Deploy sentiment analysis, churn models, moderation, embeddings, and RAG entirely on your own infrastructure. No data ever sent to external APIs. Lower long-term cost than cloud inference at scale.
We pick the best-fit components based on your data volume, latency needs, and privacy requirements.
Depends on model size. A 7B-parameter LLM runs on a single mid-range GPU (RTX 4090 or A10G). Larger models (70B+) need multi-GPU setups. For classification models (sentiment, churn) a CPU-only instance is often enough. We'll size hardware in discovery.
Any open-weight model: Llama, Mistral, Qwen, Phi, Gemma, embedding models (BGE, E5), and custom fine-tuned models. We can also deploy proprietary classifiers we build for you (churn, moderation, etc.).
We manage model updates. New base models get evaluated against your workload before rollout, then deployed with zero downtime via blue-green or canary strategy.
Yes. Common pattern: on-prem for PII-heavy workflows, cloud (OpenAI/Anthropic) for reasoning-heavy tasks with no sensitive data. We build a router that decides per request.
Yes. We offer monitoring, drift detection, and retainer for tuning. Alternatively we can hand over full ops to your team with runbooks and Kubernetes charts.
The RAG stack running on your hardware instead of external APIs.
Explore →Vision and NLP moderation with no content leaving your infrastructure.
Explore →Run your churn model on-prem for customer-data sovereignty.
Explore →Tell us the workflow your legal or security team said no to. We'll show you the on-prem architecture that would have gotten it approved.
