AI model deployment integration
Deploy models behind production APIs — REST, GraphQL or gRPC — with autoscaling, versioning and A/B routing so a new model can be promoted without a release.
REST / GraphQL · Model versioning · Autoscaling
AI Integration · Saudi Arabia
Enterprise AI integration for Saudi organizations
Most AI programmes stall at the last mile. The model works; it just cannot reach the systems where decisions are actually made. Crux builds the layer that connects them.
At a glance
Prebuilt connectors
50+
Average delivery
6 weeks
SAP / Oracle projects
6–10 weeks
Uptime SLA
99.9%
Data residency
AWS Saudi / Azure KSA
Integration work is scoped against your actual system inventory, not a standard package.
50+
Enterprise connectors
Prebuilt connectors for SAP, Oracle, Salesforce, Microsoft and 40 more enterprise platforms.
6 weeks
Average delivery
From scoping to a live integration running in production, delivered by a Saudi-based team.
PDPL
Compliant by design
Data classification, masking and audit logging specified before the first connector is built.
Zero
Downtime deployment
Live system integration with no operational downtime during deployment or go-live.
The problem
Why validated AI models never reach production
Organizations invest heavily in building and validating AI models, then discover the hard part was never the model. It is the distance between a model that scores well in a notebook and a model that returns an answer inside SAP while a buyer is mid-transaction.
That distance is made of unglamorous engineering: authentication against systems written before OAuth existed, schema translation between platforms that disagree about what a customer record is, retry logic for the third-party service that goes down every Tuesday, and an audit trail that satisfies a PDPL review.
This is the work Crux does. Every integration ships with circuit breakers, retry and backoff, full request tracing, and data handling classified against PDPL from the first design session — not bolted on before go-live.
What typically blocks integration
Legacy platforms with no API surface. Data that only moves overnight in batch files. Vendor contracts that prohibit direct database access. Security teams who have never approved an outbound model call. Each has an established pattern — the assessment identifies which applies to which system before any commitment is made.
Scope of integration
What we connect, and to what
Both sides of the integration are inventoried during assessment. Nothing is assumed from a template.
Your existing systems
SAP ERP
Finance, procurement, supply chain
Oracle Database
Transactional and reporting stores
Salesforce
CRM, service and sales cloud
Cloud infrastructure
AWS Saudi, Azure KSA, Google Cloud
Legacy platforms
Custom APIs, file drops, direct DB
AI capabilities
AI models
Inference endpoints, versioned and scaled
Data pipelines
ETL, streaming, lineage tracking
Automation engines
Workflow and decision routing
Analytics layer
BI, reporting, monitoring
LLM and NLP services
Document, language and chat systems
Capabilities
Six things we build into every integration
Which of these apply, and in what depth, comes out of the assessment rather than the sales conversation.
API and microservices architecture
Design the layer that exposes AI capability as composable services any enterprise application can consume, with a gateway that enforces auth, quotas and schema.
Microservices · API gateway · Event-drivenData platform integration
Connect models to lakes, warehouses and real-time streams, with lineage tracking so any prediction can be traced back to the records that produced it.
Data lake · Streaming · PDPL maskingEnterprise system connectivity
Native connectors for SAP, Oracle ERP, Salesforce, Microsoft 365 and ServiceNow, plus custom middleware where a legacy platform offers no API at all.
SAP / Oracle · Salesforce · LegacyAI platform orchestration
Coordinate multi-model workflows so the right model runs at the right stage, including human-in-the-loop approval gates where a decision needs sign-off.
MLflow · Airflow · KubeflowOperational adoption monitoring
Track usage, latency, drift and business impact across integrated systems, so it is visible whether the AI is actually being used and still performing.
Usage analytics · Drift detection · SLA monitoring
Architecture
Four layers, one AI estate
The integration architecture separates into four layers, each with clear ownership, its own monitoring and its own compliance controls. The separation is what makes a model or platform replaceable later without rewiring the enterprise.
-
Presentation and API layer
AI capability exposed through secured, versioned APIs — REST, GraphQL and event streams — consumed by enterprise applications.
-
AI orchestration layer
Workflow orchestration coordinating multi-model inference, decision routing and human-in-the-loop approval gates.
-
Data integration layer
Unified data access connecting models to enterprise sources, with lineage tracking and PDPL data classification.
-
Enterprise system layer
Native connectors into ERP, CRM, databases and cloud infrastructure — bidirectional, event-driven and fault-tolerant.
Prebuilt connectors
The four-layer reference architecture applied to a typical Saudi enterprise estate.
SAP ERP
Oracle DB
Salesforce
MS 365
ServiceNow
AWS Saudi
Azure KSA
Google Cloud
Databricks
Snowflake
Kafka
PostgreSQL
MongoDB
Redis
Elasticsearch
Custom API
Outcomes
What integration changes in practice
Impact figures are typical ranges from delivered engagements, not guarantees.
| Outcome | How integration delivers it | Typical impact |
|---|---|---|
| Faster AI deployment | Prebuilt connectors and reusable patterns remove most of the bespoke plumbing | ~3x faster |
| Independent scaling | Microservices let AI scale without touching the core transactional systems | Scales separately |
| One data architecture | A single integration layer gives every model consistent, governed data access | One source of truth |
| Real adoption | AI embedded in existing workflows means staff use it without changing process | Low friction |
| PDPL compliance | Centralised governance enforces policy across every AI data flow | Audit-ready |
| Less vendor lock-in | An abstraction layer decouples AI from any single platform or model vendor | Swappable |
Engagement snapshot
Procurement AI, live inside SAP
A petrochemicals manufacturer had validated demand-forecasting and supplier-risk models that no one could reach: both sat outside SAP, where every procurement decision was actually made. Crux built the API and orchestration layers, connected them to SAP MM through a governed integration layer, and put scoring in front of buyers inside the transaction screen they already used. No change to the procurement process, and no downtime during cutover.
7 weeks
Scoping to production
2
Models integrated
0
Hours downtime
SAP MM
Target module
Questions
AI integration, answered
What technology and procurement teams ask before an integration engagement starts.
What does enterprise AI integration involve in Saudi Arabia?
Enterprise AI integration connects AI models and intelligent systems to your existing business platforms — SAP, Oracle, Salesforce and custom applications. Crux designs the API layers, event-driven architecture and data pipelines that make AI capability available across the enterprise, compliant with SDAIA and PDPL.
How long does AI integration take with SAP or Oracle?
Standard SAP or Oracle AI integration typically takes 6 to 10 weeks depending on system complexity and how many models are being integrated. Prebuilt connectors for major enterprise platforms accelerate delivery and reduce risk.
Can Crux integrate AI with legacy systems that have no API?
Yes. Several patterns apply depending on the platform: RPA for UI-only systems, database-level integration where no service layer exists, event streaming via Apache Kafka, and file-based integration for older platforms. Each is wrapped with monitoring and PDPL-compliant data handling.
How is integration data secured under PDPL?
Data flowing through the integration layer is classified, masked where required, and governed by automated controls — consent tracking, right-to-erasure pipelines and full audit logging. Compliance is built into the architecture rather than added before go-live.
What happens if an AI model needs to be replaced later?
The abstraction layer is the reason this stays cheap. Models sit behind versioned APIs, so a replacement is a routing change rather than a re-integration. The same applies to swapping a platform vendor.
Do you work with models we built ourselves?
Yes. Most engagements integrate models the client or another partner has already built and validated. Crux is not required to have authored the model to deploy and operate it.
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Start here
Get your models out of the notebook
An integration assessment inventories your systems, identifies which connection pattern each one needs, and returns a sequenced plan with effort estimates — before any build commitment.