leverage across the business.
Our Approach
Vaul Labs structured the engagement around three deliberately sequenced workstreams. Most analytics projects begin with visualisation and attempt to retrofit governance afterwards. We took the opposite approach.
Building the foundation first
Before designing a single dashboard, we focused on the underlying data layer.
Working closely with the iOpt team, we defined core business metrics, codified data rules, and established consistent handling for anomalies and edge cases across large-scale sensor streams. That work created a governed data layer the business could build upon confidently.
iOpt had already identified the long-term direction of travel. Clarifying the underlying data and operational assumptions early allowed the first client-facing capability to move into production in around three months.
Delivering self-serve analytics
On top of the governed data layer, Vaul Labs built the self-serve analytics capability iOpt’s clients had been asking for.
Housing providers could now access operational insight on demand rather than waiting on scheduled reporting cycles.
The capability was introduced alongside existing workflows, allowing iOpt to continue serving customers without disruption during the rollout.
Handing over the capability
The final workstream focused on long-term ownership and independence.
Alongside equipping the iOpt team with governance definitions, operational playbooks and training, Vaul Labs scoped the permanent role required to own the capability internally, supported recruitment, and helped secure the right hire.
The system, the documentation, and the permanent owner arrived together.
Scope & Constraints
The engagement operated against live operational and regulatory requirements.
iOpt needed to continue supporting a growing client base while simultaneously introducing a more scalable analytics capability underneath ongoing delivery.
The data environment itself was also complex. Sensor streams across tens of thousands of properties required careful handling of incomplete readings, anomalies, and edge cases in order to maintain confidence in downstream insight.
Outcomes
“We had the ambition and the data. What we needed was the right team to help us formalise the foundation and move at pace. Vaul Labs did that in three months, and crucially, ensured we owned it completely. That combination of speed and handover is exactly what a scaling business needs.”
Dane Ralston, CEO, iOpt
The operational results were immediate:
- Delivery: Self-serve analytics replaced a four to six week reporting cycle with on-demand access
- Efficiency: This enabled 1.5 FTE of specialist analyst capacity to be redirected towards higher-value insight work
- Scalability: A scalable model that could absorb new customer growth without additional analyst hires
- Governance: A governed platform now underpins iOpt’s predictive and algorithmic roadmap
The platform also enabled new value for iOpt’s customers through future predictive and AI-driven capabilities, including:
- Predictive damp and mould insight aligned to Awaab’s Law timeframes
- Portfolio intelligence across thousands of properties
- Energy-efficiency and retrofit analysis
The business moved from selling reports towards selling insight.
Equally important, the capability was fully transferred internally. A permanent owner was in place, the operational processes were documented, and the platform could continue evolving independently after delivery concluded.
By establishing a governed foundation underneath the platform, iOpt created the conditions for insight to scale alongside the business, allowing value to compound across the organisation over time.