Capability · applied inside an engagement
Open-weight models & sovereign AI
Choose the model for your workload. Configure it for your hardware. Build an AI system whose data flows, permissions and operating costs you can explain.
Request a quoteHyperion 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.
From model choice to a system your team can own.
Specialist advice and bounded implementation for startups, scaleups and established enterprises. Evaluate Mistral, Qwen, DeepSeek and other suitable model families against your own tasks. The recommendation may be an open-weight model, a managed service, retrieval without fine-tuning, or no language model.
Evaluate and select
Build a representative task set and compare quality, failure modes, language coverage, latency, hardware needs and total operating cost. Record the exact model revision, licence and acceptance criteria.
Configure and serve
Set context limits, sampling parameters, structured outputs, batching and concurrency. Choose quantization and a serving runtime such as vLLM or llama.cpp for the target hardware, then measure the trade-offs.
Retrieve or fine-tune
Compare prompting and retrieval with supervised adaptation. Where justified, define a rights-cleared dataset, held-out evaluation and LoRA or QLoRA fine-tuning plan. Track regressions, provenance and adapter compatibility.
Build bounded agents
Connect retrieval, tools and workflows with explicit permissions, budgets and stopping rules. Evaluate tool selection, prompt injection, data access, human approval and recovery from ambiguous effects.
Design the data boundary
Choose on-premises, edge, private cloud or a justified hosted path. Map storage, logs, telemetry, support, backups, keys and network egress. Confidentiality follows verified controls across that whole path.
Measure and hand over
Deliver the agreed reference implementation, evaluation pack, configuration baseline and runbook. Define monitoring, change control, rollback and ownership so the client can operate and improve the system.
What your team receives
- A workload-specific model decision record and evaluation plan
- A deployment and data-flow design, configuration baseline and adaptation strategy
- The code, evaluation results and operating handover explicitly agreed in the Mission scope
Buy an independent decision through a Review, agreed implementation through a Mission, or ongoing product authority through Executive Product Leadership. The Review excludes implementation. A Mission names the code, tests and handover included; specialist assurance and client operating responsibilities remain explicit.
Individually scoped and quoted after a fit conversation.
Are open weights the same as open-source AI?
No. Access to weights is one property. Open-source AI also concerns the freedoms and components needed to use, study, modify and share the system. Check the exact release and its licence; family names do not establish commercial rights.
Does a local or European model guarantee sovereignty?
No. Define who controls the data, keys, infrastructure, updates and exit path. European and Chinese-origin models can both be evaluated; model provenance, hosting, licences and the complete data flow are separate decisions.
Technical references · verify the exact release and context
Select & size
Deploy
Adapt
Govern & evaluate
A four-stage pipeline: select and size the model, then deploy, then adapt by fine-tuning, then govern and evaluate.
Buy an independent decision through a Review, agreed implementation through a Mission, or ongoing product authority through Executive Product Leadership. The Review excludes implementation. A Mission names the code, tests and handover included; specialist assurance and client operating responsibilities remain explicit.
Model selection and sizing for the task; a deployment topology (on-premise, edge, or private cloud); domain adaptation through fine-tuning or retrieval; an inference-serving layer; and a governance and evaluation loop, so quality is measured rather than assumed.
Mistral and other open-weight models; quantization (GGUF, AWQ, GPTQ) for the target hardware; serving with vLLM, llama.cpp or Ollama; LoRA/QLoRA fine-tuning; retrieval integration; evaluation harnesses for quality and regression; on-premise GPU and edge deployment.
Open-weight models vs proprietary API
Self-hosting can favour sovereignty, cost predictability and longevity when the licence, complete data flow and operating model support it.
| Dimension | Open-weight (you run) | Proprietary API |
|---|---|---|
| Ownership | You host the weights | Vendor-hosted |
| Data residency | Can remain in a verified perimeter | Depends on vendor regions, contracts and flows |
| Cost model | Infrastructure plus operations | Usage- and contract-based |
| Adaptation | Licence-dependent fine-tune / LoRA | Provider-dependent |
| Longevity | Pin a version you trust | Deprecation risk |