Model Notices

Agentyk is built on open-weight AI. This page explains, at a high level, the foundation models our hosted Service draws on and the open-source licences and attributions that apply. It forms part of, and is incorporated by reference into, our Terms of Service.

1. We build on open-weight models

The language and speech models behind AgentykCloud chat and our APIs (including Agentyk Scribe) are built on, and fine-tuned and optimised from, open-weight foundation models— models whose trained parameters are published under an open or source-available licence. We run them on our own European infrastructure. We do not build the Service on closed, proprietary models accessed over someone else's API, and we do not use your prompts, inputs, or outputs to train models.

2. Which models we use

We continuously evaluate, combine, fine-tune, and rotate the models we run as the open-weight ecosystem advances. Depending on the product, the tier, the region, and the time, our deployments may draw on models from families such as:

• Google Gemma
• Meta Llama
• Alibaba Qwen
• Mistral and Mixtral
• Microsoft Phi
• DeepSeek
• IBM Granite
• TII Falcon
• OpenAI open-weight (gpt-oss) models
• Whisper-family and other open speech-to-text models
• and other open-weight models, including our own fine-tunes of the above

This list is illustrative and non-exhaustive, and the specific models in use change over time. For commercial, security, and operational reasons, we do not disclose which specific model, version, or configuration powers any particular product tier or codename, and a given codename may map to different underlying models over time.

3. How we build and serve our models

We do more than run these models unchanged. To deliver the Service we build derivative and optimised model systems by applying a range of engineering and machine-learning techniques. Depending on the product, the tier, and the time, these may include, among others:

• Fine-tuning and adaptation— supervised fine-tuning, instruction tuning, parameter-efficient methods (such as LoRA and adapters), continued or domain pre-training, and preference or reinforcement-learning alignment (such as RLHF, RLAIF, or DPO).
• Distillation— training smaller or faster models to reproduce the behaviour of larger ones.
• Quantization and pruning— reducing numerical precision or removing redundant parameters to serve models more efficiently.
• Model merging, ensembling, and mixture-of-experts — merging or combining multiple models, and routing or multi-model setups that run several models and return a selected, consensus, or best answer.
• Prompt and system-prompt engineering— instructions, templates, and few-shot examples that shape behaviour.
• Retrieval-augmented generation, knowledge graphs, and search grounding — supplementing a model with retrieved documents, structured knowledge, embeddings, or web and search results.
• Tool use and agentic harnesses— orchestrating multi-step reasoning, tool or function calls, planning, and verification or self-consistency loops.
• Content moderation and safety— classifiers, guardrails, and filtering applied before, during, or after generation.
• Inference and serving optimisation— techniques such as speculative decoding, batching, caching, KV-cache and decoding optimisations, reranking, and constrained or structured decoding.
• Other related techniques— we continuously adopt new methods as the field advances.

The result is a derivative system that may behave differently from, and perform better than, any single underlying model. This description is general, illustrative, and non-binding: the specific techniques and their combination change over time, we apply them differently across products and tiers, and nothing here commits us to using, or continuing to use, any particular technique.

4. Licences and attribution

Each foundation model we use is used under its own open-source or source-available licence (for example the Gemma Terms of Use, the Llama Community Licence, and the Apache 2.0 or MIT licences under which several of the families above are released). We comply with those licences, including any attribution requirements and use restrictions they impose, and we pass through use restrictions to you via our Acceptable Use Policy. Where a licence requires it, this page and our notices serve as the required attribution (for example, products in this Service that are built with Llama are “Built with Llama”). The respective model names and trademarks belong to their owners; their inclusion here is for attribution and does not imply that those owners endorse, sponsor, or are affiliated with Agentyk or Sylvanity B.V.

5. No warranty from upstream

Open-weight models are provided by their authors without warranty. Output generated through the Service is subject to the disclaimers in our Terms of Service — it may be inaccurate and is not professional advice. You remain responsible for reviewing and verifying output before relying on it.

6. Changes

Because we rotate and upgrade models, we update this page from time to time; the “Last updated” date above reflects the latest revision. Questions about model licensing or attribution: info@sylvanity.eu.