Private AI is becoming an enterprise control surface.
The next wave is not only larger frontier models. It is local coding agents, Chinese open models, private inference, model routing and QAG escalation for workflows where a probabilistic answer is not enough.
Open models are now part of the enterprise architecture.
- Qwen and Qwen-Coder for strong open coding and reasoning baselines
- DeepSeek, Kimi and GLM for high-value Chinese-model evaluation lanes
- Llama, Mistral and Phi-style small models where latency and ownership matter
- Domain-specific adapters and small specialists for regulated workflows
QGI position
QGI does not sell generic chatbot substitution. We design the routing layer: when local AI is good enough, when a frontier model is worth the cost, and when the workflow must move into Q-Prime and QAG Engine because the answer needs a replayable decision graph.
See QAG EngineLocal AI needs governance before scale.
Data boundary
Classify code, customer records, contracts, lab data and regulated evidence before routing. Private data stays local, VPC or on-premise unless a named exception is approved.
Model routing
Choose local, open, managed frontier or QAG paths by risk class. The same workflow can use a local coding model for refactors and QAG for signable decisions.
Evidence capture
Keep prompts, retrieved context, tool calls, tests, review notes and decision traces. Local AI is valuable only when the output can be reproduced and defended.
Cost control
Move high-volume, low-risk work to local and open models. Reserve expensive frontier inference for tasks where it changes the economics or quality ceiling.
Build a private AI map before your teams improvise one.
QGI can produce the model-routing policy, local agent setup, GitHub workflow, evaluation harness and QAG escalation path for one production workflow.
Request local AI strategy