Tokenisation specialists for your dedicated team.

Where tokenisation fits in a long-term product team.

Tokenisation is not uniform across languages, and that has direct commercial consequences. English is efficiently tokenised because the vocabularies were built around it; Thai, Japanese and many other languages consume considerably more tokens for the same content, so a multilingual product costs more per user in some markets than others.

It also explains behaviours that otherwise look like model defects. Difficulty counting characters, inconsistent handling of unusual formatting and sensitivity to whitespace all follow from the model seeing tokens.

What your dedicated team does with tokenisation.

Counting tokens accurately matters for anything that must fit a context window. Estimating from character counts is unreliable enough to cause truncation failures in production, and the correct tokeniser for the specific model is the only accurate source.

Getting that right in the retrieval path is detail work that prevents a recurring class of failure, handled by your dedicated AI team.

Common tokenisation use cases.

• Cost projected per language Token efficiency measured per market, because it varies substantially.
• Context budgets calculated accurately Real token counts, so truncation never surprises.
• Model behaviour explained Character-level oddities recognised as tokenisation effects.

Adding tokenisation skills to your team.

Tokenisation detail sits inside AI capacity, with accurate counting built into any path that must respect a context limit.

Tokenisation specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.