AI that runs inside your building.
Who this is for
Government bodies, banks and enterprises holding data that cannot leave the premises, and organisations wanting AI applied to a specific workflow rather than announced in a press release.
The problem
Nearly every AI offering on the market requires sending your data to a provider in another jurisdiction. For a ministry handling correspondence, a bank holding customer records or a court holding case files, that ends the conversation before it starts. We deploy models on your own hardware, inside your own network, including air-gapped environments where nothing leaves at all.
What we do
- On-premises and air-gapped LLM deployment: model selection, hardware sizing, inference infrastructure
- Private AI for environments where data cannot leave the premises
- AI strategy and use-case assessment: where it produces measurable value and where it does not
- AI agents and workflow automation acting inside existing systems
- Document intelligence and retrieval over institutional archives
- Arabic language capability: speech to text, text to speech, Arabic-first model selection
- Intelligence embedded in our own platforms: classification in DMS, routing in GTMS, anomaly detection in Fedentra
- Model evaluation and governance: what it was tested against, and what it is not permitted to decide
The platform behind it
Deployable into GTMS, DMS, Nuvetra, LogiFlex and Fedentra, or standalone. Because we own the platforms, intelligence goes inside the system instead of beside it. Classification happens where the documents already live; routing happens where the cases already sit. On someone else's system, integration is assessed first.
How an engagement runs
Use-case assessment first, then a pilot on one workflow with measured results, then production deployment and handover. Hardware requirements are specified during the assessment, before any commitment.
What you get
A running deployment on your own infrastructure, the model and pipeline documented, your team trained to operate it, and a written evaluation of what it does and does not do reliably.
Sovereignty is the point of this service. Where a client's data can lawfully leave the premises and running cost matters more than residency, we will say so and deploy against a hosted model instead.
