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.

Discuss the system decision Open the Product System

Applied AI product architecture

Edge intelligence
Governed knowledge systems
Data, evaluation & learning systems
Mission-critical architecture

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
Explore open-weight models
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
Explore industrial RAG
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
See the Product System
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
Explore embedded and edge systems

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.

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.
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.
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.
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.
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.
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 decision Model strategy diagnostic Review engagement options

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.

Decisions to resolve

Possible decision artefacts

Decisions to resolve

Possible decision artefacts

Decisions to resolve

Possible decision artefacts

Decisions to resolve

Possible decision artefacts

Workflow, rules or deterministic control

Retrieval before generation

General model with governed context

Adapt an existing model

Distil and optimise a task-specific SLM

Train a model from scratch

Product and architecture decision pack

Dataset and evaluation specification

Technical assurance pack

Operating and change-control plan

Choose the system before choosing the model.

Decisions to resolve

Possible decision artefacts

Decisions to resolve

Possible decision artefacts

Decisions to resolve

Possible decision artefacts

Decisions to resolve

Possible decision artefacts

Workflow, rules or deterministic control

Retrieval before generation

General model with governed context

Adapt an existing model

Distil and optimise a task-specific SLM

Train a model from scratch

Product and architecture decision pack

Dataset and evaluation specification

Technical assurance pack

Operating and change-control plan

Choose the system before choosing the model.