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Retrieval over approved sources
The model answers from documents and systems you approve, cites its sources, and says when it doesn’t know. Often the safest, fastest and lowest-cost way to make AI useful with your own knowledge.
Security & Deployment
The right AI, deployed the right way for your data.
AI deployment is not one-size-fits-all. We help you choose the right balance of capability, cost, data control and operational risk — from approved cloud models to private or on-premises options — and design human approval points, access controls, auditability and ownership boundaries around the workflow.
Deployment models
Five ways to run AI — each with trade-offs.
Managed / public cloud models
Capable models accessed as a service through approved accounts. Fastest to start and easiest to scale, with the provider operating the model.
- Often suits
- Lower-sensitivity workflows, pilots, drafting and knowledge tasks where approved terms and data handling meet your requirements.
- Consider
- Data handling terms, where processing occurs, retention settings, and whether inputs may be used to improve the service.
Enterprise controlled-cloud deployments
Models deployed inside your organisation’s cloud tenancy or an enterprise agreement, with your identity, network and logging controls around them.
- Often suits
- Organisations with an existing cloud platform and security team who want region selection, access control and auditability.
- Consider
- Region availability, tenancy configuration, identity integration, logging, and cost management.
Private cloud and dedicated environments
Models run on dedicated infrastructure reserved for your workloads, with tighter control over where data is processed and who can access it.
- Often suits
- Sensitive or regulated workflows that need stronger isolation, predictable performance or specific residency arrangements.
- Consider
- Higher operating cost, capacity planning, model updates and the support model required to run it reliably.
On-premises model options
Open-weight models deployed in your own data centre or secure facility, connected only to systems you approve.
- Often suits
- Workloads where information must stay within your own environment, subject to a client-specific assessment and approved controls.
- Consider
- Hardware, model capability trade-offs, patching and monitoring, and in-house or managed operational support.
Local models on approved devices
Smaller models running on approved workstations or edge devices for specific, bounded tasks — sometimes offline.
- Often suits
- Field or low-connectivity scenarios, and narrowly scoped tasks where a smaller model performs well enough.
- Consider
- Capability limits, device management, consistent updates, and how outputs are checked and recorded.
Ground, configure, integrate — and where appropriate, fine-tune
Making AI useful with your own knowledge.
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Configuration and integration
Prompts, guardrails, tools and workflow integration tuned to your process, identity and approval steps — so the AI fits the way work actually happens.
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Fine-tuning, where appropriate
Adjusting a model on curated examples when retrieval and configuration aren’t enough — for consistent style, classification or specialist language. Used selectively, not by default.
Decision framework
Nine questions that shape the right deployment.
Tick the ones that are critical for your workflow. It’s a quick way to see the conversation we’d have together — not an automated recommendation.
Shared responsibility
Clear about who owns what.
Melora
- Design and implement the agreed controls within our scope of work
- Document the workflow, data flows, approval points and known limitations
- Assess and document third-party provider risk for the options we recommend
- Support testing, monitoring set-up and handover to your teams
Your organisation
- Data governance, classification and approval of what information may be used
- Decisions on use, risk acceptance and human approval within your organisation
- Management of your own environment, accounts and access
- Ongoing ownership of the workflow and its outcomes
Third-party providers
- Risk is assessed and documented before recommendation
- Terms, data handling and processing locations are reviewed
- We don’t treat a vendor’s marketing claims as proof of security, residency or uptime
Specific responsibilities and liability are set out in each engagement agreement, not on this website.
Straight answers
Can you guarantee our data is secure?
No one can honestly guarantee that. We design controls around the workflow — access, logging, approval points and deployment choice — and assess them against your requirements. Assurance comes from your security processes and the controls actually implemented.
Can you work with highly sensitive or classified information?
Only after a client-specific assessment and with approved controls and environments. We don’t assume any option is suitable for every classification of information.
Do we need to train our own model?
Rarely. Retrieval over approved sources, careful configuration and integration usually deliver the outcome. Fine-tuning is used selectively where it adds clear value.
Start here
Start with the workflow — then choose the deployment.
Take the 5-minute AI maturity assessment for an instant readiness dashboard and report, then book a free 15-minute consultation with our AI consultant.
Prefer to talk? +61 451 423 002 · Support@meloradigital.com