How Does InferTrust™ Integrate with Existing AI Systems?

InferTrust™ integrates through a lightweight SDK that wraps your existing inference calls. No changes to your model, training pipeline, or inference logic. The

Integration Without Disruption

InferTrust™ is designed to integrate into existing AI pipelines without requiring changes to your model code, training pipeline, data processing logic, or inference infrastructure. The integration point is a lightweight SDK that wraps your model's inference call. Your application calls the InferTrust™ SDK instead of calling your model directly. The SDK calls your model, captures the output at the inference boundary, constructs and signs the decision record, and returns the result to your application. From your application's perspective, the behavior is identical except that every inference now produces a verifiable receipt.

Three Steps to Integration

A typical InferTrust™ integration involves three steps. First, install the SDK in your application environment (cloud, on-premises, edge, or hybrid). Second, configure your policy rules including confidence thresholds for autonomous versus escalated decisions, allowed decision actions, and escalation criteria. Third, wrap your inference calls with the InferTrust™ client. The SDK handles record construction, cryptographic signing, and local storage automatically.

Framework and Model Agnostic

The InferTrust™ SDK does not need to understand your model's internals. It operates on the model's output, capturing the result and associated metadata at the moment inference completes. This means it works with any inference framework: TensorFlow, PyTorch, ONNX Runtime, TensorRT, custom inference engines, and third-party model APIs including OpenAI, Anthropic, Google, and others. If your application can call a model and receive a result, InferTrust™ can wrap that call.

Deployment Topologies

InferTrust™ supports three primary deployment topologies:

• Cloud deployment: The SDK runs alongside your model in your cloud infrastructure (AWS, Azure, GCP, or private cloud). Decision records are signed in your cloud environment and can be stored locally or transmitted to a central audit repository.
• Edge deployment: The SDK runs on edge devices (medical devices, manufacturing controllers, autonomous vehicles, IoT gateways). Decision records are signed locally using device-bound hardware keys and stored on-device until network connectivity is available for synchronization.
• Hybrid deployment: Some inferences happen in the cloud and some at the edge. InferTrust™ handles both, with the same record format and verification process regardless of where the inference occurred.

Performance Impact

The cryptographic signing operation adds sub-millisecond latency to each inference. For the vast majority of AI applications, this overhead is imperceptible. The SDK is designed to be non-blocking: if the signing operation encounters an issue, the inference result is still returned to your application. The SDK does not introduce a single point of failure into your inference pipeline.

What Changes and What Does Not

What changes: your inference call is wrapped with one additional function call. What does not change: your model code, your training pipeline, your data preprocessing, your feature engineering, your inference logic, your application logic, your deployment infrastructure, and your monitoring and observability stack. InferTrust™ is additive, not invasive.