Private AI engineering, end to end
Four service lines that cover the full path from model selection to production agents — optimized for infrastructure you control.
TensorWard Optimize
Hardware-aware model selection, quantization, compression, and quality validation so large models run within your VRAM, latency, and quality envelope — without guessing.
EXPLORE OPTIMIZE →- Hardware-aware model selection
- Model quantization (GGUF, AWQ/GPTQ-style where appropriate)
- Extreme / custom quantization strategies
- FP8 / NVFP4 / low-bit deployment strategy where hardware supports it
- Model compression & conversion
- LoRA / QLoRA strategy and fine-tuning guidance
TensorWard Runtime
On-prem, private-cloud, hybrid, and air-gapped inference platforms engineered for production — not a weekend install of a single engine.
DISCUSS RUNTIME →- On-prem, private-cloud, hybrid, and air-gapped deployments
- GPU architecture strategy (single-GPU through multi-node)
- vLLM, llama.cpp, SGLang, TensorRT-LLM where appropriate
- OpenAI-compatible serving
- Model routing and load balancing
- Speculative decoding and continuous batching
TensorWard Agents
Private agents, internal copilots, and tool-enabled workflows that sit on your inference layer with permissions, human approval, logging, and evaluation built in.
DISCUSS AGENTS →- Private agents and internal copilots
- Tool-enabled agents and MCP integrations
- Business workflow and API integrations
- Internal knowledge access with controlled scope
- Agent permissions and human approval workflows
- Agent evaluation frameworks
TensorWard Advisory / Care
Readiness assessments, architecture reviews, hardware strategy, cost/performance analysis, and ongoing Care so private AI systems stay current, fast, and fit for purpose.
DISCUSS ADVISORY / CARE →- Private AI readiness assessments
- Architecture and deployment reviews
- Hardware strategy and GPU procurement guidance
- Model selection and cost/performance analysis
- Security architecture reviews
- Performance reviews and benchmarking