Hyperion connects product intent, architecture, data and evaluation before choosing predictive ML, optimisation, perception, language systems — 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 combines product leadership with scoped model engineering and cybersecurity work. A technical mission defines the configuration, adaptation, integration and handover to be delivered; specialist testing and independent qualification are agreed explicitly.
Build product capacity around your next milestone.
Senior product leadership, approved company knowledge and scoped AI workflows for industrial technology businesses. Start with one decision or one workflow.
01
Build your product function
For technical founders and solo specialists developing an industrial product: establish customer evidence, a product decision and a credible next milestone before hiring a full product department.
A customer/problem brief, prioritised MVP or pilot scope, and a responsibility map.
Illustrative scenario
Milestone: Choose the first test for an industrial inspection product before commissioning a prototype.
Hyperion · product lead
Frame the customer problem, scope the experiment and set acceptance criteria; engineering only where quoted.
Founder / client decision owner
Provide customer access and existing evidence; approve spending and customer commitments.
AI assistance
Organise approved notes, link sources and draft alternatives for human review.
Specialists & operating counterpart
Name engineering, safety and cybersecurity specialists, and a field operator before any build or physical pilot; legal review where needed.
Acceptance gate: The founder accepts the problem brief, test scope and resourcing plan: proceed, revise or stop.
For existing product teams: resolve a bottleneck in research, feedback, requirements or release preparation. Agree the review rules, test the assisted workflow and hand it over to a named owner.
An approved knowledge set, a bounded workflow, evaluation examples and operating instructions.
Illustrative scenario
Milestone: Turn recurring equipment field reports into a reviewed product-change decision.
Hyperion · product lead
Design and evaluate one assisted workflow; hand over review rules and operating instructions.
Founder / client decision owner
The product owner approves priorities and accepts the workflow; the team runs the recurring review.
AI assistance
Group approved reports, trace sources and draft a change brief; no release or machine authority.
Specialists & operating counterpart
Service staff verify field context; engineers and relevant specialists validate changes; the operator authorises rollout and recovery.
Acceptance gate: Agree source accuracy, accepted output, review effort and rework against the current process before adoption.
Hyperion leads the agreed product work. Your organisation retains business commitments and the decisions assigned to it. Engineering, field testing, safety, cybersecurity and legal work require named delivery capacity.
Each engagement is scoped and quoted individually.
Choose the model around the work and the data.
Evaluate Mistral, Qwen and other open-weight or hosted candidates on representative tasks, licence terms, languages, hardware and operating constraints. Use retrieval, configuration or fine-tuning when evaluation justifies it. Confidentiality depends on the complete data path: access, hosting, tools, logs, backups and operational ownership.
Establish recurring product leadership under a written Fractional CPO mandate.
Explore the Product Team Design Canvas
Fictional worked example · editable planning aid
From evidence to a human decision
An equipment team is deciding how to respond to recurring field reports. AI can organise approved evidence and draft alternatives; the product owner checks the sources and approves the decision. Engineering validates any product change.
Prepare the evidence
Input
Approved interviews or field reports
Output
Source-linked themes and gaps
Responsibility
AI assists; the source reviewer verifies.
Challenge the proposal
Input
Verified evidence and alternatives
Output
A decision brief with assumptions and the next experiment
Responsibility
Hyperion leads the agreed product work; the client owns business commitments.
Approve and hand over
Input
Reviewed decision brief and acceptance criteria
Output
A mission with named participants, approvals and evidence to collect
Responsibility
The authorised person approves; qualified specialists carry out delivery.
Fictional worked example · editable planning aid
Product Team Design Canvas
Map the next milestone, the people accountable for it and the work AI could assist. Edit the suggestions and identify missing specialist capacity before committing.
Entries stay in this page’s memory and disappear on reload. No entries are sent to a model or saved automatically. Download a copy before leaving.
Your draft division of work
Suggestions use explicit rules based on your situation, bottleneck and data boundary. They are starting points to edit, not a readiness score or an autonomous team.
A named human reviews the evidence before a commitment or system change. Product release, safety and physical-machine authority stay with the responsible people and validated systems.
Your milestone, people, data choice and edited notes are not included. You can remove the situation on the contact page.
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.
P01
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
P02
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
P03
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
P04
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
Start with the task
Different tasks need different methods.
Compare an appropriate non-AI baseline, the operating constraints and acceptance evidence before choosing a model family. These are alternatives, not stages.
Prediction & forecasting
Candidate approaches
Statistical baselines, regression and time-series models.
Evidence to establish
Time-based holdouts, calibration, drift and the cost of errors.
Optimisation & planning
Candidate approaches
Rules, mathematical optimisation and simulation.
Evidence to establish
Feasibility, constraint violations and sensitivity to changing assumptions.
Perception & inspection
Candidate approaches
Signal processing, vision models and multimodal models where justified.
Evidence to establish
Representative conditions, false positives, missed detections and uncertainty.
Knowledge & language
Candidate approaches
Search, retrieval and language models selected for the task.
Evidence to establish
Source authority, permissions, factual accuracy and abstention.
Governed action
Candidate approaches
Bounded workflows and agent harnesses around permitted tools.
Evidence to establish
Identity, policy checks, approvals, outcome verification, refusal and recovery.
Language and knowledge options
Choose what the evidence justifies.
These six options concern language and knowledge work. They are not a required sequence or a ranking of model size. Retrieval, adaptation and compact models can be combined; compare only approaches relevant to the task.
Workflow, rules or deterministic logic
Use when
The behaviour can be specified and maintained without a learned model.
Evidence needed
Test coverage and maintenance effort against the acceptance criteria.
Search and retrieval
Use when
The task is locating authoritative information, records or procedures.
Evidence needed
Relevance, permissions, source coverage and information freshness.
Existing model with governed context
Use when
An existing model can perform the task with approved instructions or retrieved evidence.
Evidence needed
Task quality, grounding, refusal, cost and operating limits.
Adapt an existing model
Use when
Representative evaluation identifies a gap that adaptation could address.
Evidence needed
Data rights, held-out improvement, regression tests and adaptation cost.
Task-specific compact model
Use when
A compact candidate may fit latency, memory, privacy or cost constraints.
Evidence needed
Compare existing small models, adaptation or distillation on target hardware.
A differentiated requirement and defensible data justify assessing this exceptional option.
Evidence needed
Compare alternatives; establish capital, data, evaluation and lifecycle ownership.
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.
01 / DIRECT
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
02 / SCOPED
Scoped before commitment
A scoped Mission may include named implementation by Hyperion. The proposal assigns remaining delivery, client operating ownership and qualified independent assurance explicitly.
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.
A01
Product and architecture decision pack
Intended use, system boundary, alternatives, architecture views, interfaces, decision records and a build, buy, partner or stop recommendation.