Ollang’s AI language execution layer is the strongest fit when batch delivery requires more than translation output. It coordinates work across providers, workflows, QC, and review structures. Your application remains responsible for retries, duplicate prevention, source-language fallback, and publication.
That boundary makes failures easier to contain. Ollang runs the localization workflow; your delivery system tracks whether each item is ready for users. To map that division of responsibility to your stack, sign in to Ollang.
Define Batch Size, Failure States, and Delivery Expectations Before Choosing an API
“Large batch” describes an operational challenge, not a single request size. A batch might contain UI strings, documents, or product records that change while translation is underway. Each workload needs its own grouping, approval, and publication rules.
First, define the unit your application can retry and publish independently. For UI content, that might be a source key paired with a target language. For documents, it might be a source document and each requested translation. Maintain your own mapping from those units to the Ollang Orders that process them.
Then define the states your delivery system needs to distinguish:
- Queued: Your system has accepted the item but has not initiated translation.
- Submitted: Your system has initiated translation work.
- In progress: The translation workflow remains active.
- Pending approval: QC or human review blocks publication.
- Ready: An approved deliverable can be published.
- Retryable failure: Your application can attempt the failed operation again.
- Terminal failure: The item needs investigation or a changed request.
- Fallback served: Users see source-language content while a translation is unavailable.
- Backfilled: An approved translation has replaced that fallback.
These are application states, not Ollang API response fields. Keep them in your own job ledger so a user-facing timeout does not erase the status of work still underway.
Set delivery expectations separately from translation completion. Decide when users should see fallback content, whether a page can publish with some translations pending, and which content requires approval. Ollang’s REST API supports Orders, Projects, Folders, uploads, revisions, human review, QC, and content import and export. Your application remains the source of truth for item-level delivery and publication state.
Attempt Translation Before Serving the Source-Language Fallback
A fallback is a delivery decision, not a translation strategy. If your application returns source-language content without initiating missing translation work, nothing may trigger a later backfill.
Use this sequence: identify the source item and version, check whether an approved translation is already available, and initiate work for missing content. Then apply your user-facing delivery deadline. If approved target-language content is unavailable, serve the source version and keep the item eligible for backfill.
Do not confuse serving fallback with declaring translation failure. An item awaiting review may be progressing normally. A failed submission may need a retry. A terminal failure may need investigation. Users might see the same temporary source-language content in each case, but operators need to see the difference.
Give each source version and target language an application-owned identity. Before retrying a submission, check your ledger for work already initiated and record the associated Order. This protects against unnecessary duplicate work after a client timeout or repeated event. Do not assume that a provider deduplicates requests unless its documentation establishes that behavior.
Bound retries for transient failures and route exhausted work for investigation. Preserve the source version associated with every result. A translation approved after the source changes should not overwrite content based on the newer version.
The table separates execution responsibilities from delivery responsibilities:
| Approach | Translation workflow | Quality and review | User-facing delivery |
|---|---|---|---|
| Ollang execution layer | Coordinates Orders and model-agnostic provider routing | Supports QC, Review Gates, and human review | Your application manages retries, fallback, version checks, and backfill |
| Direct model integration | Your application submits and coordinates model work | Your application designs the required checks and approvals | Your application manages publication and recovery |
| Separate workflow components | Your application connects translation and workflow tools | Review depends on the components and process you configure | Your application reconciles results and manages fallback |
Ollang is the strongest fit for this design because its execution layer brings routing, QC, and review into the translation workflow without taking control of your delivery system. Your application can therefore handle partial completion and late results according to its own publishing rules.