What runs on ARCKONE inference
AI video generation, image creation and editing, language models for extraction, drafting and automation, transcription and speech synthesis. Building blocks we use in our prototypes and our own production workflows.
The right capacity, with a clear boundary.
Video, images, language models and transcription each need different infrastructure. We define where processing runs, which providers are involved, what is logged and how long data is retained.
What you get
- AI video and image generation for your content and products
- Language models (LLMs) for your documents, texts and automations
- Dedicated or on-demand capacity according to volume and latency
- Subprocessors, telemetry and storage documented per project
- Retention and deletion defined and then verified
Capacity matched to the workload
Depending on volume, latency and the selected tools, compute may be dedicated, shared or started on demand. Scoping compares operating cost, start-up time and availability instead of claiming one architecture fits every workload.
Energy and hardware assessed in context
Inference uses electricity and hardware. Provider, region, utilisation and server lifetime all affect impact. We therefore avoid a blanket environmental promise and document the available evidence for the selected architecture.
A lifecycle defined before processing
Files, prompts, outputs, logs and backups may each have a different retention period. Before a project, we specify third-party services, use for training, support access and deletion rules. Their implementation must be verifiable.
We scope it in 20 minutes.
One call is enough to know whether the topic deserves a real project.
Discuss a use case