Who this is for
Companies with data-residency, IP, or regulatory constraints
Build internal AI copilots, doc-search, and developer tooling on models that keep your data under control — via private API routing, local inference, or enterprise-scoped providers.
Outcomes
- Internal AI tooling without sending source code or customer data to public APIs
- Mix of local inference (Mistral, Llama) and scoped enterprise Claude/GPT where appropriate
- Auditable logs and retention policies built in
When you actually need this
If your data is covered by GDPR, HIPAA, financial regulations, or strong IP contracts, sending it through a public API is often a non-starter. Private toolchains keep the hot path inside your perimeter and only hand off to external providers when the task clearly justifies it and the data is allowed to leave.
The architecture
Typical stack: a local inference layer (Mistral, Llama, or a fine-tuned open model) on your own GPU box or a provider's VPC, wrapped in the WolfAI router. Requests classified as 'safe to send out' escalate to Claude or GPT with data-processing agreements in place. Everything is logged for audit.
What this is not
This is not a drop-in replacement for ChatGPT Enterprise or Claude for Work, though it can sit alongside them. It is the answer when those products do not fit your data policy or when you want a custom routing strategy across public and private models.
Related products
Models typically involved
Frequently asked questions
Can I use private AI without sending data to OpenAI or Anthropic?
Yes — you can run local models (Mistral, Llama, or fine-tuned open models) on your own infrastructure and only escalate to public providers for tasks where the data classification allows it. WolfAI builds the routing layer that makes this practical.
Is local inference fast enough for real users?
For most internal tools, yes. A well-tuned local 7–70B model on modern hardware answers in 1–3 seconds for typical prompts. For the hardest reasoning, the router escalates to a hosted model.