Runs in your own cloud account or on your own hardware.
Private AI infrastructure
The models, the gateway and the tools run in your own cloud account or on your own hardware. Any call to an outside model is your decision, set up explicitly and logged.
- Client cloud account or on-premises servers
- External services only by approval
- Usage and cost reported by team
- Open-weight models served on your GPUs, with the serving engine chosen for your hardware and workload.
- One gateway for routing, keys, budgets and logs; hosted frontier models only where you approve them.
- Sign-in through your existing identity provider, authorisation on every request, private networking and allow-listed outbound traffic.
- Capacity sized from your workload: model memory, cache, concurrent users and response-time targets.
- Tracing, usage and cost reported by team and task.
- Pinned, verified software dependencies.
Components are selected for each build after evaluation; the ones named here are typical options, not a fixed stack. Engineering notes
What this turns on
Hosted frontier models keep getting more capable, and most of them are reached over the internet. For confidential information, regulated records and commercially sensitive work, that is the difficulty: the data goes to a service somebody else operates, under terms that can change. Cost is the second difficulty. Metered inference is affordable in a pilot and grows with every call at production volume. The third development is the one that changes the answer. Open-weight models are now capable enough for a large share of everyday work, and they are files you can download and run on your own machines. A private environment puts those models inside your boundary, keeps the larger hosted models available for the work that needs them, and leaves the choice of which goes where with you. See what private deployment means and what a model gateway does.
The smallest version is one model, one gateway and one team, running inside your network with the source shown on every answer. It can grow without being rebuilt. Outside models are used only where you approve them, and every such call is logged.
What we actually build
A gateway every request passes through
One place decides which model answers, applies your policy and keeps the record. Changing model later is a setting rather than a project. Model gateways.
Open-weight models, hosted by you
Deployed on your own hardware or on capacity inside your own account. The weights are a file you keep, and upgrades happen when you decide. Open-weight models.
Frontier models, through the same gateway
Reached under the same policy and the same record, for the tasks you approve. Which tasks qualify is written down.
Your identity, your network, your keys
Your existing single sign-on and roles, private networking with no public route in, and encryption in transit and at rest. The environment inherits controls your security team has already approved.
Capacity sized on measured load
Model size, hardware class, concurrency and headroom are measured on your own work and written down with the reasoning, before anything is bought. Tokens, inference and why sizing matters.
Cost and usage in view
Every request is logged with who made it and which model answered, and totalled by team and by task. Where routing by task cost makes sense, it is configured here.
What you receive
A working environment, the connections into it, the models inside it, and the work it is set up to do.
Your private environment
Deployed in your cloud account or on the hardware you choose, under your accounts and your own controls.
Your data, connected
The information the system needs, from documents and databases to ERP and CRM. It works with what you already have.
The models you need
Open-weight models, hosted frontier models, or both. You choose the mix, and you can change it as your needs change.
Ready for your work
Configured for the work you intend to use it for, whether that is knowledge systems, document processing, agents or reporting.
Scoped per environment. The size of the work depends on how many people will use it, where your data is allowed to sit, and how quickly an answer has to come back. All three are measured before anything is bought.
Your system should outlast the model inside it
Models change quickly. One that is expensive today may be matched by a cheaper open-weight model next quarter, and a new hosted model may do better at the tasks you care about.
Your system shouldn't have to start again each time. We keep the model behind the gateway, so it can be replaced after testing on your own examples.
Model A
Your system
Model C
Your system
How we work on every build
Tested on your own work
Tested against examples from your own work before anything goes live.
People approve what matters
A person approves anything that cannot be undone.
Your data stays with you
Your data stays in your environment by default; any outside call is explicit and logged.
Models stay replaceable
Models can be changed later without rebuilding the system.
Why businesses accelerate with Ales Analytics
In a world being reshaped by AI, the advantage goes to organisations that put it to work quickly, safely and on their own terms. That is what we deliver.
Designed around your business
Every system starts from how your organisation works today, so your people keep the processes they know and gain the speed they need.
Works with what you have
We connect to the ERP, CRM, practice and accounting systems already in place. Nothing that works today needs replacing.
Your data stays yours
Everything runs in your own cloud account or on your own servers, under your identity provider and your controls.
Results you can trace
Every figure, answer and completed task traces back to the record it came from, with a full history of who approved what.
Engineers from start to finish
The people you speak to are the engineers who design, build and look after your system, from the first conversation onwards.
Ready for what comes next
Models can be replaced as AI advances, without rebuilding, so your systems keep getting better as the technology does.
Ales Analytics and everything else
| Question | With Ales Analytics | Everything else |
|---|---|---|
| How it fits your work | Designed around how your organisation already works | Your processes adapt to the tool |
| Your existing systems | Connects to everything already in place | Often one more system to manage, or a replacement project |
| Where your data lives | Your own cloud account or your own servers | Usually someone else's platform |
| Who builds it | The engineers you speak to, from start to finish | Handed between teams over time |
| As AI advances | Models upgraded without rebuilding | Tied to one vendor's roadmap |
Related docs
- Technical pagePrivate deployment topologiesOn-premises, private cloud, hybrid and air-gapped
- Technical pageRouting, gateways and multi-tenancyOne door for every model request
- Technical pageSizing a self-hosted deploymentWeights, cache, concurrency and headroom
- Reference deploymentSingle on-premises GPU serverOne server, one workload, data that stays in the building
- Reference deploymentPrivate cloud tenancyGPU capacity in your own cloud account and region
- Reference deploymentHybrid with a gatewaySelf-hosted models, with approved routes to hosted ones
- Reference deploymentAir-gapped deploymentNo outside connection; updates by signed transfer
- Technical pageSecurity controls for private AIZero trust, least privilege and injection defences
- Technical pageChoosing an open-weight modelLicence, shape and fit before accuracy
- Technical pageChoosing a serving enginevLLM, SGLang, TensorRT-LLM and others, by fit
- Industry guideTexas health-record storage and AI disclosureSB 1188 and HB 149 in plain terms
- ExplainerPrivate deploymentRead
- ExplainerModel gatewaysRead
- ExplainerLanguage models and open weightsRead
Let's build yours
Tell us a little about your work. Our engineers will come back with how private ai infrastructure would run for you, and a fixed scope to get it live.