Your organisation's knowledge is not in the model. It is in your systems.
Generic RAG stacks return weak results on Arabic corpora because their embeddings and retrieval were tuned for English. A buyer asking a question of their own documents in Arabic gets an English-shaped answer, or no answer at all.
Seamless Enterprise connects to your document stores, databases and business systems and answers from your organization's own knowledge, with the Kawn-Embed family handling Arabic retrieval and Baseer handling Arabic document parsing.
Why Arabic retrieval is the whole problem
Embeddings built for the language
The Kawn-Embed family converts Arabic text into vectors tuned for semantic search and RAG, with domain variants rather than one general-purpose model asked to cover everything.
Arabic documents are a first-class source
Baseer parsing is built in, so a scanned Arabic contract is retrievable content rather than an image sitting outside the index.
Inside your compliance boundary
Seamless Enterprise runs on-premises or on dedicated servers inside Saudi Arabia, architected around SDAIA and NCA ECC requirements rather than certified against them afterwards.
What it connects, and what it answers from
Your own document stores, databases and business systems
The platform answers from the organisation's own knowledge, not just from what a general model happens to have read.
Domain-tuned Arabic embeddings
kawn-embed-light for fast production retrieval across standard business content, with religious and medical variants for corpora where general vocabulary is not enough.
Chat and web search, kept secure
Teams use AI models and pull in real-time information without the data exposure that comes with public tools.
Pipelines you assemble rather than build
Seamless API is a node-based flow builder with Arabic document parsing as a node and pre-configured Arabic RAG setups, so LLMs, embedding models and chunking strategies can be swapped and compared directly.
The numbers behind it
- 0.25Word Error Rate on Misraj-DocOCR, the expert-verified Arabic OCR benchmark
- 300Mkawn-embed-light: production retrieval without the compute overhead
What changes
One place to ask
The question goes to the organisation's material instead of to whoever last touched the file.
Arabic content stops being a dead zone
Retrieval quality holds on Arabic corpora, which is where generic RAG stacks quietly degrade and get blamed on the data.
Answers you can trace
Responses are grounded in retrieved source material, so a reviewer can check where an answer came from.
How it deploys
On-premises
The full platform inside your own environment. Nothing leaves unless you decide it should.
Dedicated servers in-Kingdom
Hosted inside Saudi Arabia for organisations that need residency without running the hardware themselves.
Cloud API
Individual models through Kawn Console, for teams building their own retrieval stack.
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.