Platform
The home highlights the four modules that carry the ROI. Here is the full runtime — seven governance modules and the five proprietary engines that decide earlier, refuse harder, and prove further.
Seven enforcement modules. One SDK. Zero pipeline rewrites.
Every request scored across cost, quality, and latency. The Arbiter routes to the cheapest model that still meets your SLA — in real time, at every call.
Hard budget caps per team, agent, and billing period — enforced at the runtime layer. When spend approaches limits, Azoth acts. Not alerts. Action.
Recursive agent loops are invisible until they appear on your bill. We monitor execution graphs in real time and terminate runaway calls before the cost compounds.
Your prompts carry token weight you're paying for twice. Our engine compresses, restructures, and optimizes every prompt — 40% avg reduction, zero quality loss.
Identical questions rarely arrive identically phrased. We match semantically equivalent queries to cached responses — eliminating redundant model calls.
Full cost attribution by agent, model, team, and workflow. Real-time spend, anomaly detection, and 90-day economic forecasting. Finance and engineering, unified.
Connect Azoth to your existing infrastructure. GitHub App integration triggers regression tests on every PR. Native integrations for Slack, PagerDuty, and custom webhooks keep your team informed.
Beyond routing and caching. Azoth embeds five proprietary systems that decide earlier, refuse harder, and prove further than any AI infrastructure layer available today.
Requests are evaluated before reaching a model. The majority that don't require generative reasoning are resolved instantly — at a fraction of the cost.
Prior reasoning is captured and reused across related requests. Teams stop paying to re-derive conclusions their system has already reached.
Long-session context is compressed without losing decision integrity or compliance constraints. Conversations and agent memory become dramatically cheaper to maintain.
Every request is cost-modeled before execution. Model selection and resource allocation adapt dynamically as budget pressure changes — service never degrades abruptly.
Complex AI workflows are simulated against real historical data before a single token is spent. Teams see cost, risk, and quality projections — and act on them before deployment.