Orchestrate every channel
from one intelligent
layer.
In 30 minutes we'll show you how Twinny connects to your operation and starts delivering results from week 1.
Local AI: models in your infrastructure, under your control.
We work on local AI implementations for enterprises: models deployed on your servers or in your private cloud, with no per-token cost to third parties, with data that never leaves your perimeter and with the same AI Operating Layer as in the cloud.
Definition
Who it is for
- CIO / CTO
- CISO and security
- CFO
- Regulated sectors
- Infrastructure
Local AI. Local AI (on-premise or private cloud) is the deployment of language models and other AI components inside the company’s own infrastructure, instead of consuming them as an external provider’s service. It saves per-use cost when volume is high, controls where data resides and how it is processed, and meets security and data-residency policies that do not allow sending data to third parties. Twinny designs, deploys and operates that infrastructure and connects it to the Company Memory, MCP servers and digital workers.
Why local AI: cost, security and infrastructure
- Cost savings: with high volumes of conversations and documents, external providers’ per-token cost exceeds the cost of running your own models. The investment becomes a predictable cost.
- Security control: customer data, records or contracts never leave the company perimeter; not for inference, not for training.
- Infrastructure control: you decide where the model runs, with what capacity, which versions and which update policy.
- Compliance: data residency, regulated sectors, contracts that forbid third parties and auditable EU AI Act requirements.
- Latency and availability: models close to the systems that use them, without depending on a provider’s quotas or outages.
What we deploy
- Open models Open-weight language and voice models, sized for your load and tuned on your data where applicable.
- Inference and orchestration Inference servers, queues, cache and balancing to operate at volume with controlled cost.
- Local Company Memory Vector database, data model and connectors inside your network.
- MCP and digital workers The same capabilities and agents as in the cloud, pointing to your models.
- Observability and governance Per-decision traceability, permissions, cost and quality metrics.
Local, cloud or hybrid: choose per process
No need to choose for the whole company: each process can run wherever it makes most sense, on the same AI Operating Layer.
| Twinny cloud (Europe) | Local AI in your infrastructure | Hybrid | |
|---|---|---|---|
| Cost | Per use, no upfront investment | Upfront investment and predictable cost at volume | Local for bulk, cloud for peaks |
| Data | European territory, isolated per customer | Never leaves your perimeter | Sensitive data local, the rest in the cloud |
| Time to go live | Days | Weeks | Weeks |
| Who operates | Twinny | Twinny with you, or your team | Shared |
When it makes sense
- Thousands of conversations or documents a day with a growing per-token cost.
- Clinical, financial, legal or HR data that cannot leave the organisation.
- Legacy systems and databases only reachable from the internal network.
- Contracts or audits that require full control of the infrastructure.
Frequently asked questions
- What is local AI?
- Deploying language models and AI components inside the company’s infrastructure (on-premise or private cloud) instead of consuming them as an external service, to control cost, data and infrastructure.
- When does it save costs?
- When volume is high and sustained: the external per-token cost is replaced by your own infrastructure with a predictable cost. We calculate it per process before proposing it.
- Are local models good enough?
- For most enterprise processes (classify, extract, answer with context, execute) yes, especially with the Company Memory and business rules. For specific cases we combine local and cloud models.
- Who operates it?
- Twinny deploys it and can operate it with you or hand it over to your team, with observability, updates and governance.
- Does anything change for digital workers?
- No. The AI Operating Layer, Company Memory and MCPs are the same; only where the model runs changes.
Start with one process.
Tell us what you want to automate and you will receive an initial proposal within 24 hours.