Skip to content

FIG. 01Industries

Built for how your sector actually works.

The constraints differ by industry. The discipline does not.

Sectors we work in

  • Finance

    The challenge

    • Core systems that cannot be taken offline
    • Every automated decision needs an evidence trail
    • Fraud patterns move faster than the release cycle

    What we build

    • Reversible automation with immutable event logs
    • Model decisions that carry their own evidence
    • Migrations sequenced behind a live legacy path

    AI opportunities

    • Document extraction with confidence thresholds
    • Anomaly detection with human review at exceptions
    • Reconciliation and exception triage
  • Insurance

    The challenge

    • Claims cycles measured in days, not minutes
    • Roughly one case in six does not fit the happy path
    • Underwriting knowledge concentrated in a few people

    What we build

    • Claims pipelines with the exception queue as a first-class path
    • Reviewer tooling that arrives with full context
    • Policy and document workflows with full audit retention

    AI opportunities

    • First-notice-of-loss classification and routing
    • Document summarisation for adjusters
    • Fraud signal surfacing with human adjudication
  • Healthcare

    The challenge

    • Patient data under strict handling rules
    • Clinical workflows that cannot absorb friction
    • Integration with systems designed decades apart

    What we build

    • Least-privilege data paths with full audit
    • Clinician-tested interfaces, tested during the build
    • Integration layers that isolate systems you cannot change

    AI opportunities

    • Clinical documentation support with clinician approval
    • Scheduling and capacity optimisation
    • Prior-authorisation preparation
  • Travel

    The challenge

    • Demand peaks an order of magnitude above baseline
    • Partner systems whose data quality you do not control
    • Disruption handling that decides the customer relationship

    What we build

    • Capacity models built from real peak data
    • Defensive handling of every inbound partner feed
    • Graceful degradation rather than failure

    AI opportunities

    • Disruption rebooking suggestions with agent approval
    • Multi-language customer response drafting
    • Dynamic package assembly
  • Government

    The challenge

    • National data-residency obligations
    • Procurement timelines measured in quarters
    • A public that notices every outage

    What we build

    • In-region architecture proven before build
    • Accessibility delivered to WCAG 2.2 AA, tested during the build
    • Delivery documented for the record

    AI opportunities

    • Case triage and routing
    • Citizen enquiry response drafting with officer approval
    • Policy document search with citation
  • Retail

    The challenge

    • Margin pressure makes infrastructure cost a board-level number
    • Seasonal peaks an order of magnitude above baseline
    • Inventory truth split across channels

    What we build

    • Cost per order tracked as a product metric
    • Elastic capacity sized to real peak data
    • Channel reconciliation against the physical record

    AI opportunities

    • Demand forecasting and replenishment
    • Product content generation at catalogue scale
    • Customer service deflection on order status
  • Manufacturing

    The challenge

    • Operational technology that predates the internet
    • Telemetry volumes most platforms are not built for
    • Safety consequences for getting it wrong

    What we build

    • Read-only integration with OT boundaries respected
    • Stream processing sized for peak, not average
    • Failure modes designed before the happy path

    AI opportunities

    • Predictive maintenance from sensor telemetry
    • Quality inspection support
    • Production scheduling optimisation
  • Education

    The challenge

    • Extreme seasonality around enrolment
    • Minors' data under specific protection rules
    • Institutional systems that resist replacement

    What we build

    • Elastic capacity sized to enrolment peaks
    • Data minimisation by default
    • Integration rather than rip-and-replace

    AI opportunities

    • Admissions document processing
    • Student enquiry response with staff oversight
    • Timetabling and resource allocation
  • Hospitality

    The challenge

    • Distributed operations with high staff turnover
    • Reviews and reputation move faster than management reporting
    • Thin margins on high transaction volume

    What we build

    • Operations tooling designed for a busy Tuesday, not a demo
    • Reporting that reaches managers before the guest complains
    • Cost per cover tracked continuously

    AI opportunities

    • Review sentiment clustering and response drafting
    • Demand-based staffing recommendations
    • Multi-language guest communication

Ready to see what AI can do for your business?

Book a discovery call. We'll map your workflows, find the automation opportunities, and show you what a Scale NXT product could do for your bottom line.