Applied AI systems

Intelligence that earns its place in the product.

Hyperion connects product intent, system architecture, datasets and evaluation before choosing RAG, fine-tuning, a task-specific small language model — or no model at all.

The objective is not more AI. It is the least complex system that can meet the product's acceptance bar under real operating constraints.

Hyperion does not position itself as a general engineering bench. Technical fluency is used to make better product decisions, challenge assumptions, define evidence and coordinate the critical specialists required by the mandate.

Discuss the system decisionOpen the Product System
Applied AI system map with four connected pillars: edge intelligence, governed knowledge systems, data and evaluation systems, and mission-critical architecture.

Applied AI product architecture

  1. Edge intelligence
  2. Governed knowledge systems
  3. Data, evaluation & learning systems
  4. Mission-critical architecture
ProductArchitectureEvidenceOperations

Four connected pillars

A model is one component. The product is the whole system.

Each pillar resolves a different production risk. They are designed together because model behaviour, data quality, architecture and field operations change one another.

  • Edge intelligence

    Task-specific SLMs and compact multimodal intelligence

    Bounded intelligence for products where latency, privacy, power, connectivity or offline operation shape what is viable.

    Decisions to resolve

    • Intended task, no-model baseline and measurable acceptance bar
    • Base-model, adaptation, distillation and quantization path
    • Latency, memory, power, cost, update and rollback envelope

    Possible decision artefacts

    • Model and deployment decision record
    • Edge evaluation and rollback specification

  • Governed knowledge systems

    Industrial RAG, retrieval and authoritative operational knowledge

    Knowledge products that preserve source authority, permissions, provenance and abstention instead of hiding uncertainty behind fluent answers.

    Decisions to resolve

    • Corpus authority, ownership, permissions and change control
    • Search, hybrid retrieval, reranking, citation and abstention behaviour
    • Offline, edge, multilingual and operator-workflow boundaries

    Possible decision artefacts

    • Knowledge and retrieval architecture
    • Grounding, permission and answer-quality evaluation plan

  • Data, evaluation & learning systems

    The evidence infrastructure behind every intelligent behaviour

    Datasets, simulations, evaluation suites and feedback loops treated as governed product assets rather than one-off inputs to a model build.

    Decisions to resolve

    • Scenario coverage, dataset lineage, rights, quality and versioning
    • Offline, simulation, hardware-in-the-loop and field evaluation mix
    • Regression, incident learning, monitoring and release thresholds

    Possible decision artefacts

    • Dataset and evaluation specification
    • Acceptance matrix and learning-loop design

  • Mission-critical architecture

    Product, solution, system and software architecture

    An accountable architecture across hardware, software, AI, data, controls, safety, security, people, edge and cloud — designed around failure as well as nominal operation.

    Decisions to resolve

    • System boundary and AI-versus-deterministic responsibility
    • Hardware, edge, cloud, interfaces, suppliers and integration path
    • Failure behaviour, human authority, cybersecurity and operability

    Possible decision artefacts

    • System context, interfaces and architecture decisions
    • Failure, assurance and operating-boundary specification

The least-complex-solution ladder

Complexity must be admitted by evidence.

Start at the lowest rung that could satisfy the acceptance criteria. Move upward only when measured failure justifies the additional data, infrastructure, assurance and operating burden.

  1. Workflow, rules or deterministic control

    Use when

    The behaviour can be specified, tested and maintained without a learned model.

    Advance only when

    Measured variation makes the bounded rule set insufficient.

  2. Retrieval before generation

    Use when

    The problem is locating authoritative information, records or procedures.

    Advance only when

    Search and a deterministic workflow cannot meet the user's decision need.

  3. General model with governed context

    Use when

    An existing model plus controlled retrieval meets the task, grounding and risk envelope.

    Advance only when

    Evaluation shows systematic task or domain failure that context cannot resolve.

  4. Adapt an existing model

    Use when

    Fine-tuning or parameter-efficient adaptation can correct a bounded, evidenced capability gap.

    Advance only when

    The adapted model still misses deployment, cost, latency or control constraints.

  5. Distil and optimise a task-specific SLM

    Use when

    A smaller model can preserve the required behaviour inside a tighter edge, privacy or cost envelope.

    Advance only when

    No available model family can meet the evidenced requirement through adaptation or distillation.

  6. Train a model from scratch

    Use when

    The requirement is strategic, sufficiently differentiated and supported by defensible data, capital and lifecycle ownership.

    Advance only when

    This is the exceptional last rung, not a default destination.

Accountability before implementation

One decision owner. An explicit delivery boundary.

Hyperion owns the coherence of the product and architecture decisions. Implementation is never implied: the engagement names what Hyperion handles directly, what remains with client engineering and what requires qualified specialists.

Led directly by Hyperion

The decision system stays accountable end to end.

  • Product intent, intended use, non-goals and system boundary
  • Solution, system and software architecture decisions
  • Dataset strategy, evaluation design and acceptance thresholds
  • AI-versus-deterministic allocation and human-authority boundaries
  • Vendor, model, platform and implementation-route decisions
  • Evidence reviews, release gates and change governance

Scoped before commitment

Implementation ownership follows the requirement, evidence and available team — not a blanket promise.

  • Prototype, retrieval and evaluation-harness implementation
  • Model adaptation, distillation, quantization and edge packaging
  • Data engineering, labelling operations and synthetic-data pipelines
  • Platform, MLOps, fleet, cloud and enterprise-system integration
  • Independent safety, security, compliance and certification work
  • Production handover, support and ongoing operating ownership

Before work begins, the proposal names accountable owners, environments, acceptance evidence, handover conditions and ongoing model, data and change responsibilities.

Decision-ready artefacts

What a scoped engagement may produce

The artefact set follows the decision at hand. It is selected in scope; it is not presented as a generic promise of every engagement.

  • Product and architecture decision pack

    Intended use, system boundary, alternatives, architecture views, interfaces, decision records and a build, buy, partner or stop recommendation.

  • Dataset and evaluation specification

    Scenario taxonomy, dataset requirements, lineage, evaluation suites, acceptance thresholds, regression policy and field-evidence plan.

  • Technical assurance pack

    Failure modes, threat and misuse cases, human oversight, grounding and abstention behaviour, rollback conditions and evidence gaps.

  • Operating and change-control plan

    Release gates, model and data versioning, observability, incident learning, supplier responsibilities, handover and lifecycle ownership.

Choose the system before choosing the model.

Bring the product decision, the operating constraints and the evidence you have. The first task is to identify the simplest credible path forward.

Discuss the system decisionModel strategy diagnosticReview engagement options