Production outcomes.
Not projections.
Every number on this page comes from a live production deployment in a regulated environment. Client names are withheld by agreement. The outcomes are verifiable.
93% AI Infrastructure Cost Reduction at Scale
Situation
A major UK life insurer had deployed Azure AI Foundry to process tens of thousands of underwriting documents per month. Token costs were scaling linearly with volume. At projected 5,000+ documents per hour, the annual AI infrastructure bill was forecast to exceed £2M — and growing. The CFO flagged it. The CTO needed a solution that didn't compromise on performance or data governance.
Complication
Simply switching to open-source models was not viable. The insurer operated under FCA oversight with strict data residency requirements. Any model serving production workloads needed to run within the organisation's own cloud estate — not third-party API endpoints. The technical team had no experience operating GPU infrastructure at scale.
Solution
Nural AI designed and deployed a patent-pending hybrid LLM routing architecture. Low-volume, latency-sensitive queries continued to route to managed Azure AI Foundry endpoints. High-volume batch processing — the dominant cost driver — was redirected to a self-hosted GPU cluster (NC48ads A100 v4 nodes) running Llama 3.x and Qwen 2.5 models via KAITO operator on AKS. An intelligent routing layer classified each request at inference time and directed it to the optimal endpoint based on cost, latency, and compliance parameters.
Results
The architecture is now the organisation's standard for LLM deployment. A patent application has been filed covering the routing methodology.
360× Underwriting Speed — 3 Hours to 30 Seconds
Situation
A US life insurance group's underwriting team was processing medical evidence manually. A single policy application required a senior underwriter to read, interpret, and annotate lab results, APS summaries, MVR reports, and prescription histories — a process taking 3–4 hours per policy. With application volumes growing, the backlog was extending customer wait times and creating retention risk.
Complication
Medical underwriting involves complex clinical reasoning. Errors carry significant liability. The legal and compliance teams were explicit: any AI system must be explainable, auditable, and traceable — hallucinations were not acceptable. Third-party AI vendors offering generic solutions could not meet the data sovereignty and model transparency requirements.
Solution
Nural AI designed a multi-agent medical intelligence framework. Four specialist agents were orchestrated in sequence: a Demographics Agent (builds the risk profile), a Blood Pressure Agent (interprets cardiovascular indicators), a Lab Analysis Agent (processes LOINC-coded results against clinical thresholds), and a QA Verifier Agent (validates all outputs for consistency and traceability). Every output was token-grounded — each finding linked to the specific source text that produced it. The system deployed on the insurer's own Azure estate with no data leaving the perimeter.
Results
The system processes 171 policies per batch at 28 seconds average per policy. Senior underwriters now review AI-generated summaries rather than raw documents — reducing their cognitive load while maintaining accountability. A patent application has been filed covering the zero-hallucination extraction methodology.
89% HR Query Resolution Without Human Intervention
Situation
A major UK retailer with 10,000+ employees across hundreds of sites was operating a high-volume HR support function. The volume of repetitive queries — payroll, leave, benefits, policies — was consuming significant HR team capacity. The business needed a solution that could handle queries at scale, integrate with existing systems, and maintain compliance with UK employment law requirements.
Complication
Employee trust was the primary risk. A poor AI experience — wrong answers, unable to escalate, losing context — would damage adoption and create legal exposure. The solution needed to integrate with ServiceNow and Workday without exposing sensitive HR data to third-party AI APIs.
Solution
Nural AI designed and deployed a Microsoft Copilot Studio platform with custom knowledge architecture, intent classification, and escalation routing. The system was integrated with ServiceNow for ticket management and Workday for employee data via Azure Logic Apps — all within the organisation's Microsoft 365 estate. A human-in-the-loop escalation layer ensured no sensitive query was handled incorrectly.
Results
The platform handles the equivalent of a full HR team's query volume with a fraction of the resource. Human HR staff now focus on complex cases, policy development, and employee relations.
Multi-Region Azure Landing Zone — Adopted as Org Standard
Situation
A UK financial markets infrastructure provider was running a fragmented Azure estate — multiple subscriptions with inconsistent security controls, no unified governance model, and CI/CD pipelines failing in production at an unacceptable rate. Regulatory expectations around infrastructure resilience were tightening.
Complication
The organisation had a complex stakeholder environment — multiple engineering teams with conflicting conventions, a risk function requiring evidence of compliance, and a strict change management process. Any new architecture had to be implemented without disrupting live trading infrastructure.
Solution
Nural AI led the design and implementation of a multi-region Azure Landing Zone with custom Terraform modules built to the organisation's specific security and compliance requirements. CIS benchmark compliance was enforced in IaC from day one. Shift-left security was integrated into the CI/CD pipeline — catching misconfigurations before they reached production. The architecture was documented and presented to the risk committee as the new organisational standard.
Results
The Landing Zone was formally adopted as the organisation's Azure architecture standard. The Terraform modules are now the baseline for all new workload deployments across the estate.
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