Give your team one search box for everything the company knows
One internal search over SOPs, wiki pages and past tickets, running entirely on EU-hosted hardware so documents never leave your infrastructure.
- How it works
- Your SOPs, policies, wiki pages, past support tickets and internal how-tos are indexed with embeddings, so staff can ask 'how do we handle a return from Switzerland?' and get the relevant passages regardless of which tool the answer lives in or how it was worded. Optionally the small language model composes a short answer on top of the retrieved passages. Everything runs on your EU-hosted environment, so internal documents never leave your infrastructure.
- Data you need
- The documents themselves, exportable to text — Notion, Confluence or Google Docs exports, helpdesk ticket archives. Plus an honest map of who is allowed to see what, because search must respect existing permissions.
- What to expect
- Genuinely useful once a company has more written knowledge than anyone can hold in their head, and it finds answers phrased nothing like the question. The classic failure is confidently surfacing an outdated policy because nobody deleted the old page — search makes stale documents more visible, not less wrong. And most 5–20 person companies have less written down than they think: if the corpus is thirty documents, a well-organised folder may serve you just as well.
- Where people stay involved
- Someone owns document hygiene — archiving superseded pages, marking what is current. Staff should treat generated summaries as pointers to the source, not gospel.
Which model, and what it costs to run
Qwen3-8B
No public benchmark ranks this particular job, so this is not a leaderboard pick — it is where we would start: small, permissively licensed, and strong wherever it has been measured. We prove it on your own content before anything ships.
- 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.
Search over your own catalogue
No benchmark measures this
MTEB and BEIR rank embedding models, but their evaluation sets sit inside the models' training data. Scores fall by double-digit nDCG on a private corpus, so the ranking does not transfer to your catalogue.
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.
Also in search & discovery
All 12 →Let shoppers search in their own words, not your catalogue's words
Meaning-based search next to your keyword search, so loosely phrased queries find the right products even when no words match your titles.
Stop losing sales on 'no results found' pages
When keyword search returns nothing, the query falls through to embedding search and shows the closest products instead of a dead end.
Handle misspellings, synonyms and mixed-language queries without maintaining dictionaries
Multilingual embeddings put 'trui', 'pullover' and 'jumper' near each other, cutting the need for hand-maintained synonym lists in an EU shop.
Let customers describe the look and find matching products
Shoppers describe a style in plain words — 'minimalist Scandinavian living room, oak and off-white' — and get products whose text matches it.
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.