The AimRank Production Standard
Anyone can demo AI. We prove it.
Individual AI is incredible, until a business depends on it. Then it breaks on reliability, calibration, drift, audit, and scale. The AimRank Production Standard is the layer that turns a clever demo into a system you can trust. Every solution we ship meets four guarantees, or we tell you plainly that it can't.
The Four Guarantees™
Calibrated & evaluated
Every output is checked against a real eval suite, and the system escalates when unsure instead of bluffing.
We don't ship on a hunch. The agent runs against a versioned eval suite scored by an independent judge (itself validated against human labels), with task, trajectory and groundedness checks. It ships only if the suite passes. Where a classical model is in the loop, we add probability calibration (Brier, ECE) and fairness on top.
What you get as proof: The eval report with the suite scores and the pass-or-fail gate.
Drift-aware
Quality can't quietly decay after launch, even when the underlying model changes under you.
We re-run the eval suite on a schedule and on every model upgrade, and watch behaviour (refusal rate, tool mix, cost) and the topics coming in. A regression alerts and triggers a fix, and the model version is pinned so a provider update can't silently shift behaviour. A classical model in the loop adds input-drift monitoring (PSI / KS).
What you get as proof: The eval-regression history and the alert log.
Explainable
Every decision can be explained to a person, a board, or a regulator.
Each decision carries its reasoning, the full tool-call trace (what it did, with what inputs and outputs) and the sources it relied on, recorded as it runs so a human can review, approve or override. A classical model in the loop adds per-decision SHAP reason codes. This is what satisfies EU AI Act Articles 13 and 14.
What you get as proof: The decision trace, tool calls and citations for any single decision.
Cloud-agnostic & self-documenting
It runs in your cloud, records everything it does, and you own it. No lock-in.
The whole system ships as one container (Terraform and Kubernetes) in your own region, so your data never leaves. Every action is written to a tamper-evident, hash-chained log you can replay (Article 12), it generates its own Annex IV documentation, and autonomy is consequence-gated with human-in-the-loop and a kill-switch.
What you get as proof: The Annex IV dossier and the replayable audit chain of every action, both yours to keep.
How a build actually runs
Six steps, with a human checkpoint at each one.
- 1 Scope & risk-class
Define the buyer, the wedge, and the risk class. Legal sign-off where it carries weight.
- 2 Data intake
We point at your data in your own store. No personal data leaves your infrastructure.
- 3 Model, calibration & bias audit
Baseline first (classical often beats deep), validate calibration, review disparate impact.
- 4 Drift baseline & monitoring
A real reference window, a nightly job, and alerting wired up before launch.
- 5 Evidence & dossier
Complete the validation docs and verify the audit chain.
- 6 Deploy & handover
Cloud-agnostic deploy, runbooks, and training so you own the system.
For agents: Agent Assurance
The four guarantees secure each tool an agent calls. When agents start to act, we lift assurance to the decision and action level: we evaluate the decision and the whole trajectory (not one output), watch for behavioral drift, trace every action into a tamper-evident log you can replay, and gate autonomy with guardrails, human-in-the-loop, and a kill-switch. Autonomy without assurance is recklessness; agents make assurance more valuable, not less.
Built for agents, not just screens
Every system ships as an MCP server (predict, explain, evidence, healthcheck), so your agents can use it directly, not just your people.
This page describes the methodology and the evidence each step produces. The implementations live inside the AimRank solutions we build for you.