AI Infrastructure
Every technology on this page ultimately depends on a model running somewhere near the sensor. Getting a model to work on a workstation is a research result. Getting it to run reliably on a hundred heterogeneous edge devices, and knowing when it has started to degrade, is an infrastructure discipline.
Mentat builds that discipline: models as versioned artifacts, hardware-aware packaging, staged rollout with automatic rollback, and continuous monitoring of both system health and prediction quality.
We also insist on deterministic guardrails around probabilistic components. A model produces a probability. The action taken on that probability should be governed by explicit, testable, reviewable rules, not by the model alone.
What we build
A model is a build artifact with a version, a provenance record, and a target platform. It gets released like software, because it is software.
New models reach a canary slice of the fleet first, are compared against the incumbent on live traffic, and roll back automatically when they underperform.
Field conditions change: lighting, weather, camera aging, seasonal traffic. Model performance degrades quietly unless something is watching for it.
Probabilistic output wrapped in explicit rules: thresholds, required corroboration, human review, and hard limits on what an inference alone may trigger.
How we get there
Versioned, signed, provenance-tracked, and stored in a registry, with a documented lineage back to training data and code.
Build and validate against the actual heterogeneous device fleet, measuring the accuracy cost of every optimization.
Canary rings, shadow comparison, promotion gates, and automatic rollback, applied to models exactly as they are to application code.
Continuous drift and quality monitoring, with deterministic policy governing what any given inference is permitted to trigger.
Governance & safeguards
This mirrors the position we take across our AI work: build the deterministic foundation first, then let probabilistic components operate inside it. A model that can trigger consequences without a rule layer and a human in the path is an unaccountable system.
Every production model carries measured accuracy, known failure modes, and the conditions under which it should not be relied upon.
For any automated determination, the inputs, model version, confidence, and applied policy are all recoverable from the audit record.
Models surface candidates and rank possibilities. Decisions that affect a person require a human, with review requirements enforced by the platform.
Built on
We build on established open-source and cloud-native building blocks rather than proprietary black boxes, so the platform stays inspectable, portable, and yours.
Model lifecycle
Edge runtime
Guardrails
Related
Putting models in the field?
We can review your model deployment path, monitoring coverage, and guardrail design, then build the pipeline that makes edge inference operable.