Across every route

AI & Machine Translation

Does the AI or MT claim hold up? AI and machine translation change who does the work, what must be reviewed, and how effort and risk are measured. A provider claim about savings or quality is not a result until the workflow, evidence, and assumptions can be tested.

Explore the decision basis

Decision basis

Test the full workflow, not the model alone.

We compare the current baseline with the proposed or deployed workflow and examine how model selection, quality estimation, automated post-editing, confidence thresholds, and human-review routing affect quality, cost, and control. That includes the translation memory, terminology, and style guides the workflow draws on, and any automated score that lets content skip human review. Where operating records exist, we compare paired raw and reviewed output, correction effort, billing units, and dated changes to models, prompts, and rules. An acceptance rate, automated score, or provider benchmark alone does not establish quality or savings. A proposal is tested as a model, not passed off as an observed result.

Baseline or proposed case: Separate observed operating results from provider claims and pre-deployment assumptions.
Human correction and quality: Count the review effort, output risks, and controls that remain around machine output.
Costs and economics: Compare cost and effort on a documented pricing basis for the actual or proposed workflow, and identify what a provider-managed service adds beyond generating the translation.
Change control and rollout: Specify owners, evidence requirements, and the go/no-go conditions for a controlled rollout or pilot.

Evidence limits

A proposed use case is not an observed result.

For a deployed workflow, paired raw machine output and final reviewed output can support empirical assessment. Before deployment, work starts from documented claims and an explicit model; it cannot substantiate realized savings.

Possible outcomes

Automation is not the predetermined answer.

The result can be a controlled pilot, revised human-review thresholds, narrower routing, a different split of work between buyer and provider, an evidence request, or retaining the existing workflow. A failed claim is not proof that every AI/MT approach fails.

Relevant engagements

Match the evaluation to the AI claim.

One engagement examines the decision and its operating conditions, whether the workflow is live or planned.

Engagement

AI and Machine Translation Audit

For an AI/MT workflow that is already running, or a proposed use case whose productivity, quality, or cost claims need to be examined before rollout.

Starting scope

One deployed or proposed workflow or use case, one provider or stack, one commercial decision, and up to 15 program languages. Use observed evidence for deployed work or documented claims and assumptions for a proposal.

Included work

Document the baseline or proposed workflow.
Test the claimed performance or productivity uplift.
Assess quality risks and controls.
Account for human review and correction effort.
Examine commercial economics.
Review change-control arrangements.
Analyze the workflow and the decisions it depends on.
Define initial controls and rollout requirements.

Expected outputs

Formats confirmed at scoping.

Workflow and decision map.
Claim and evidence register.
Normalized cost and effort view or pre-deployment cost model.
Control requirements.
Rollout and measurement plan.
Recommendations and findings briefing.

Operating boundary

Legal and security specialists determine compliance; we assess the localization workflow decision.

Production operation, model development, ongoing monitoring, full-language validation, system administration, and provider remediation are outside scope.

Optional evidence module

Independent Linguistic Validation

Independent Linguistic Validation can be added to an eligible engagement when an independent sample would help test the evidence. It is not available as a standalone service.

Fit call

What claim needs evidence before rollout?

Start with the proposed workflow and the claim behind it. Pilot results, reviewer records, and cost assumptions help establish what can actually be tested.

Request a fit call

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