Connect Your AI Models to Your Business
A model in isolation creates no value. We integrate AI into your existing workflows — real-time scoring, batch processing, and agentic orchestration.
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AI integration connects your AI models to the business processes where they create value. A model that runs in isolation — in a notebook, in a staging environment, in a data science team’s queue — produces no business outcome until it is integrated into the workflow that depends on its predictions.
Integration Patterns
We implement four integration patterns based on your latency, volume, and architecture requirements:
Synchronous REST API: The target system calls the inference API and waits for a response before continuing. Used for real-time decisions: credit scoring at loan application, fraud detection at payment authorisation, property valuation at listing creation. Latency must be under 500ms for user-facing workflows.
Asynchronous Messaging: The target system publishes an event; the inference API consumes it and publishes a result. Used for workflows that can tolerate seconds of delay: document classification, lead scoring, risk assessment. Built on Kafka, SQS, or Azure Service Bus.
Batch Scoring: Scheduled jobs process a dataset and write predictions to a database or data warehouse. Used for overnight risk calculations, daily demand forecasts, weekly churn propensity scores. No latency requirement — optimised for throughput.
Agentic Orchestration: Multiple AI models are orchestrated in a pipeline where the output of one model informs the input of the next. Used for complex decisions: a document classifier routes to a specialist model; an anomaly detector triggers an explanatory model. We build orchestration layers using LangChain, LlamaIndex, or custom orchestration code depending on complexity.
Engagement Phases
Integration Architecture Design
Map target business process requiring AI decisions. Design integration pattern: synchronous API, asynchronous messaging, batch job, or event-driven. Define latency requirements, fallback logic, and data flow.
API Development & Connection
Build inference API wrapper around existing model or connect to third-party AI service. Implement authentication, rate limiting, input validation, and response transformation. Integrate with target system (ERP, CRM, core banking, mobile app).
Testing & Go-Live
End-to-end integration testing, load testing at production request volume, failover testing. Phased rollout with feature flag control. Monitoring and alerting for integration layer.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| Time to Decision | Manual review queue: 4-6 hours from application to credit decision | Real-time AI scoring: <500ms — instant decision at point of application |
| Integration Stability | Notebook model: zero SLA, manual restart required on failure | Production API: 99.9% uptime SLA with automated failover |
| Prediction Volume | Batch job: 10,000 predictions per night | Real-time API: 500 requests/second with horizontal auto-scaling |
Tools We Use
Frequently Asked Questions
What systems can you integrate AI models with?
We have integration experience with UAE-prevalent systems including core banking platforms (Temenos T24, Finastra Fusion), ERP systems (SAP, Oracle Fusion), CRM platforms (Salesforce, HubSpot), property portals (Dubizzle/Property Finder APIs), healthcare systems (NABIDH, Salama), retail platforms (Shopify, Magento, SAP Commerce), and custom-built applications via REST API. For legacy systems without APIs, we build adapter layers using database change data capture (CDC) or file-based batch integration.
How do you handle AI model fallback when the model is unavailable?
Every production integration includes a defined fallback strategy: rule-based default, previous model version, or human review queue. Fallback logic is configured per use case based on the risk of an incorrect default versus the cost of manual review. For high-stakes decisions (credit approval, clinical triage), we default to human review on model unavailability. For lower-stakes decisions (product recommendation, demand forecast), we use a statistical baseline or cached prediction.
Can you integrate models with on-premises systems that cannot connect to the cloud?
Yes. For clients with air-gapped or on-premises-only requirements (common in UAE government entities and regulated financial institutions), we deploy the inference API within the same network as the target system. The model runs on-premises on GPU or CPU infrastructure managed by the client. We provide the deployment configuration, Helm charts, and operations runbook for on-premises management.
What latency is achievable for real-time AI scoring?
For CPU-optimised models (ONNX-exported scikit-learn, XGBoost, LightGBM): P99 latency under 20ms. For GPU-accelerated deep learning models: P99 under 100ms. For LLM inference: P99 100-500ms depending on output length and model size. All latency targets are measured at the inference API boundary, not including network round-trip to your client system. We load-test every integration to validate latency meets your requirement before go-live.
Build It. Run It. Own It.
Book a free 30-minute AI discovery call with our Vertical AI experts in Dubai, UAE. We scope your first model, estimate data requirements, and show you the fastest path to production.
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