Apliko
Problem we solve
We need to localise content for 5+ markets, fast
AI translation and cultural adaptation, drafted straight into your CMS.
A pipeline that takes one source article and produces region-aware drafts for every market you launch in.
Signs this is your problem
You will recognise yourself in at least one of these
- Translation agencies are slow and expensive.
- Marketing wants to scale to more markets but the localisation pipeline does not.
- Tone and cultural references rarely survive a direct translation.
- API rate limits cause translation jobs to fail silently.
Why it happens
The root cause, in plain language
Translation is not a one-step transformation — it is parse → translate → adapt → draft into the right CMS slot, repeated per market. Without an orchestrated workflow, each market becomes a separate manual project.
Our approach
How we actually fix this
- 1
Take base content (English or otherwise) and run it through AI translation tuned per market.
- 2
Adapt cultural tone, idioms and references — not just words.
- 3
Draft directly into your CMS in the correct localised slot, ready for human review.
- 4
Manage API rate limits and throttling so jobs never silently fail.
- 5
Add new language pipelines on demand.
What you can expect
Outcomes our clients see
- Smooth transitions between newer AI translation models as they improve.
- API limits and rate throttling actively managed to avoid bans.
- New languages and regions configured rather than rebuilt.
Case study
Marketing team — automated content localisation
A marketing team needed to scale their global reach. Our automation takes base English content, uses AI to translate and adapt the cultural tone for 5 different regions, and drafts the posts directly into their CMS.
Related solutions
People with this problem usually also have these
Launching an app in a new country — language and rules are different
A localisation layer that handles UI strings, date formats and regional rule sets as first-class concerns.
AI CoworkersOur AI bill is growing faster than the revenue it generates
Model selection, prompt engineering and caching tuned against your actual usage data.
AI CoworkersOpenAI or Meta changed their API and everything broke
Proactive deprecation tracking and refactoring under a maintenance plan that gets ahead of the change.
Recognise this problem? Let's talk.
Tell us what you are seeing and we will tell you whether this is the right fit — and what the next step looks like.