aerosysIntelligence Technologies
العربية

Artificial intelligence

AI that runs where the data lives.

The same AI work that runs inside our aviation platforms, offered as products of their own: private models, the gateway that connects them, an orchestrator for engineering work, topic intelligence, and a secure layer for the records they exchange.

Local AI

Model development and service

In development

Private AI models that run on the client's own hardware. Data stays on site, and each model is fine-tuned on the client's own records.

  • On-premises model serving
  • Fine-tuning with LoRA and QLoRA on real operational data
  • Escalation path: a local model first, then a frontier model, then a person
  • Evaluation on held-out exams before any model is promoted
  • English and Arabic
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CORTEX

AI engineering orchestrator

Active engineering

Plans and runs software work through two loops. It breaks a specification into small atomic tasks, has each plan reviewed independently, then implements, reviews and fixes one task at a time.

  • Plan loop: specification → domains → slices → sealed atomic tasks
  • Execute loop: implement → review → fix, until no high-severity findings remain
  • Works with any capable language model
  • Crash-safe: resumes from its last recorded step
  • Live convergence dashboard and knowledge retrieval (RAG)
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AISAIGW

AI gateway

Active engineering
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P3

Topic intelligence and social data analysis

In development

Collects and analyses open data around the clock, links entities and events with evidence, and shows hypotheses from AI agents whose probability rises and falls as new evidence arrives.

  • Continuous 24x7 collection from configured sources
  • Evidence-backed entity and relationship graphs
  • Hypothesis board with clearly labelled probability classes
  • Configuration-driven: sources, thresholds and AI agents are set through screens
  • Live operational graphs updated every minute
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UCL

Secure canonical messaging layer

In development

A secure message and record format with one canonical encoding. The same input gives byte-identical results in Python, Rust and JavaScript.

  • Canonical CBOR encoding across three runtimes
  • Signed and co-signed entries; each co-signer commits to the exact content it saw
  • Encrypted envelopes and a versioned keystore
  • Cryptographic suite agility, including post-quantum algorithms
  • Mesh admission with delegated credentials