How We Refine Spanish MTPE

While much of the industry focuses on improving AI engines and training data, we focused on something else: the human layer.

Because even the strongest MT output fails when post-editing is inconsistent.

When MTPE Feeds Back Into Your System

MTPE is not just a delivery step. It feeds your long-term language assets.

When post-edited content re-enters your Translation Memory, it becomes part of your future baseline.

If inconsistencies or terminology drift are not fully resolved, they propagate.

Over time, this can quietly reduce the reliability of both your TM and your AI output.

AI-driven workflows improve — or decay — based on the data they ingest.

That’s why MTPE must be treated as infrastructure, not as a cleanup task.

Training the Human Layer

We built a structured training program specifically for Spanish MTPE.

We first brought in external experts to train our senior staff.

Then we developed our own internal framework, incorporating practical knowledge from hundreds of real-world projects.

MTPE is not traditional translation. It requires a different mindset.

Post-editors must relearn:

The MTPE workflow
The tools
What to correct
What to ignore

This prevents over-editing, under-editing, and inconsistent intervention.

Testing for Stability

No linguist is considered fully trained without completing six rounds of post-editing testing and structured feedback.

We track performance across each cycle, measuring both quality and productivity.

This allows us to identify where performance stabilizes — and where further calibration is needed.

The goal is not just quality.

It is sustainable quality at scale.

The Process

Before starting any MTPE project, we evaluate the raw MT output.

If the baseline quality is too low and the text is nonsensical, we do not proceed with post-editing.

MTPE only works when the engine output meets a minimum threshold.

The workflow itself consists of two stages:

Step 1: Post-Editing

The post-editor corrects:

Additions
Omissions
Incorrect terminology
Grammar and spelling
Register
Formatting

At this stage, the text is accurate and error-free.

Step 2: Humanization

A second reviewer refines style, flow, and naturalness — ensuring the text reads as if originally written in Spanish.

Making AI Scalable Without Quality Drift

We designed our Spanish MTPE framework to stabilize the human layer of AI-driven workflows.

Structured training
Measured performance stabilization
Threshold evaluation before post-editing
Two-step review model

We ensure the content entering your system strengthens it.

Let’s Talk

If AI is part of your Spanish workflow, the question is not whether to use it.

The question is whether your human layer is engineered to support it long term.

Let’s review your current Spanish MTPE workflow and see how it can be strengthened.