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.
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.
Define the workload and baseline, qualify candidates, configure and adapt only if justified, obtain human acceptance, then roll out with monitoring and handover. Failed acceptance returns to qualification or configuration.
- Define the workload
Tasks, baseline and acceptance criteria
- Qualify candidates
Exact release, rights, hardware and failure modes
Same authorized task and corpus · same access rules
- Search baseline
No answer-generation model
- Candidate A
Exact release and configuration
- Candidate B
Exact release and configuration
- Search baseline
- Configure; adapt if justified
Compare prompting, retrieval and fine-tuning
- Human acceptance
Named owner reviews evidence before release
Compare on held-out cases · human acceptance · no winner assumed
- Controlled rollout
Monitoring, rollback and operating handover
Evaluation and security across every stage.
If acceptance fails: revise the candidate or configuration.
Compare candidates against one workload, baseline and acceptance criteria.
The decision
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.
Qualify the model
Design example
A model decision you can inspect
Design scenario with proposed checks. Technical record in English.
MODEL / 01
A maintenance planner needs the applicable manual revision and a cited answer about a spare-part record. The assistant must not provide machine commands or safety-critical operating advice.
Does answer generation improve this task enough to justify its operating cost and failure modes?
Establish the baseline
Permission-filtered BM25 search returns the authorized passages and their revisions. Assess whether the planner can find a usable answer.
Qualify exact candidates
Compare Ministral-3-3B-Instruct-2512 and Qwen3-8B using the same authorized retrieval and buyer task. These are example candidates, not a shortlist winner.
Freeze the configuration
Pin immutable release revisions or digests for weights, tokenizer and chat template; record runtime version, device, context and sampling settings, plus corpus/index revision, parser, chunking and access policy. Treat each quantization as a separate configuration.
Decide from held-out evidence
The named client owner sets pass/hold criteria before evaluation. Keep the acceptance set out of tuning data; assess retrieval quality separately from answer quality.
No winner selected. Keep the search baseline until an authorized evaluation establishes additional user value within the agreed constraints.
Explore the technical record
Proposed checks, not results
Exercise outdated and conflicting manuals, missing records, unauthorized documents and instructions embedded in source text. Check source selection, citation support, refusal and whether the planner reaches the right record. Latency, operating cost and operator workload remain unmeasured qualification questions.
Configuration before adaptation
Repair retrieval, access filtering, templates and inference settings first. Fine-tune only an evidenced residual gap with licensed data and a held-out regression set. A smaller model can be a starting candidate; adaptation is not its prerequisite.
Release record and ownership
Record the accepted configuration, unresolved failures, rollback reference and monitoring owner. Hosted API terms, downloaded weights and inference software have separate rights and data-processing conditions. A model label alone does not establish confidentiality or sovereignty.
A Product Decision Review can define the comparison and recommendation. A quoted Mission can include a named evaluation harness, configuration or adaptation task. Client acceptance and ongoing operation remain explicit.
Configure and adapt
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.
Control the boundary
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.
Choose the operating arrangement
Model rights and hosting are separate decisions. Open-weight models can run in your environment or through a managed service. Compare the complete data flow, operating capacity and lifecycle obligations for your workload.
| Dimension | Self-managed inference | Managed inference |
|---|---|---|
| Model rights | Check the exact model release and licence | Check model rights and service terms |
| 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 |
| Lifecycle | Own updates, security maintenance, regression checks and rollback | Check version support, deprecation and portability |
Evidence and handover
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.
Frame a model and deployment decisionTechnical depth
Architecture view
Start with a representative workload, baseline and acceptance criteria. Qualify the exact model release for the target hardware and operating boundary. Configure inference, compare prompting and retrieval, and fine-tune only when the evidence justifies changing model weights. Evaluate the complete system before a named owner accepts controlled rollout; maintain regression, security and rollback checks afterward.
Engineering detail
Mistral and other open-weight models; GGUF model packaging where supported; quantization methods such as AWQ or GPTQ where compatible with the target hardware and runtime; 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.
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.