Edge & On-Premise AI
AI that runs inside your walls — so your data never leaves them. Frontier-grade capability your competitors rent from the cloud, except yours is sovereign by architecture, not by promise. Designed and deployed for organisations where 'trust our cloud' is not an answer, it is a liability.
The pattern we keep seeing.
Some organisations cannot put their data in someone else's cloud. Patient records, legal files, financial data, government-adjacent work — for these, "trust our cloud" is not an answer, it is a liability. We design and deploy AI infrastructure that runs entirely on your own hardware, on-site or in your private environment. Full capability. Full compliance. Full control.
Public-cloud AI is off the table for your workloads.
Privilege, residency or classification rules out sending the work to a third-party model. The brief is not whether to adopt AI, it is how to adopt it without surrendering control of the data.
"Trust our cloud" is not an answer your auditor accepts.
A vendor SOC report and a residency clause do not satisfy obligations written in legislation. Sovereignty has to be architectural, not contractual.
Your competitors are deploying anyway. Behind their own walls.
The organisations winning this decade aren't the ones that avoided AI on compliance grounds. They are the ones that deployed it without surrendering control of their data.
What is Edge AI?
AI infrastructure that runs entirely inside your walls — on-site or in your private environment — so sensitive data never leaves them. Built for organisations where compliance is non-negotiable.
AI that runs inside your walls — so your data never leaves them. Frontier-grade capability your competitors rent from the cloud, except yours is sovereign by architecture, not by promise. Designed and deployed for organisations where 'trust our cloud' is not an answer, it is a liability.
Edison AI designs and deploys AI infrastructure that runs entirely inside a client's own environment — on-site, in a private colocation tenancy, or behind an organisation's existing security boundary — for Australian mid-market and enterprise organisations in health, legal, finance, professional services and government-adjacent sectors with data sovereignty and regulatory compliance obligations that prohibit public-cloud AI. Includes on-premise model deployment (open-weight families and private-tenancy closed models), compliance-first architecture, integration with existing identity and audit tooling, and ongoing operation across a 10–14 week build with a 90-day optimisation window.
The shifts you can't postpone.
Three reasons on-premise AI moved from research project to deployable system this year.
- 01
Open-weight models caught the frontier.
Llama, Mistral, Qwen and DeepSeek now run within points of the closed leaders on most enterprise workloads. The sovereignty premium dropped to near zero.
- 02
Inference hardware became economic.
Professional-grade GPUs now deliver mid-market-grade throughput on a single chassis. The capex story works for a single department, not just a national bank.
- 03
Regulators stopped waiting.
Privacy commissioners, prudential regulators and procurement bodies are publishing AI obligations with teeth. The organisations that wired sovereignty into the architecture early will not be retrofitting it under deadline.
What you get.
- 01
On-premise model deployment — production-grade language and analysis models running locally, sized to your actual workloads, not a generic spec
- 02
Compliance-first architecture — designed against your regulatory obligations from day one, with the security and audit trail your industry demands
- 03
Systems integration — wired into the tools and workflows your team already uses, so the secure option is also the easy one
- 04
Ongoing operation — monitoring, model updates, and performance tuning, run by us or handed to your team with full enablement
- 05
Hardware specification + procurement guidance for the GPU/server footprint your workload actually needs
- 06
Written sovereignty standard your auditor, board and customers can read
Where this shows up.
Practical examples, not promises. Every engagement is scoped to your specific business.
Private legal-research agent running on a law firm's own hardware, reading matter files that never leave the firewall
Patient-record summarisation inside a hospital VPN, with audit trails wired into the existing clinical governance
Sovereign analyst-copilot for a defence-adjacent consultancy, running on an isolated network
On-premise document classifier for a regulator, with full lineage from input to decision
Internal policy Q&A for a federal agency, answering from controlled-access source material
Private banking research assistant, querying client portfolios without exposing them to any third-party model
Mining-sector field reports drafted on-rig, syncing only when the operator chooses
The engagement.
- Step 01
Sovereignty audit
Weeks 1–2: map regulated workloads, classify data sensitivity, document the obligations the architecture must hold. The audit becomes the design brief.
- Step 02
Architecture & model selection
Weeks 2–4: pick the open-weight or licensed models that match your workload, design the on-prem stack, size hardware, and write the deployment plan against your compliance obligations.
- Step 03
Build & validate on-prem
Weeks 4–10: deploy inside your environment, integrate with your existing tools and identity systems, validate against historical cases and adversarial inputs before a single live query lands.
- Step 04
Operate & enable
Weeks 10–14 + 90 days: monitoring, model refresh cadence, performance tuning, and full enablement so your team can run the system, with retained support optional after the 90-day window.
What changes.
AI capability without data egress.
The same drafting, summarising, classification and reasoning your competitors use on public cloud, running entirely inside your boundary. No third-party model ever sees the data.
A sovereign architecture your auditor can read.
Documented data flow, identity controls, audit trail and model-refresh standard. Defensible against regulator review and customer-trust questions in writing, not in conversation.
An economics story that holds at scale.
Per-token cloud-AI costs become a line item that grows with usage. On-premise amortises the hardware once and runs flat. The crossover is closer than most CFOs expect.
Who this works for.
This is for you if…
- You operate in health, legal, finance, professional services, or a government-adjacent sector
- You have data-residency, privilege or classification obligations that prohibit public-cloud AI
- You want frontier-grade capability without third-party data exposure
- You have or can sponsor on-prem infrastructure and a security team to run it
- You take compliance seriously and want it baked into the architecture, not bolted on
Not the right fit yet if…
- Your workloads are not regulated and public-cloud AI is acceptable to your risk team
- You have no on-premise infrastructure footprint and no appetite to build one
- You need a deployment live this month and have no in-house security capacity
What buyers ask first.
“Won't on-premise AI lag the frontier?”
Not meaningfully, anymore. Open-weight families closed the gap in 2025–26, and where a closed model is genuinely required we deploy it under private tenancy with no data egress. The sovereignty premium that existed two years ago is mostly gone.
“We don't have a GPU cluster.”
You don't need one for most deployments. A single inference node with two professional GPUs is enough to run a department-scale system. We size for your actual usage and write a runway plan so capacity is a planned, not surprise, decision.
“Will this lock us into your team?”
No. The architecture, models, operating standard and runbooks are yours. Your security and platform teams can run the system after the optimisation window. We stay on call for major model updates if you want, but we are not the only people who can operate it.
Common questions.
What does Edge or on-premise AI actually mean?
Models, vector databases and orchestration that run on hardware you control — either on-site in your own data centre, on a rack in a colocation facility you contract directly, or inside a tenancy that never sends prompts or outputs to a third-party AI provider. No data egress to public-cloud AI APIs by design.
Why would we choose on-premise over a public-cloud AI service?
Three reasons. Regulatory obligations that prohibit cloud egress (health, legal privilege, classified-adjacent work). Contractual data-residency commitments to your own customers. And the long-term economics of inference at scale, where on-prem amortises faster than per-token API spend once volume is real.
Which models can run on-premise?
The serious open-weight families — Llama, Mistral, Qwen, DeepSeek and their fine-tunes — are now within a few points of frontier-closed models on the workloads most organisations care about. Where a closed model is genuinely required we deploy it under a private-tenancy contract that holds data inside your boundary.
What hardware do we actually need?
Depends on the workload. A single inference node with two professional GPUs handles a department-scale chat or classification system. Multi-user reasoning workloads scale up to a small GPU cluster. We size for your actual usage, not a generic spec, and document the runway so capacity is a planned decision, not a surprise.
How do we keep models current without internet egress?
Scheduled offline model refreshes from a controlled source. The cadence is part of the operating standard — typically quarterly for foundation models, monthly for any in-house fine-tunes. Updates are reviewed and signed off before they reach production, never auto-pulled.
What's the investment range and timeline?
Engagements run 10–14 weeks for the build plus a 90-day post-launch optimisation window. Cost depends on hardware footprint, model licensing and the depth of integration into your existing stack. We scope on a per-engagement basis after the sovereignty audit, never as a generic shelf-price.
Can you integrate with our existing identity, audit and SIEM tooling?
Yes. Active Directory / Entra, Okta, SSO, your existing audit logging, your SIEM. The architecture is designed to sit inside the controls your security team already operates, not alongside them.
What happens after we go live?
90-day optimisation window: monitoring, tuning, model-refresh rehearsals and a final review. Retained fractional support is optional beyond that. Most clients run the system in-house after the window, with us on call for major model updates.
Other implementation services.
- S02
AI Agents
Custom-built AI agents that handle the repetitive parts of a workflow: qualifying leads, drafting replies, updating records and chasing follow-ups, with a human in the loop wherever judgement matters.
Learn more - S05
Bespoke AI Systems
Multi-agent AI systems that plan, coordinate and execute across operations.3–7 agents working in coordination with documented handoffs and human approval gates, built inside your existing stack.
Learn more - S03
Workflow Automation
Practical workflow automation that connects the tools you already pay for, with AI inside the steps that need reading, drafting or judgement.
Learn more - S06
Operating Dashboards
A custom operating dashboard that pulls the numbers and signals you care about from your existing systems, summarises them in plain English, and tells you what changed since last week.
Learn more - S04
AI Marketing & Search Visibility
One content + citation engine that gets you found by Google and cited by AI, so your business shows up in both surfaces where buyers now search, and the work compounds every month.
Learn more - S01
AI Readiness Audit
A practical audit of your workflows, tools, bottlenecks, and team capability to identify the highest-return AI opportunities.
Learn more