Still Needs a Human is the translation quality platform that scores brand voice, naturalness, and register, then keeps a human in control of the verdict. Every delivery checked, scored, and benchmarked.
Machines are fast and humans are busy, so errors and off-brand lines slip through. Most QA tools only catch the mechanical mistakes, and pure AI cannot be trusted to sign off alone.
Machines, and rushed humans, ship copy that is technically right yet lands flat, off tone, and obviously translated.
Tag and number checkers pass copy that reads translated, lands off tone, or breaks the brand voice. They cannot judge marketing.
A model alone is confident and sometimes wrong. For anything a client sees, a person has to make the final call.
Anyone can count tags and numbers. Xbench and lexiQA stop there. The real question is whether the copy reads native, holds the brand voice, and works as a real asset. We score that 1 to 5 against your voice and approved exemplars, and a human always confirms.
We grade fluency and naturalness the way a reviewer would, not by counting edits, so translationese gets caught.
Tone, register, and brand voice scored 1 to 5 against your approved exemplars, so a line that is correct but off brand still gets flagged.
The AI proposes, your reviewer confirms or overrides, and every sign-off leaves a defensible trail.
Everything mechanical checks out, so it passes. The off-brand, translated feel goes straight to the client.
Literal and flat. A native asset would say "İçerik üreticilerinden, içerik üreticilerine." Warmer, on brand, and it scans.
The human accepts the flag, applies the transcreation, and signs it off. Proof attached.
QA is the on-ramp, high volume and easy. LQA, with transcreation and brand voice, is the depth. Eval tells you which engine to trust. One engine underneath all three.
The high-volume on-ramp. Deterministic rules catch the certain issues instantly, and an AI judge re-reads only the risky segments. A clean report with every issue, the evidence, and a suggested fix.
See a QA reportThe depth. Score deliveries on your own MQM or DQF template, then run the transcreation and brand-voice mode that judges naturalness, register, and brand fit. The AI pre-scores, a human confirms.
See the scorecardThe engine decision. Compare every MT and LLM engine on the same source, ranked against your own human reference, with a best-quality and a best-value pick, plus cost and latency.
See a bake-offAn XLIFF, a TMS export, or work live inside Crowdin, Trados, and WorldServer. No re-keying, no migration.
Deterministic rules catch the certain errors instantly. The AI judge re-reads only the risky segments, so it stays fast and cheap.
A QA report, a human scored LQA card, or an engine ranking. Ready to act on, and nothing gets edited without a human.
A browser check inside your editor, a watched Dropbox folder, a Crowdin app, or an API for your own pipeline. No platform to migrate to.
Isolated workspaces, a real paper trail, and the controls procurement asks for. Run 100+ locales and a vendor program on one quality system of record.
Begin self-serve with the everyday QA and Eval. Add human LQA, configured profiles, transcreation, integrations, and the vendor program when you are ready.
For smaller teams and freelancers who want a fast, honest check. Onboard with an invite code and get a first result in under a minute.
For in-house localization teams and large LSPs who grade vendors, run 100+ locales, and need a defensible system of record. Demo and quote led.
Tell us your stack and your language pairs. We will set you up with a workspace and run your first QA, scorecard, or engine bake-off on a real sample.
Prefer to explore on your own? Get started or sign in.