60%
Faster Time-to-Production
using pre-built AI accelerator frameworks vs. building ML infrastructure from scratch on each project
3×
More Reliable Deployments
standardised pipelines with built-in testing, monitoring, and rollback reduce production failures significantly
40%
Engineering Cost Reduction
reusable components eliminate duplicated infrastructure work across AI projects and business units
What We Build

Stop rebuilding the same
ML infrastructure every time.

Every AI project that builds its own pipelines, feature stores, and deployment infrastructure from scratch is wasting engineering capacity that should go into building AI that creates business value. Crux AI Engineering Accelerators change this — providing a foundation of battle-tested, production-ready components.

Whether you are scaling ML models or deploying intelligent systems into production for the first time, these accelerators help you move faster, reduce risk, and deliver AI outcomes that compound over time — purpose-built for Saudi Arabia's enterprise context and regulatory requirements.

crux — ai-accelerator — deploy
~ crux-ai accelerator --list available
✓ [CORE] ML Pipeline Framework — ready
✓ [CORE] Feature Store Template — ready
✓ [CORE] Model Serving API — ready
~ crux-ai deploy --accelerator nlp-arabic
✓ Arabic NLP pipeline: installed · configured
✓ Models: arabert · camelbert · custom fine-tuning
~ crux-ai deploy --accelerator fraud-detection
█Fraud engine: LIVE · 2.4M transactions/day scoring
Accelerator Library

What's in the library.
Pre-built for Saudi enterprise.

Pre-Built ML Pipeline Frameworks

Production-ready machine learning pipeline templates that eliminate the most time-consuming infrastructure work — letting your team focus on the models and use cases that matter.

  • End-to-end pipeline templates — Ingestion → feature engineering → training → evaluation → deployment pipelines, pre-wired and tested.
  • Experiment tracking — MLflow-based experiment management that gives full reproducibility and model versioning out of the box.
  • Automated retraining — Drift-triggered retraining pipelines that keep models performing without manual intervention.
  • Data validation gates — Great Expectations-based quality checks that prevent bad data from reaching model training.
01
Pipeline Accelerators
Classification Pipeline
PyTorch · Scikit · AutoML — ready
NLP Pipeline (Arabic)
AraBERT · CAMeL · fine-tuning — ready
Time Series Forecasting
Prophet · N-BEATS · TFT — ready
Anomaly Detection
Isolation Forest · VAE · LSTM — ready

Model Serving & Inference Accelerators

Pre-built, production-grade model serving infrastructure that delivers sub-100ms inference with auto-scaling, load balancing, and zero-downtime model updates.

  • REST inference APIs — FastAPI-based serving templates with input validation, versioning, and automatic OpenAPI documentation.
  • Real-time feature serving — Online feature store integration for sub-10ms feature retrieval at inference time.
  • Model A/B testing — Traffic splitting and shadow mode deployment for safe production model rollouts.
  • GPU inference optimisation — TensorRT and ONNX export pipelines that maximise throughput on GPU-accelerated infrastructure.
02
Serving Performance
Avg Inference Latency
P50: 12ms · P99: 47ms
Throughput
12,000 req/sec per replica
Model Update Downtime
0ms — rolling deployment
Auto-scaling
CPU/GPU threshold · elastic

Arabic NLP & Language AI Accelerators

Purpose-built NLP accelerators for Arabic language AI — including pre-trained model fine-tuning, document intelligence, and conversational AI frameworks configured for Saudi enterprise use cases.

  • Arabic document intelligence — Pre-built OCR, entity extraction, and classification pipelines for Arabic government and banking documents.
  • Bilingual chatbot framework — Arabic/English conversational AI foundation with intent classification and dialogue management.
  • Sentiment & opinion mining — Arabic social media and customer feedback analysis models pre-trained on Saudi-context corpora.
  • Custom model fine-tuning — Fine-tuning pipelines for AraBERT and CAMeL on your domain-specific Arabic data.
03
Arabic NLP Accelerators
Document Classification
98 Arabic document types · pre-trained
Named Entity Recognition
Arabic NER · 94.2% F1 score
Bilingual Chatbot Base
Arabic/EN · intent + entity + dialogue
Sentiment Analysis
KSA context · 91.8% accuracy

MLOps & Model Governance Accelerators

Pre-built MLOps toolchains that give your AI team full lifecycle management — from model registry to monitoring, governance, and SDAIA compliance — without building it all from scratch.

  • Model registry & versioning — MLflow-based registry with stage transitions (staging → canary → production) and full audit trails.
  • Performance monitoring — Real-time model performance dashboards with drift alerts and automated retraining triggers.
  • AI governance templates — SDAIA ethics framework and PDPL compliance templates built into the model lifecycle from day one.
  • Cost & resource tracking — Cloud resource attribution for AI workloads to support FinOps governance across model portfolios.
04
MLOps Dashboard
Models in Production
34 active models monitored
Drift Alerts
Automated · 0 undetected regressions
SDAIA Compliance
Ethics checklist · 100% coverage
Retraining Trigger
Auto-triggered on performance drop
How We Deliver

From toolkit to production AI faster.

A structured acceleration programme that audits your current AI capability gaps, selects the right accelerators, and deploys them into your engineering workflows.

01
AI Capability Audit
Assess current AI engineering maturity, identify the bottlenecks costing the most time and money, and select the right accelerators.
Maturity AssessmentGap AnalysisAccelerator SelectionRoadmap
02
Environment Setup
Configure your cloud environment, CI/CD pipelines, and MLOps toolchain to work with the selected accelerator frameworks.
Cloud SetupCI/CD IntegrationMLflow ConfigFeature Store
03
Accelerator Integration
Integrate selected accelerators into your engineering workflow — with training for your team and documentation for long-term maintainability.
Pipeline IntegrationTeam TrainingDocumentationCode Review
04
First Model Deployment
Deploy your first AI use case using the accelerator framework — with monitoring, governance, and rollback all in place from launch.
Model TrainingServing SetupMonitoringGovernance Gates
05
Scale & Optimise
Expand accelerator usage across additional AI use cases — compounding the time savings with each new model or pipeline deployed.
Use Case ExpansionPerformance TuningCost OptimisationCapability Building
Client Outcome
"Crux's AI accelerators reduced our ML infrastructure build time by 60%. Our team was deploying production models in weeks instead of months — and the built-in SDAIA governance framework meant we could demonstrate responsible AI from the very first deployment. We shipped 8 production models in the first 6 months instead of the 2 we'd originally planned."
Ho
Head of AI Engineering
Saudi Government Digital Authority · Riyadh
8 models
Deployed in 6 months — target was 2
-60%
ML infrastructure build time per use case
100%
SDAIA governance compliance from day one
3×
More reliable production deployments
Technology Stack — Best-in-class tools · No vendor lock-in
Python PyTorch TensorFlow MLflow Feast FastAPI Triton ONNX AraBERT CAMeL Kubernetes Ray Airflow Great Expectations Weights & Biases dbt Kafka SageMaker Vertex AI SDAIA Compliant Python PyTorch TensorFlow MLflow Feast FastAPI Triton ONNX AraBERT CAMeL Kubernetes Ray Airflow Great Expectations Weights & Biases dbt Kafka SageMaker Vertex AI SDAIA Compliant

Extend your AI engineering capability.

SVC / 01
AI Strategy & Intelligent Systems
Define which AI use cases to prioritise before deploying your accelerator library across the enterprise.
Explore service
SVC / 05
Data Engineering & Analytics
Build the data pipelines and feature infrastructure that feed your accelerated AI models with high-quality inputs.
Explore service
SVC / 02
Intelligent Application Engineering
Embed the AI models built with accelerators directly into the enterprise applications that drive business value.
Explore service
// ready to accelerate

Ship AI faster with
battle-tested accelerators.

Pre-built. Production-grade. SDAIA-compliant. Purpose-built for Saudi Arabia's enterprise AI teams who want to spend time on use cases, not infrastructure.

Explore Accelerator Library Schedule a Technical Demo