Vlincta. Europe's #1 cost efficient SMB all in one AI platform.

Vlincta is an all in one AI platform for small and medium businesses that reduces large language model token costs by 30 to 50 percent, depending on the workload. The saving is engineered, not taken out of the answer. Three systems do the work: semantic caching reuses results the platform has already produced for an equivalent task; context compression sends a model only the facts, constraints and evidence the task needs instead of the whole history; and routing gives each request to the cheapest model that can still hold the quality the task requires. It works the same whether a team brings seat plans or its own API keys: the same models at the same output quality for roughly 35 percent less, and up to 20 percent better on narrow, repetitive work where a specialised model beats a general one. Chat, company knowledge, files, voice and workflows sit in one interface, and every request is processed on EU infrastructure under GDPR.

It is used to answer you, and for nothing else. We do not sell your data, we do not pass it to advertisers or data brokers, and we never train a shared model on it — not ours, not a partner's. Your account, your chats and your files are hosted in France, inside the EU, and the open-weight models we serve run on French infrastructure too. You stay the controller: we process strictly on your instructions under a GDPR data processing agreement, you can export or delete anything at any time, and the privacy notice names every provider and storage location involved. If you bring your own frontier plan, that provider's terms apply to those requests and we show you which request went where.

Exactly as long as you want. Your conversations live in your account, in France, so you can pick them back up, and you can delete any of them — or your whole history — at any moment. Delete a chat and it is gone for good: no shadow copy, no backup that quietly survives it, no archive we keep for training.

Not by default, and never on shared data. Nothing you send is used to train a shared model — not ours, not a partner's — and we do not hand your prompts, answers or files to a third party to train on either. That is a contractual commitment in our processing agreement, not a setting buried in a preferences page. The open-weight models we serve are already trained; running them on your input teaches them nothing. Training is opt-in per workspace and stays off until you turn it on: a workspace that opts in gets a private model trained or distilled on its own prompts and workflow, serving that workspace alone, with personal data filtered out first and every version published to your registry with a price and a snapshot date.

All of them — that is the point. The frontier, from GPT-6 Astra and Fable 5.1 to the other leading proprietary assistants, sits next to the whole open-weight field: Kimi, GLM, Qwen, Mistral, Llama, gpt-oss and the rest. Frontier models come in through the plan you already pay for and move onto Vlincta; the open-weight roster we serve ourselves in France, currently GLM 5.2, Qwen3.5 397B-A17B, Qwen3.6 35B-A3B, Qwen3 235B Instruct, Mistral Medium 3.5, gpt-oss 120b, Llama 3.3 70B Instruct, Gemma 4 26B-A4B, plus the enabled image models. When a new model ships, we build it in straight away, so you are never waiting on a release cycle. Concrete models stay directly selectable; Auto stays disabled until its routing targets have completed attestation.

We take a commission on the money our systems save you, and nothing else. No seat price, no subscription, no minimum. You bring the frontier plan you already pay for onto the Vlincta platform, we move everything that does not need a frontier model onto cheaper models, and you pay us a share only of the spend we actually removed — after it is saved. No savings, no invoice. If you have no frontier plan at all, we offer a mixed plan with every model included; its price moves with your usage and stays below what any other LLM provider charges for the same work.

Yes, and nobody has to migrate. Vlincta is not a competitor to Claude — it is the layer underneath it. You transfer your existing Claude plan onto the Vlincta platform and your team keeps working with Claude exactly as before. Behind it, Vlincta sends routine work to cheaper open models and pulls in additional models for planning, execution and review when a task is genuinely hard. Same assistant, same habits, a much smaller bill — and you only pay us a share of what we save.

No — you bring it with you. Move your existing ChatGPT plan onto the Vlincta platform and everyone carries on with the assistant they already know. Vlincta decides underneath which model each task deserves: routine drafting, extraction and formatting go to cheap open models, hard work still goes to the frontier. Nothing to relearn, no migration project, and our commission comes only out of the spend we take off your bill.

Because Langdock is a place to work and Vlincta is what makes that work cheap. Your Langdock plan moves onto the Vlincta platform, the interface and workflows your team is used to stay where they are, and Vlincta routes each request to the model that can actually handle it — open-weight where that is enough, frontier where it is not. You do not choose between the two; you run one on top of the other and pay us only from the savings.

Yes. Bring your Cursor plan onto the Vlincta platform and your engineers keep their editor, their agents and their habits. Vlincta shifts the bulk of the tokens — boilerplate, refactors, test scaffolding, log reading — onto cheaper models, and keeps the frontier for architecture, tricky debugging and review, where it earns its price. Coding is the workload with the most routine volume in it, so it is usually where the saving is largest.

Bring your frontier plan onto Vlincta and keep working as you do today — that is the whole setup. From there two things open up: the workspace for chat, files, company knowledge, images and the enabled voice features, and Vlincta's routing underneath it, which decides per task whether a cheap open model, a strong open model or your frontier model should answer. If you would rather keep your assistant in the lead, you can also connect Vlincta to it over MCP. Availability depends on the features enabled for your workspace.

Vlincta keeps difficult planning and judgment with the frontier model and routes bounded execution to lower-cost open-weight models. A task only moves when the cheaper model holds the same output quality on it — that check is the product, not a rounding we ask you to accept — and because we are paid only out of the spend we remove, we carry the downside when a routing decision turns out to be wrong. Real savings depend on your workload, your provider's limits and how much of your work is genuinely routine, so we measure it on your own tasks before anything is signed.

It does not have to, and usually should not. The point is to keep the assistant your team likes and take the cost out from underneath it: move the plan onto Vlincta, let routine work run on cheaper models, and drop to a lower tier only once your own numbers say you can. Validate that on your own tasks first — provider limits and routing suitability vary.

Vlincta is built in Berlin and hosted in France. The open-weight models we serve run on Scaleway's Paris infrastructure, so that processing stays in France. Your account, your conversations and your files stay on EU infrastructure under a GDPR data processing agreement, with deletion on demand, full provider disclosure and no training unless your workspace opts in. Those are technical and contractual measures; the final legal assessment still depends on your organisation's purpose, contracts, configuration and compliance review.

The open field has closed most of the gap: Kimi, GLM, Qwen, Mistral, Llama and gpt-oss now handle the majority of day-to-day work at a fraction of frontier cost, which is exactly why routing pays. Auto is temporarily disabled pending model attestation. The concrete choices in this deployment are GLM 5.2, Qwen3.5 397B-A17B, Qwen3.6 35B-A3B, Qwen3 235B Instruct, Mistral Medium 3.5, gpt-oss 120b, Llama 3.3 70B Instruct, Gemma 4 26B-A4B, and new releases are added as they ship. Enabled image models handle image generation.

No. There is no key to manage, no model to deploy and no infrastructure to run. Create an account, sign in, connect the plan you already pay for and pick a model from the list. Auto becomes available once its targets pass attestation.

Create account · Log in

Yes. You can connect MCP servers and custom tools to give the models real actions — search the web, read a repo, hit your own APIs — so a chat can actually do things instead of only talking about them.