Capabilities
Platform Overview
From time-series data to reliable, explainable decisions
Build high-accuracy, deterministic models in seconds — without complex data science workflows.
Our platform transforms raw historical data into transparent, production-ready intelligence for real-time decision-making.
Why it’s different:
- Deterministic, not black-box
- Fully explainable models
- Minimal data science overhead
- Designed for real-world deployment
The result: faster decisions, greater operational stability, and full transparency into how outcomes are determined.
How It Works
Deployment options
Choose the deployment model that fits your environment.
Reliable Insights can be deployed as a hosted SaaS platform, a containerised solution within your infrastructure, or within restricted OT environments where data movement is limited or not possible.
Option 1: Hosted
For teams that want fast setup, centralised access, and minimal infrastructure overhead.
Best for:
- Business users
- Analytics teams
- Multi-site monitoring
- Rapid pilots and proof-of-value projects
Option 2: Containerised
Run Reliable Insights inside your own infrastructure, close to your operational systems and data sources.
Best for:
- Production environments
- Industrial sites
- Sensitive operational data
- Customers with internal IT or security requirements
Option 3: OT / edge networks
Deploy within constrained operational technology environments, including local networks where connectivity is limited or controlled.
Best for:
- Manufacturing sites
- Energy assets
- Remote equipment
- Resource constrained applications
Whichever option you choose, the same explainable forecasting, anomaly detection, soft sensing, and causal analysis capabilities are available across the Reliable Insights platform.
Forecasting
Trusted predictions support proactive decision making
Our AI-powered approach delivers measurable impact across every level of the organisation. By combining advanced analytics with practical industry experience, we help clients work smarter, respond faster, and operate with greater clarity and confidence.
Examples of forecast areas
Why it matters
Better planning, fewer surprises, improved safety, lower costs and more resilient operations.
Anomaly Detection
Spot non-normal behaviour before it impacts operations
Real-time anomaly detection highlights behaviour that deviates from normal patterns, in the context of influencing variables (load, environment, weather, production state, etc.). Whether the issue is mechanical wear, a data quality problem or an electrical spike, the early identification of issues enables teams to quickly and effectively provide targeted responses.
Example use cases
Why it matters
Early detection prevents failures, reduces operational cost, and improves reliability, often without deploying new sensors or hardware.
Causal Links
Understand what drives key performance and risk
Many operational challenges arise from the combined influence of multiple interacting factors. Understanding these causal drivers unlocks better interventions, more confident decision-making and engineering insight.
What is analysed
Why it matters
Causal clarity enables engineers, planners and regulators to act decisively, improving outcomes with faster, more targeted interventions.
Soft Sensors & Simulation
Virtual measurements and scenario modelling
Soft sensors estimate values that can’t be directly or reliably measured. When physical instrumentation is costly, damaged, impractical or unavailable, virtual sensors give you a complete and continuous view of your system’s condition.
Once the underlying relationships are modelled, these same capabilities enable powerful simulation and what-if analysis, helping you understand the impact of potential interventions before taking action.
Example applications
Why it matters
Soft sensors increase resilience, improve control strategies and reduce reliance on expensive or failure-prone physical hardware.
