Spot GDPR requests, legal threats and chargebacks the moment they arrive
Watches your inboxes for GDPR requests, payment disputes and legal letters, and routes them straight to a named person with a plain summary.
- How it works
- A dedicated detector watches the inbound text channels you connect — shared mailboxes, contact forms, chat transcripts — for messages that carry deadlines or legal weight: data-deletion and access requests (GDPR gives you one month), payment-dispute messages from customers, letters from lawyers or consumer authorities, safety complaints about products. These are routed to a named responsible person immediately, with a plain summary of what is being demanded and by when. It works across the EU languages your customers write in.
- Data you need
- Access to the inbound message streams — the detector can only see channels that are actually connected, including any personal-alias or info@ inboxes where legal mail tends to land — plus a short internal playbook naming who owns each category. No historical examples needed: the categories are well-defined enough for the model out of the box.
- What to expect
- Explicitly worded requests and disputes are caught dependably across languages. The detector is tuned for recall over precision — a false alarm costs a minute, a missed GDPR request can cost a regulator complaint — so expect some false alarms by design. Oblique phrasings ("remove me from everything") are caught less reliably than explicit ones, in any language, so keep a human skim of the general queue as a backstop. And know what it cannot see: formal chargeback notices usually arrive in your payment provider's dashboard, not your inbox, and legal letters still arrive by post — it covers the text channels you connect, not those.
- Where people stay involved
- The model only detects and routes; every response is human. That is not a limitation but the point — these are exactly the messages where an automated reply creates liability.
Which model, and what it costs to run
Qwen3-32B
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 structured output and tool calls.
- Licence
- Apache-2.0
- Weights at 4-bit
- 18 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
- $0.12 / $0.12per M in / out · Nebius AI Studio · EU
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.
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
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
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.
Also in customer service
All 15 →Sort the support inbox before anyone reads it
Classifies every incoming message into your own categories, pulls out order details, and routes it to the right queue before anyone opens it.
Draft answers to where-is-my-order emails
Looks up the real order and tracking status and drafts a reply with the carrier's information, ready for an agent to approve and send.
Suggest replies from your own help pages and policies
Finds the relevant passages in your FAQ and policy pages and drafts a reply based only on them, with a link to the source for the agent to check.
Handle return and refund requests against your own policy
Checks a return request against your written returns policy and drafts the instructions or a polite refusal, for a human to approve.
Next
Tell us what your team does by hand
Describe the process that takes the most time. We will say plainly whether a model is the right tool for it.