The Starseer Platform

End-to-end AI runtime assurance from trace collection to policy enforcement.

Platform Overview

Full Visibility Into Every AI Call

Modern AI systems are not black boxes — they're complex pipelines with measurable behavior at every stage. Starseer gives your engineering team the instrumentation layer that should have been built into the model serving infrastructure from day one.

  • Trace every model invocation with microsecond-precision timestamps
  • Correlate inputs, outputs, and intermediate reasoning steps in one view
  • Query historical traces for debugging and compliance investigations
Request a Demo
Starseer trace visualization showing AI model inputs and outputs in real time
Core Modules

Four Pillars of AI Runtime Assurance

Each module addresses a distinct failure mode in production AI. Use them together or start with the one that matters most to your team.

Trace Engine

High-throughput trace collection with structured storage. Handles millions of requests per day without adding latency to your inference pipeline.

Learn more

Policy Engine

Define behavioral contracts for your models using a declarative policy language. Enforce rules at inference time with configurable actions — block, log, or flag.

Learn more

Drift Monitor

Continuous statistical monitoring of output distributions. Detects semantic drift, confidence shifts, and vocabulary changes before they impact downstream systems.

Learn more

Alert Hub

Centralized alert management with severity routing, on-call escalation, and runbook integration. Your team hears about AI failures — not just server failures.

Learn more
Architecture

Designed for Real Infrastructure

Starseer integrates with the systems you already run, not the other way around.

Lightweight Agent

Under 1ms of Added Latency

The Starseer agent runs inside your existing service process. It intercepts model calls asynchronously — traces are buffered locally and flushed in batches, so your inference path is never blocked by observability overhead.

Zero external dependencies in the hot path. The agent degrades gracefully if the Starseer backend is temporarily unreachable — no missed traces, no dropped requests.

Starseer lightweight agent architecture showing asynchronous trace collection
Zero-Trust Data Pipeline

Your Data Stays Yours

All trace data is encrypted in transit and at rest. Role-based access controls determine who can query what — from raw trace data to aggregated metrics.

On-premises deployment available for regulated industries. Starseer never needs access to your model weights or proprietary training data.

Zero-trust data pipeline architecture diagram showing encrypted trace routing
Use Cases

Where Teams Use Starseer

Runtime assurance requirements look different depending on the stakes involved. Here are the most common places Starseer makes a difference.

Financial Services

Fraud Detection AI

Monitor classification confidence and decision consistency in real-time fraud models. Catch the subtle behavioral shifts that precede false positive surges.

Healthcare

Healthcare Diagnostics AI

Maintain complete audit trails for AI-assisted clinical decisions. Enforce output schemas and ensure diagnostic models stay within approved operational boundaries.

Enterprise Automation

Autonomous Process Automation

Supervise multi-step AI agents operating on business-critical workflows. Intercept and review agent decisions before they trigger irreversible downstream actions.