AI & Data
Production-grade autonomous agents, sovereign intelligence and high-velocity data lakehouses.
Overview
XSIS AI & Data moves enterprises beyond experimental chatbots to deterministic, sovereign intelligence that earns revenue. We deploy air-gapped, on-premises and regional-cloud language models, including Arabic-first and regional dialect fine-tuning, backed by permission-aware GraphRAG and unified semantic data layers. Our engineers build multi-agent workflows that run around the clock governed by the Model Context Protocol (MCP), Agent2Agent (A2A) interoperability and human-on-the-loop supervision.
Beneath the intelligence we build the data foundations that feed it. That means real-time Kafka and Flink event streaming, Snowflake, Databricks and Iceberg lakehouses, large quantitative models for financial and supply-chain simulation, and strict cross-border data residency.
Who it is for
Chief AI officer
Agents and models in production, governed, measured and kept inside your boundary.
Chief data officer
Lakehouses, pipelines and governance that make institutional data ready for AI.
Chief operating officer
Supervised agents that do routine work around the clock, from documents to decisions.
What we do
Multi-agent systems and protocols
Multi-agent workflows with human-on-the-loop supervision, MCP servers and tool use, A2A and AP2 interoperability, and AgentOps.
Sovereign LLMs, SLMs and context platforms
Air-gapped, on-premises and sovereign-cloud model hosting, frontier and open-weight models, Arabic-first fine-tuning and permission-aware GraphRAG.
Large quantitative models and applied ML
Quantitative models for financial and supply-chain simulation, forecasting and pricing, credit risk with explainable AI, and fraud and AML detection.
Multimodal, speech and vision AI
Speech-reasoning voice agents and contact centres, transactional conversational AI, NLP, and physical and edge computer vision.
Intelligent document processing (IDP 2.0)
Extraction from invoices, statements, trade finance, KYC and forms, with validation rules, human review and direct posting to the ERP.
Packaged AI tiers and managed AI services
Ready-to-deploy AI packages for mid-market firms, high-scale sovereign enterprise systems, and managed AI APIs on subscription or usage.
Data architecture, lakehouses and real-time pipelines
ETL and ELT pipelines, real-time streaming with Kafka and Flink, lakehouses on Snowflake, Databricks and Iceberg, and vector and graph stores.
Data governance, sovereignty, privacy and security
Master data, data contracts and lineage, data quality and residency, and data security: encryption, tokenisation, masking and synthetic test data.
Agentic BI and decision intelligence
Conversational BI and AI data analysts, executive dashboards and command centres, and customer-facing analytics.
What we are known for
- Data and analytics
- Snowflake
- Databricks
- Apache Kafka
- Key anchors
- Multi-agent workflows: MCP, A2A and AP2
- Sovereign and air-gapped LLMs and SLMs
- Large quantitative models (LQMs)
- Apache Iceberg, Databricks and Snowflake
- Intelligent document processing (IDP 2.0)
The AI & Data stack
Three layers carry every system we build, each with the standard it follows and what it guarantees.
| Layer | Technical standard | What it guarantees |
|---|---|---|
| Agent interoperability | Model Context Protocol (MCP and WebMCP), Agent2Agent (A2A) and the Agent Payments Protocol (AP2) | Deterministic tool execution and cryptographic auditability |
| Sovereign model hosting | On-premises GPU clusters and air-gapped private clouds, with Jais, Falcon, ALLaM, Mistral, Qwen, DeepSeek and Llama | In-country data residency and no third-party training on your data |
| Modern data foundation | Snowflake, Databricks, Apache Iceberg, Apache Kafka and Flink, and permission-aware GraphRAG | Row-level and role-level access rules enforced before the model sees any data |
Technical standard
- Agent interoperability
- Model Context Protocol (MCP and WebMCP), Agent2Agent (A2A) and the Agent Payments Protocol (AP2)
- Sovereign model hosting
- On-premises GPU clusters and air-gapped private clouds, with Jais, Falcon, ALLaM, Mistral, Qwen, DeepSeek and Llama
- Modern data foundation
- Snowflake, Databricks, Apache Iceberg, Apache Kafka and Flink, and permission-aware GraphRAG
What it guarantees
- Agent interoperability
- Deterministic tool execution and cryptographic auditability
- Sovereign model hosting
- In-country data residency and no third-party training on your data
- Modern data foundation
- Row-level and role-level access rules enforced before the model sees any data
How to start
What happens next
Agreeing a time
Our team emails you to agree a time for the session.
Pre-consulting session
A free 30-minute online pre-consulting session: we go through your goals, systems and constraints.
Readiness review
In a free 48-hour readiness review, a Principal Architect checks your project against the 6-point Definition of Ready and recommends the track that fits.
Choosing the track
Scope-ready projects receive a binding proposal within 3–5 business days. Concept-stage projects start in the Blueprint Studio. A live system that is failing, undocumented or needs integration starts with a diagnostic of 1–2 weeks. The Blueprint Studio and the diagnostic each have a fixed fee, quoted in the proposal.
AI-native product teams
Managed, cross-functional pods of senior engineers, equipped with agentic coding and QA harnesses.
Fixed-outcome projects and round-the-clock managed services
Fixed-scope, milestone-driven delivery with named delivery leads, and round-the-clock application managed services under SLA governance.
The quickest way in is the Sovereign agentic POC (MCP, A2A, GraphRAG) package, one of our rapid engagement packages.
Questions and answers
How is this different from adding a chatbot?
A chatbot answers questions. We build systems that do work. Agents call your tools under supervision, models answer only from what the user may see, and data platforms keep them supplied. Each one is tested, logged and measured.
Where does our data go?
Where you decide. We deploy models air-gapped, on your premises or in a regional sovereign cloud, so regulated data stays in the country. Data never leaves the enterprise unless you choose a service that allows it.
How do you keep AI agents under control?
Agents act through narrow, logged tools with the permissions you grant. People supervise them on the loop and approve the steps that matter. AgentOps adds telemetry, evaluation, guardrails against invented answers and limits on token spend.
Where should we start?
Usually with the sovereign agentic proof of concept: one workflow, connected to your internal tools in a private network, that shows the value before a larger build.
Does our data need to be in order first?
Not entirely, but we check it first. If documents are unstructured or databases are siloed, we start with a data quality, lineage and AI-readiness review, then build the platform the models need.
Who owns what you build?
You do. The code, prompts, evaluation sets, data pipelines and any fine-tuned model weights we produce for you are yours once each milestone is paid.
Start with a conversation, not a contract.
Tell us what you need to build, connect or secure. An architect goes through it with you, then recommends where to start.