How SOLVER-AI delivers per-asset predictive services you can rely on

This page explains how SOLVER-AI is engineered to deliver predictive services that can be integrated into real operational systems — and the boundaries within which those services are designed to operate.

Our focus is not on novelty or tooling choices, but on reliability, lifecycle management, and operational trust.

SOLVER-AI delivers prediction as a managed service, not as a one-off artefact.

We take responsibility for:

• building predictive models aligned to a specific operational decision,
• running inference as a live service,
• and updating models as behaviour changes over time.

Rather than handing over model files or notebooks, SOLVER-AI exposes stable prediction APIs designed to be consumed directly by customer systems.

Customer data and derived model artefacts are isolated per deployment and are not used to train models across customers.

This approach allows teams to use prediction without needing to operate or maintain machine learning infrastructure internally.

Predictive services in SOLVER-AI are deliberately isolated from one another.

Each service:

• corresponds to a specific asset, asset class, or equipment variant,
• runs independently of other predictive services,
• and can evolve without affecting downstream integrations.

Failures or changes in one predictive service are contained and do not propagate to others.

This isolation is critical for per-asset modelling, where behaviour varies across real-world systems and changes over time. It allows predictive services to be updated, refined, or retired without breaking customer workflows.

SOLVER-AI is designed to support long-running predictive services, not short-lived demonstrations.

The execution environment prioritises:

• predictable behaviour over peak performance,
• recoverability over aggressive optimisation,
• and operational stability over experimentation.

This design reflects the reality that predictive services must continue to operate as data changes, systems evolve, and usage patterns shift.

SOLVER-AI is provisioned using repeatable, automated processes.

This ensures:

• consistent environments across deployments,
• reduced operational risk from manual configuration,
• and the ability to recreate or evolve platform components in a controlled way.

Repeatable provisioning supports both early-stage PoCs and later-stage expansions without introducing hidden complexity or deployment drift.

SOLVER-AI is designed with explicit data responsibility boundaries.

In particular:

• customer data is used solely to deliver the agreed predictive services,
• data access is restricted to what is operationally required, following least-privilege access controls,
• and data ownership remains with the customer.

We avoid opaque data reuse, silent aggregation, or hidden secondary purposes. These boundaries are intentional and central to building operational trust.

Security measures are applied consistently across the platform to protect both data and access.

This includes:

• network-level protections and controlled ingress,
• authenticated and authorised access to services,
• and default use of multi-factor authentication.

Security is treated as a baseline operational requirement, implemented to support safe operation rather than marketing claims.

SOLVER-AI is designed to scale along dimensions that matter operationally:

• number of assets or variants,
• frequency of prediction requests,
• and computational complexity of predictive services.

Scaling is approached incrementally and deliberately, ensuring that increases in load do not compromise reliability or predictability of behaviour.

This aligns technical scaling with real-world usage and value creation.

Predictive services evolve as data, usage, and system behaviour change.

In SOLVER-AI, this evolution is controlled, not implicit.

Models are updated:

• in the background,
• without disrupting inference endpoints,
• and without introducing silent changes to external behaviour without validation and agreement on scope.

This avoids unexpected shifts that could undermine downstream decision-making or automation.

SOLVER-AI is currently operationally validated at Proof-of-Concept grade for predictive APIs.

The platform does not claim:

• production SLAs,
• high-availability guarantees,
• or closed-loop automation by default.

SOLVER-AI provides prediction as a service. Decisions, automation, and downstream actions remain under customer control unless explicitly agreed otherwise.

Automation, optimisation, and tighter operational coupling are pursued only when validation and operational readiness justify it, and in collaboration with customers.

SOLVER-AI is engineered to bridge the gap between monitoring and action by delivering prediction in a form that operational systems can consume.

Our design choices prioritise:

• clarity over complexity,
• responsibility over hype,
• and long-term operability over short-term demonstrations.

If you would like to understand how these principles apply to a specific use case, we're happy to discuss them in context.