Draft replies to customer billing questions
Drafts replies to billing emails using your policy and the customer's actual order and payment records, quoted verbatim. A person reviews and sends.
- How it works
- For emails like 'why was I charged twice?', 'where is my refund?' or 'can I get the invoice reissued to my company name?', the system retrieves your billing and refund policy and pulls the customer's concrete order, payment and refund records from your shop or accounting system. It then drafts a reply in the customer's language with those records quoted verbatim — figures come from the injected records, never from the model. Your agent reads, adjusts and sends.
- Data you need
- A written billing/refund policy and a handful of answer templates, plus a working lookup or export of the customer's order, payment and refund records from your shop, payment provider or accounting system (Shopify, WooCommerce, Mollie, Stripe, invoicing software). That hookup is the real setup work; without it, drafts stay generic policy restatements and the case loses most of its value.
- What to expect
- Good at routine factual answers where a matching record exists; when none is found, the draft must say so rather than improvise — the model must never invent an amount, date or refund promise. Refund promises are binding under EU consumer law, which is precisely why the send button stays human. Drafting in smaller EU languages is noticeably weaker on the fast small model, though the larger model can take over where that matters. On genuinely angry customers, a templated-feeling reply can make things worse — hand those to a person.
- Where people stay involved
- A person reviews and sends every reply. Anything involving an actual refund decision, a chargeback, a dispute or a legal-sounding complaint goes to a human entirely.
Which model, and what it costs to run
Qwen3-8B
This job answers while someone waits, so latency comes first: we hold it to a single small card and, within that, take the best independent score on sticking to the document.
- Licence
- Apache-2.0
- Weights at 4-bit
- 5 GB
- Context
- 32K tokens
- Publisher
- Alibaba (Qwen Team)
What the hardware costs
One 48 GB card holds it
- Rent in the EU
- $1.60/hrScaleway, Paris (PAR2)
- Buy the card
- $7,569new, one-off
- Or rent it by the token
- $0.04 / $0.04per M in / out · DeepInfra
Hardware only, third-party prices from 2026-07. The figure excludes the KV cache, which grows with context length and how many people use it at once — sized properly in a conversation, not guessed here. Renting by the token is cheaper up front; why our customers still self-host is below.
Sticking to the document
Measured on public documents, by a model acting as judge. Read it beside the answer rate: the lowest hallucination rates on this board belong to models that simply decline more often.
Independent measurement · Vectara · board updated May 11, 2026
Structured output and tool calls
Measured in a sandbox, on somebody else's functions. It tells you which models are capable of the shape of the job, not which one will survive contact with your API.
Independent measurement · UC Berkeley (Gorilla project) · board updated 2026-04-12
Reading a long document whole
No benchmark measures this
No current benchmark ranks today's open models here. HELMET showed that the popular test — finding a planted sentence — predicts nothing, and its own table has not been rerun on 2026 models.
So we do not show a chart here. We measure it on your own content, in the first week, and you see the result before anything ships.
The API is cheaper per token. Here is why our customers don't use it.
We will not pretend otherwise: renting a model by the token from a serverless API costs less per million tokens than a card we run for you. We show that price on every use-case page. What it does not include is the part a shop with a customer database actually pays for.
- 01
Your data never leaves hardware you can point at
A serverless "we don't retain your data" is a clause in a contract. Running the model on a card in Amsterdam is a fact of architecture: your catalogue, tickets and customer records are never sent to a third party at all. For a GDPR audit, that is the difference between a promise and a floor plan.
- 02
The price cannot move without your say-so
A serverless rate card is somebody else's lever. The provider can raise the price, retire the model, or change the terms, and your cost moves with it. The same model on the same card costs the same next year — you own the number.
- 03
The model cannot be taken away
Hosted APIs deprecate models on their own schedule; the one you built on can be gone in a quarter. An open-weight model on your own hardware runs for as long as you keep the lights on. No vendor can end-of-life it out from under you.
And the price gap closes with volume: past a card you keep busy — very roughly four billion tokens a month — owning is cheaper outright, even before the three reasons above.
Getting a case like this one from a conversation to production takes about two months, and you can stop at the end of any phase.
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