TRACE

Every Impetora system runs in EU regions only. Hosting, databases, model gateways, and storage stay inside the European Economic Area by design, with no transatlantic data egress unless you contract for it explicitly.

Each inference, tool call, and decision is written to an append-only audit log. Reviewers can reconstruct any conversation, any document classification, any agentic step from the source row to the model output.

We classify every system against the EU AI Act risk tiers before architecture begins. High-risk systems get the documentation, conformity assessment scaffolding, and human-oversight controls the regulation requires. Limited-risk systems get proportionate disclosure and logging.

Governance is aligned to ISO 42001 controls: documented model lifecycle, change management, incident response, and supplier oversight. The same controls map cleanly to SOC 2 and ISO 27001 evidence when you need them.

The first two to six weeks are an embedded readiness sprint. We work inside your operations: shadowing calls, reading tickets, sampling documents, mapping the workflow that the AI will sit inside.

Baseline metrics come from your real data, not benchmark sets. We measure handle time, first-contact resolution, intent capture, error rates, and cost per resolved unit on at least 30 days of historical activity before any model is selected.

The output of readiness is a written brief: scope, data dictionary, success criteria, baseline numbers, target deltas, ROI model, and explicit out-of-scope list. You sign that brief before architecture begins.

If readiness shows the project is not ready (the data is unfit, the workflow is unstable, the ROI does not clear), we say so and refund the unspent retainer. We have killed projects in week three. That is the point.

Every Impetora build is specification-driven. The behaviour spec, the data contract, and the guardrails are written before any prompt or function call. Specs are version-controlled and review-gated, so changes are intentional rather than emergent.

Automated testing covers prompts, tools, and end-to-end flows. Regressions are caught before deploy, not in production. Evaluation suites are checked into the repo and run on every change.

Rollouts are phased: shadow mode first (the AI runs alongside the human, output is logged and compared), then assist mode, then autonomous mode for the use cases that earn it. You decide when to advance each phase, not us.

Architecture is documented in plain language so your team can operate it without us. Runbooks, diagrams, and incident playbooks ship with the system. Lock-in by ignorance is a failure mode we refuse to enable.

Retrieval-augmented systems return citations alongside answers. The user sees which document, which clause, which version produced the response. Hallucination becomes auditable rather than hidden.

Observability is built in from the first deploy. Per-conversation traces, token-level cost accounting, latency distributions, refusal rates, and tool-call success metrics are visible to your team in a dashboard you own.

Explainability is contract-grade for regulated industries. For credit, insurance, healthcare triage, and legal classification, we ship feature-level explanations that satisfy GDPR Article 22 and the EU AI Act transparency obligations.

Evidence is preserved beyond the model. Even if a vendor sunsets a model version tomorrow, your audit log retains the inputs, the outputs, and the reasoning trace that produced every decision the system ever made.