AI infrastructure that lives on your floor — not someone else's.
We design, deploy, and continuously manage the full local AI stack for Australian organisations. Hardware in your data centre or hosted by us. Models tuned to your workload. Sovereign cloud failover so the lights stay on when local doesn't.
Hosted AI is fine, until it isn't.
For most workloads, cloud inference is the right answer. For some, it is not. When your data is sensitive, your latency requirements are real, your usage is heavy enough that token costs add up, or your regulator has an opinion about where customer data lives — local AI starts to make sense. We help you work out which side of that line you sit on, and then we build it.
Your data does not leave your perimeter.
For workloads governed by Privacy Act 1988, APRA CPS 234, or sector-specific obligations, the simplest defence is the data never going anywhere. Local inference keeps customer records, clinical notes, and operational data inside controls you already own.
Sub-100ms responses, every time.
When AI is in the customer-facing path — voice, real-time copilots, control loops — round trips to a hyperscaler in Sydney or Singapore stop being acceptable. Local inference takes that variability off the table.
Capex once, opex flat.
Token-based pricing scales with usage — which is great until usage scales. For high-volume workloads, owning the hardware (or having us host it on your behalf) flattens the cost curve and makes budgeting boring again.
Your models. Your guardrails. Your audit trail.
You decide which models run, which prompts they see, which tools they can call, and what gets logged. No quiet model swaps. No surprise policy changes. No emails about deprecation in ninety days.
Five layers. One service. Fully managed.
We do not drop a server and a model in a rack and call it AI. The work that matters is in the layers between — orchestration, observability, governance, the unglamorous operational discipline that keeps a production system running.
Hardware
GPU servers specified for your workload. Dell, Supermicro, Lenovo — sourced through our reseller channels at genuine market pricing.
Inference runtime
vLLM, TGI, or Triton — whichever fits the workload. Quantisation, batching, and concurrency tuned per use case.
Models
Open-weight foundation models — Llama, Qwen, Mistral, Gemma — selected, evaluated, and refreshed against your benchmarks.
Orchestration
Routing, retries, tool calling, RAG pipelines, evaluation harnesses. The plumbing that turns a model into an application.
Operations
Monitoring, alerting, capacity planning, patching, security updates, quarterly governance review. Day-two work, done.
Three places it can live.
You do not need a data centre to run local AI. You need the right model in the right place for the workload. We help you choose, and we run it either way.
On your premises
GPU hardware in a rack you already own — head office, branch, or factory floor. Best when latency or air-gapping is non-negotiable.
- Site survey and power / cooling assessment
- Hardware sourced and racked by us
- Out-of-band management for remote ops
In your data centre
Your existing colo or DC space. We deploy and operate the AI stack alongside your existing infrastructure, on hardware you own.
- Integration with your network and identity
- Existing change and incident processes respected
- You retain hardware title and asset register
Hosted by us
You get dedicated hardware — not shared tenancy — in Australian facilities we operate. Faster to stand up, no rack management on your side.
- Australian-hosted, sovereign by default
- Dedicated GPUs, not pooled
- Monthly opex, no capital outlay
What "fully managed" actually means.
The build is the easy part. Running an AI system in production for years — through model updates, hardware refreshes, security disclosures, and changing workloads — is where most projects fall over. That is the part we own.
Model lifecycle
Selection, evaluation, deployment, A/B testing, deprecation. New foundation models benchmarked against your workloads before anything changes in production.
Performance tuning
Throughput, latency, and accuracy continuously measured. Quantisation, batching, and routing adjusted as your usage patterns change.
Security & patching
OS, runtime, model server, and dependency patching on a predictable cadence. CVE response inside agreed SLAs. Audit logs you can actually read.
Capacity & scaling
Utilisation tracked against headroom. We tell you when you need more GPU — and when you can hand some back — before the workload tells you.
Monitoring & on-call
Twenty-four-seven monitoring with clear escalation. Same accountable SLAs that govern the rest of our managed services. Same month-to-month terms.
Governance reviews
Quarterly review of model behaviour, cost, risk, and roadmap. The kind of conversation a board would want to read the minutes of.
Local-first. Sovereign cloud second. Never offline.
Local hardware fails. Power events happen. Models occasionally need to be taken down for a controlled upgrade. When that happens, traffic routes seamlessly to AmmaCloud — our sovereign Australian inference platform — so your workflows keep running. When local comes back, traffic comes back with it.
How failover works
A traffic-aware gateway sits in front of every workload. Health, latency, and error budgets are checked continuously. If local degrades — for any reason — requests are routed to AmmaCloud inside a defined SLA. Nothing manual. Nothing waiting for a pager.
AmmaCloud runs the same model families as your local stack, hosted in Australian facilities under the same governance regime described on ammacatize.ai. Your data never leaves the country during failover. Your audit trail does not get a gap.
When the underlying issue is resolved, traffic returns to local automatically. You get the incident report at the next governance review, not as a 2am phone call.
Local inference
Routing & observability
AmmaCloud · sovereign AU
Built for Australian regulatory reality.
The same four-layer governance model that underpins AmmaCloud applies on-prem: inbound traffic controls, role-based AI governance, controlled inference, and explicit residency rules. Designed so you can answer audit questions with evidence, not adjectives.
TLS 1.3 + identity
Every request authenticated against your existing identity provider. No shared keys floating in spreadsheets.
Per-workload policy
Role-based access, prompt and tool whitelisting, exportable audit trail covering every inference call.
No training, no retention
Customer data is never used to train models. Inference logs retained only on terms you define.
AU-sovereign by default
Storage, backups, and failover all inside Australia. Privacy Act 1988, APRA CPS 234, ISO 27001 alignment.
Four steps, no surprises.
The same engagement pattern that runs across the Ammacatize practice. Discovery first. Build at our risk. Decision point before commitment. Then we run it.
Understand the workload
Use case, throughput, latency budget, regulatory frame, existing infrastructure. No pitch — we work out whether local is actually the right answer.
Specify the stack
Hardware spec, model selection, hosting choice, integration plan, security model, total cost of ownership against your existing baseline.
Build it
Hardware sourced, racked, and commissioned. Models deployed, evaluated, and tuned. Failover proven before anything goes near production traffic.
Run it
Twenty-four-seven managed service. Quarterly governance reviews. Month-to-month terms. You can leave whenever the work stops earning its keep.
The boring detail behind the brochure.
If you are the person who has to actually own this in production, here is the substance under the marketing. Happy to go deeper in a working session — none of this is hidden behind a sales process.
Hardware reference designs
Three baseline configurations, each scoped to a workload profile rather than a vendor sheet:
- Edge node — single GPU (L4 / L40S class), ≤7B parameter models, branch deployments, embedded use cases.
- Production node — 2–4× H100 / H200 or MI300X, 70B+ models, RAG with sub-200ms p95, mid-market workloads.
- High-throughput cluster — multi-node, NVLink / RoCE fabric, agentic workloads or 24/7 voice at population scale.
Inference runtime
We default to vLLM for throughput-oriented workloads and TGI or Triton where multi-model serving or specific quantisation paths matter. Decisions are workload-driven, not preference-driven.
- AWQ, GPTQ, FP8 quantisation evaluated per model
- Continuous batching, paged attention, speculative decoding where it pays
- OpenAI-compatible API surface so applications stay portable
Model selection
Open-weight foundation models, refreshed quarterly. Current defaults: Llama 3.x, Qwen 2.5/3, Mistral, Gemma. Closed-weight options available via AmmaCloud where the workload justifies it.
- Eval harness against your representative workload before promotion
- Fine-tuning and LoRA adapters where the data and ROI support it
- Guardrails and content filters tuned per use case
Orchestration & observability
Application layer designed for production realities — retries, fallbacks, structured logging, traceable tool calls — not notebook-grade demos.
- RAG pipelines with vector store of your choosing (
pgvector, Qdrant, Weaviate) - OpenTelemetry traces across the full request lifecycle
- Prometheus / Grafana for metrics; alerting routed to your existing on-call
- Identity via SAML / OIDC against your IdP — no parallel user directory
Local AI is not for everyone. That's fine.
Most businesses are well-served by cloud inference and an honest managed service on top of it — which is what ammacatize.ai is built for. Local infrastructure becomes the right answer when one of these is true:
Regulated industries
Financial services under APRA CPS 234, health and aged care, legal, government, defence supply chain — anywhere data residency is contractual rather than aspirational.
High-volume or low-latency
Voice and real-time copilots, call-centre automation at scale, control-loop applications, anywhere token economics or round-trip latency stop adding up.
Distributed operations
Mining, agriculture, manufacturing, logistics — operations where connectivity to the public cloud is intermittent, expensive, or just genuinely not the right shape for the work.
The questions buyers actually ask.
How long does a typical deployment take?
From signed scope to production traffic, eight to twelve weeks for a single production node. Hardware lead time is usually the longest pole — we work that early. Edge deployments can be faster; high-throughput clusters take longer because of network and power preparation on your side.
Do we have to commit to a long-term contract?
No. The managed service runs month-to-month, same as the rest of the Ammacatize practice. You own the hardware (or we host it for you on equivalently flexible terms). If the work stops earning its keep, you can leave. That commercial model is deliberate — it keeps us focused on outcomes, not on holding contracts.
What happens if a hardware component fails?
Traffic fails over to AmmaCloud automatically inside the SLA. We dispatch replacement hardware against the vendor support contract we hold on your behalf, swap it in, validate, and rejoin the local node to the gateway. You see one incident report at the governance review, not a fire drill.
Can we run closed-weight models like Claude or GPT locally?
No — those are not licensed for self-hosting. What we do support is a hybrid pattern: open-weight models locally for the bulk of inference, with routed calls to AmmaCloud (which hosts approved closed-weight options under Australian-resident commercial arrangements) for the smaller share of work that genuinely needs them. Governance is uniform across both paths.
How do you price it?
Three lines: hardware (capex or our hosting opex), one-time deployment and integration, and a monthly managed-service fee tied to scope and SLA. No per-token billing on the local stack. AmmaCloud failover is metered transparently and capped by agreement. Indicative pricing in the first meeting; firm pricing after discovery.
How does this interact with our existing cloud / Microsoft / AWS estate?
It sits alongside it. Identity, networking, and observability integrate with what you already run. We treat your existing platforms as constraints to design around, not as things to replace. If a workload is better served by Azure OpenAI or Bedrock, we will say so.
Are you vendor-locked to a particular GPU or model provider?
No. We are an accredited reseller across Dell, Lenovo, Cisco, Supermicro, and others, and our model selection is open-weight by default. We have no margin reason to push one chip or one model — and if your workload runs better on AMD MI300X than on NVIDIA H100, that is the recommendation you will get.
Book a consultation.
Thirty minutes. No pitch. We listen to the workload, the constraints, and the regulatory frame, then tell you whether local AI is actually the right answer — and what it would take to get there.