For translation agencies & freelancers

The errors that reach your client, caught before they ship

Every problem below is one a translator or agency told us cost them a client, a rework fee, or hours of unpaid review. Here's what happens to each in ChatsControl.

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Your files are stored in the EU and deleted 30 days after upload by default. Text that goes to an AI model passes through OpenRouter (US) with training off, and the rule-based checks never leave our servers - see our subprocessors and OpenRouter's data policy.

The false-positive noise that makes people switch checks off

✕98% of your QA warnings are false positives, so you stop reading them
✓Checks are locale-aware (real CLDR rules, not blunt regex) with MQM severity, so the short list is worth reading - and you can mute whole classes of known false positives yourself, per project.
✕Thousands and decimal separators are endless whack-a-mole with regex
✓Decimal and group separators are checked per locale (1,000.50 vs 1.000,50), including the OCR split-decimal case, so you stop hand-writing rules.
✕Section numbering like 3.2.1 is read as a changed number
✓The numeric check explicitly skips section and clause numbers, so document structure never fires a false accuracy error.
✕A number inside a product name like “Gizmo 1000” is flagged as a changed figure
✓We pair characters exactly so a product-name digit doesn't fire - and we deliberately never add a heuristic that could mute a real change to a dosage or an amount in a CRITICAL numeric check.
✕“Source is same as target” fires on the client's brand, over and over
✓A brand kept verbatim across the whole file collapses into one finding with its occurrence count, instead of the same warning N times.
✕A borrowed termbase full of words like “home”, “in”, “of” - and typos
✓Glossary hygiene flags stop-words, too-short and over-frequent entries, duplicates and conflicts; a separate check catches likely typos in glossary terms.
✕Because of the noise, the REAL errors slip through
✓That's the whole point: less noise means real defects don't drown, and an AI semantic pass catches the meaning errors deterministic checks can't see.

What ships to the client broken - the expensive category

✕An untranslated source segment shipped straight to the client
✓Untranslated and empty segments are flagged before delivery - the one-click miss that reaches the client in every CAT tool.
✕“X is not Y” came back as “X is Y” and passed proofreading
✓A semantic pass catches meaning inversions, swapped subjects and reversed direction - the fluent-but-wrong MT output that sails through a human read.
✕Text inside an image never got translated - 44 words, a €780 layout bill
✓We OCR image-only PDFs and photos your CAT tool can't import at all; catching text baked into embedded images inside an otherwise-editable file is next on the roadmap.
✕Pages after a blank page were skipped entirely, and the client found it
✓The scan pipeline processes every page; an explicit page/block completeness signal (“content possibly missed on page N”) is on the roadmap, built on data the OCR already emits.
✕A 100% TM match dragged in another client's product name - £315 off the corrector
✓Glossary and forbidden-term checks catch a wrong brand or banned term in the target; a dedicated TM-contamination signal is planned.
✕A number written as a word (“2” → “twice”) is flagged as a missing figure
✓The numeric check already suppresses many of these; broad spell-out coverage is being verified on real corpora rather than promised blind.

Scans, OCR and layout - what CAT tools can't do

✕An image-PDF can't be imported, but everyone assumes it can
✓We OCR image-PDFs and photos - exactly the files CAT tools reject - and turn them into translatable, layout-restored documents.
✕Rebuilding the layout of a scan eats unpaid hours
✓We rebuild the layout 1:1 automatically, painting the translation back onto the original scan - the restoration work that usually isn't billable.
✕Hyperlinks broke when the file went from the CAT tool back to Word
✓Our PDF-to-DOCX conversion preserves hyperlinks, so they don't quietly disappear on the round trip.
✕Zero-width characters wrecked your match rates - invisible, and nobody checks for them
✓We flag zero-width and bidi characters (U+200B, BOM, soft hyphen, RLO) that most QA tools never look at, before they corrupt your TM leverage.
✕A password-protected PDF lands Friday evening and quietly breaks the import
✓We detect a password-protected PDF the moment you upload it and say so plainly, instead of handing back an empty result you notice on Monday.
✕Nobody checks the OCR - indices, superscripts, Greek letters in formulas
✓Overlay keeps the original scan visible for verification; a low-confidence OCR flag (per-token score, Greek-in-formula) is on the roadmap on data we already capture.
✕Numbers, sums and dates aren't in the word count, so that work is unpaid
✓Our calculator counts by characters (numbers, dates and sums included), and OCR gives a real count straight off a scan.

Machine-translation quirks

✕The same term comes back translated four different ways in one document
✓An AI consistency pass flags a single source term rendered several ways across the document - no glossary required - so it reads consistently.
✕Terminology drift across a document is a reputation risk
✓Glossary adherence and forbidden-term checks plus segment- and term-level consistency keep terminology aligned document-wide.
✕The MT output is smooth but wrong, and it passes review
✓The semantic pass targets exactly this - meaning that reads fine but says the wrong thing - rather than only surface fluency.
✕Terminology checks don't tell a noun from a verb, so a valid form reads as a missing term
✓The glossary is morphology-aware for Russian and Ukrainian, so one entry covers inflected forms instead of firing on every variant.
✕The AI knows its typical mistakes but can't catch them itself
✓The semantic pass flags those recurring MT failure modes so a human sees them, instead of trusting the model to police itself.

What translators say

I don't really know how to compliment your tool - it's just too perfect. Tables are well formatted. I see tools like this as great helpers for formatting-heavy or repetitive documents

Williams Christopher
Williams Christopher
EN→FR & ES Translator | MTPE & Linguistic QA Specialist

Your tool has created a really good Hungarian translation! Better than most machine or AI output I post-edit. It's awesome! I really liked the Analysis section with all the relevant decisions displayed. This tool is amazing, and my clients would certainly buy it

Réka Patik
Réka Patik
EN→HU Translator | Human Translation Specialist

The interface feels very intuitive, especially for translators who work with CAT tools. I tested it with a technical video game text and was pleasantly surprised by how well it handled specific terminology. The way it preserved DOCX formatting is a big advantage - anyone in translation knows how much time formatting issues cost

Lorenzo Pereira
Lorenzo Pereira
EN→PT-BR Translator | Software, Hardware & Game Localization

It's the need of the hour to adapt with technology. Simple text gave a flawless result - the syntax and flow felt completely natural. As a translator, this tool will save me a lot of time. I'd use it to do the translation and then simply proofread for flow and accuracy

Mariyam Usmani
Mariyam Usmani
French Language Specialist | International Client Support

The analysis panel explaining translation decisions is a game-changer. The bilingual comparison view is exactly what linguists need for MTPE and quality checks. Combined with Custom Instructions for enforcing terminology, this tool feels genuinely built for professional translators

Winnie Wairimu
Winnie Wairimu
SW↔EN Translator | MTPE & Linguistic Quality

Excellent formatting + good translation - every translator dreams about this!

Natasa Budisin
Natasa Budisin
Certified Court Interpreter | Legal Translator (SR, IT, EN)

The translation is quite good in terms of grammar, terminology use, and sentence structure

Lyudmila Dovgan
Lyudmila Dovgan
UK Translator | Maxword Translation Agency

Translation looks good actually. The separate tool sections for poetry and receipts are a good touch and something you could develop into more tools. I know these things are not easy, so I think the software is good

Marie Simone Caroline Dennis
Marie Simone Caroline Dennis
Language Consultant | Co-Founder of Lingua.ink

Your translation is excellent. I ran a comparison between ours, here is the tracked changes. I had translated alberi as trees, but I agree more with your nautical translation. But regarding the 'disposto', it is il mare that is disposto, not i segreti... But in any event we both had the same correct translation

Joe Dougherty
Joe Dougherty
Localization Advisor | Enabling AI localization strategies

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Questions translators ask

Is this a replacement for my CAT tool?
No. It's the QA and scan layer around whatever CAT tool you already use - run a finished translation through it before delivery to catch what slipped past.
How is this different from Verifika or Xbench?
Those are strong deterministic QA tools. We add locale-aware suppression to cut the false positives that make people switch checks off, plus an AI semantic and term-consistency layer and a 1:1 scan-restoration pipeline they don't have.
Which languages does it cover?
The QA checks are locale-aware for the major European and CIS languages; the scan and OCR pipeline works on any script.
Do I have to send the client's document to your servers?
You upload a document to run the checks; you control how long anything is kept in your settings. For a quick look, the QA validator runs without an account.
Is there a free version?
Yes - the QA validator and the price calculator both run for free without an account.