Triage and routing
Inbound tickets, emails and documents sorted into the queues you already run. Your queue structure, not ours.
Data labeling
Labeling at volume, where the cost per item is what matters.
Classification is the workload where model tier stops mattering most. High volume, low reasoning, a clear right answer — and a bill that scales with the work.
The shape of the work
Sorting items into categories is the least glamorous thing a language model does, and often the most valuable at scale. Ticket triage, document routing, sentiment tagging, intent classification, language detection — all high volume, all with an answer that is either right or wrong, none of them needing deep reasoning.
That combination is exactly where a Flash-class model is the correct choice. You are paying for a reasoning capability you would never use.
One thing to be clear about: this is not a data labeling service in the outsourced-team sense. There are no annotators on our side and no per-item quote from a delivery manager — you get the endpoint, run your own data through it, and pay a rate rather than a headcount. If what you need is a managed data annotation vendor with people in the loop, this is not that, and we would rather say so before you fill in the form.
Tagging and extraction
Multi-label tagging where an item can belong to several categories at once, plus pulling the field that justified the label.
Multilingual labeling
One taxonomy across languages, so a French ticket and an English one land in the same queue without separate rules.
Backfilling a backlog
Historical archives that were never labeled can be processed in bulk — and batch work fits off-peak billing, at half the peak rate.
How to judge whether it is working
Label a sample by hand first. Then run the same sample through the model and compare. That comparison — not any published benchmark — is the number that tells you whether this works for your taxonomy. It is also the number we would want to see before quoting you a rate, because a taxonomy that needs a reasoning model is a different product.
Questions about labeling
How accurate is it on our taxonomy?
That depends entirely on the taxonomy — how many categories, how distinct they are, and how much overlap there is between them. Label a sample by hand, run the same sample through, and compare. We will do that with you before you commit.
Is this a data labeling service, a tool, or a platform?
None of the three, in the usual sense. Data labeling services usually mean people: the data annotation companies and the data labeling companies whose staff label your data by hand, priced per item or per hour. Data labeling tools and data labeling platforms usually mean software with a human sitting in its interface. This is an API: the labeling happens in the model, there are no data labelers on our side, and the review queue is one you own rather than one you rent.
What if our categories overlap?
Multi-label output handles that. You can also ask the model to return the evidence it based the label on, which makes reviewing the output far faster.
Can we use it for a backlog of unlabeled historical data?
Yes, and it is a common first project because there is no live system to disturb. Batch work like this fits off-peak billing.
How do we handle items the model is unsure about?
Route them to a human queue. Ask for a confidence signal alongside the label and set a threshold — anything below it goes to review rather than into the system silently.
Does it work in languages other than English?
Yes, and one taxonomy can cover several languages at once. That is usually simpler than maintaining per-language rules.
CLASSIFICATION & ROUTING
Input
Inbound ticket · email · document
high volume, low reasoning
DeepSeek V4.1 Flash
Output
Queue · label · priority
per item, at volume
- Sorts into your existing queues
- Steady cost per item
- Accuracy bar well inside Flash
Tell us what you're running.
Send us your monthly volume and what you use today. We usually reply within one business day with a price and a named contact.