Per-pair engine benchmarking
No single engine wins every language pair. We measure on your content and route accordingly instead of trusting vendor claims.
Service
AITranslation
Machine translation you can put in front of customers.
Running
- MT + LLM post-edit
- Quality estimation
- Terminology control
- Reviewer routing
Theproblem
Raw MT is cheap andoccasionally expensive
A negated warranty clause, a product name translated into a common noun, a formal register where the market expects informal. The usual fix is having a human read everything, which puts the cost back where it started.
Abdul Rehman
AI Engineer · leads this service
Our approach
We pair engine output with an LLM post-edit pass against your terminology and tone, then score every segment so reviewers open only what actually needs judgment, and their corrections improve the routing.
What youget
- Benchmarked engine routing per language pair
- Terminology and tone constraint set with test cases
- Quality estimation calibrated to your reviewers
- Review interface and correction pipeline
- Review scope
- Risk-ranked
- Terminology
- Enforced
- Engine choice
- Measured
Capabilities
What AI Translationincludes
Scoped per engagement. We start with whichever of these removes the biggest constraint first.
Terminology and tone enforcement
Product names, legal phrasing and register are applied as constraints on output, not as a style guide someone is asked to remember.
Automated quality estimation
Every segment carries a confidence score so review effort concentrates where the risk actually is.
Reviewer feedback loop
Corrections are captured as data and change future routing and scoring rather than disappearing into a document.
Domain adaptation
Tuning on your historical bilingual data so the output sounds like your company, not like a generic engine.
Batch and real-time modes
Bulk catalogue translation and live API translation share the same terminology and quality controls.
Delivery
Howa project runs
Typical shape for this service. Timings move with scope, the order does not.
- 01
Benchmark
Week 1
We run your real content through candidate engines and measure, rather than accepting a vendor's numbers.
- 02
Constrain
Week 1–2
Terminology, tone and formatting rules are encoded and tested against known-hard examples.
- 03
Calibrate
Week 2–5
Quality estimation is tuned against your reviewers' real corrections until the ranking is trustworthy.
- 04
Hand over
Ongoing
Pipeline, benchmark harness and review interface, documented and owned by you.
Typicalstack
Tools we reach for
Chosen per engagement and biased toward what your team can maintain after we leave.
- Python
- LLM APIs
- COMET / QE models
- FastAPI
- PostgreSQL
- React
Commonquestions
The questions we getabout Translation
Is this better than hiring translators?
It is not a replacement for them. It changes what they spend time on: reviewing risky segments instead of re-typing correct ones.
Which languages do you support?
Any pair the underlying engines cover. The value we add is routing, constraint and scoring, which is language-agnostic.
Can we keep data on our own infrastructure?
Yes. Self-hosted models are supported where confidentiality or residency rules it out of the cloud.
Starthere
Tell us the process,not the solution.
The most useful first message describes what someone on your team does by hand today and how often. That is enough for us to tell you whether it is worth building.
What happens next
- A named engineer reads it, not a form inbox
- Reply within 24 hours, even if we're not the right fit
- A 30-minute call to trace the process end to end
- A fixed-scope quote, or an honest no