AI that stays inside the compliance boundary
Regulated data, kept inside the compliance boundary
Banks and financial institutions require AI capability - chat, search, document processing - that never leaves their compliance boundary, deployed on-premises or on dedicated in-Kingdom infrastructure.
Why regulated institutions start with deployment
A bank cannot evaluate a capability it is not allowed to switch on. Everything here is designed to run inside your own environment, on-premises or on dedicated servers inside the Kingdom, so the question stops being whether the data may move and becomes what you want the system to do. Arabic is handled natively rather than as a localisation pass, which matters when the documents, the calls and the correspondence are all in it.
Nothing crosses the boundary
Deploy on-premises or on dedicated in-Kingdom infrastructure. The platform is architected around SDAIA and NCA ECC from the start rather than measured against them afterwards.
Arabic that holds up under retrieval
Generic embeddings degrade badly on Arabic. The Kawn-Embed family is trained for it, so search across Arabic policy documents, correspondence and internal knowledge returns results that make sense.
Evidence, not a black box
Every model in the catalog is backed by published research, public benchmarks and datasets we built ourselves, the evidence a risk function asks for before a system goes near regulated data.
What financial institutions deploy
- Private AI
Chat, search and analysis without an exception request.
Seamless Enterprise runs strong open-source models in an environment your team controls, so the capability arrives without the data exposure. For institutions that cannot put anything in a public cloud, the same platform, the same models and the same controls deploy on your side of the boundary.
- Enterprise search and RAG
One place to ask, across Arabic and English internal knowledge.
Retrieval connects to internal document stores, databases and business systems, and answers come from the institution's own knowledge rather than from a general model's memory. Arabic retrieval runs on the Kawn-Embed family and Arabic document parsing on Baseer, which is why it does not fall apart on the Arabic half of the corpus.
Enterprise Search & RAG - Meeting intelligence
Board minutes transcribed and analysed without leaving the building.
Sada is on-premises meeting intelligence, not a cloud transcription service with an Arabic setting. Audio runs through Arabic speech recognition trained for the task, then a multi-agent layer (a decisions agent, an actions agent, a risk agent, a compliance agent) turns the transcript into something a committee can act on rather than a summary to read.
Speech & Voice
Already running in the sector
A regulated financial institution
AI capability was needed that never left the institution's compliance boundary.
Seamless Enterprise was deployed on dedicated infrastructure inside the institution's own environment, architected around SDAIA and NCA ECC.
The full private-AI and enterprise-RAG stack now runs entirely inside the institution's compliance boundary.
Deployment and compliance
On-premises or dedicated in-Kingdom deployment, and an architecture built around SDAIA and NCA ECC from the start. On every Workforces deployment: data masking on sensitive fields, full audit logging and continuous monitoring as standard rather than as an upgrade tier. The Trust Center carries the current detail.
Let's talk about what you're trying to build.
Tell us the problem. We'll tell you honestly whether AI is the right answer.