Private AI deployment
AI systems that run on your infrastructure
We build and deploy production LLM systems on your own hardware or private cloud, using open-weight models.
Organizations that cannot send their data to a public AI API
If counsel has already said no to public AI tools, if a regulator has asked where your data goes, or if your most sensitive documents are the ones you would least want indexed by a third party, this is built for that constraint, not around it.
Banks and financial firms
Credit files, AML case notes, and client portfolios sit under regulatory data-residency rules that most public AI vendors cannot meet.
Construction and industrial groups
Bid pricing, claims files, and subcontractor terms are the commercial crown jewels of a construction business, and the last thing that should leave the building.
Other data-sensitive businesses
Any organization operating under contractual confidentiality, sovereign data rules, or a network with no route to the public internet.
A system, not a chatbot wrapper
Model serving on your hardware or VPC
Open-weight models deployed inside your data center or a private cloud tenancy you control, with no default path to a third-party API.
Retrieval over your internal documents
Contracts, reports, policies, and drawings indexed and searched inside your environment, so answers are grounded in your own material.
Integration into existing tools and ERP
The system reads and writes where your team already works, instead of adding another tab nobody opens after week two.
Role-based access control
Every query respects the same permission boundaries your document management system already enforces.
An evaluation suite
A test set built from your own documents and questions, so quality is measured against your domain, not a generic benchmark.
Monitoring and a handover path
Usage, cost, and quality dashboards from day one, and a documented path for your own team to run and extend the system.
Delivered by people who have built this before
The same team that runs the assessment builds the pilot and hardens it for production.
The honest case for running this yourself
Data sovereignty and residency
Data stays inside the network and jurisdiction you choose, at rest and in processing.
Built to align with GDPR and the EU AI Act
Architecture decisions are made with your compliance obligations in view from the start, not retrofitted after a review.
Predictable cost at volume
Per-token API pricing scales against you as usage grows. Infrastructure you run has a cost curve you can plan around.
No vendor lock-in
Open-weight models can be swapped, fine-tuned, or run alongside each other, without rebuilding the system around a single provider.
Works in restricted networks
For air-gapped or heavily firewalled environments, this is the only option that functions at all.
This is not the right tool for every workload. For non-sensitive, low-stakes tasks, a public API is faster to start with and cheaper at low volume. We will tell you when that is the better call.
How delivery works
From assessment to a system your team can run
We review your document set, hardware or cloud options, and access constraints to scope what is realistic.
A working system tested against your own contracts, reports, or drawings, not a demo dataset.
Access control, monitoring, and the evaluation suite are built out so the system holds up under real usage.
Your team takes it over with full documentation, or we continue running it under a support agreement.
Frequently asked questions
What hardware do we need?
It depends on model size and query volume. We size the deployment during the assessment phase against your existing infrastructure or a private cloud budget, rather than asking you to over-provision up front.
Can it run fully offline?
Yes. The entire stack, model serving, retrieval, and the application layer, can run in an air-gapped environment with no outbound internet connection.
Which models do you use?
Open-weight models from providers such as Meta, Mistral, and Qwen, selected per project based on the language, domain, and hardware you have available. We reassess as new open models are released.
How does quality compare to GPT-class APIs?
On narrow, well-scoped tasks over your own documents, a properly tuned open model with good retrieval performs close to frontier APIs. On open-ended general reasoning, frontier APIs still lead. The evaluation suite tells you which case you are in before you commit to production.
Who maintains it after launch?
Either your own team, after a documented handover, or Rexto under an ongoing support agreement. Most clients start with a managed run and shift maintenance in-house once their team is comfortable with the system.
What does a pilot cost and how long does it take?
A pilot typically runs 4–8 weeks depending on document volume and infrastructure readiness. Cost depends on scope and hardware; we give a fixed quote after the assessment phase rather than a headline number that will not match your situation.
Talk to us about a private deployment
A 30-minute call to review your data, infrastructure, and constraints, and whether a private deployment is the right fit.