Enterprise LLMOps Platform
DenseMAX Studio integrates state-of-the-art language models with secure deployment, fine-tuning, evaluation, safeguarding, and optimization—so teams can ship reliable generative AI at scale.
Available on DenseMAX Appliances and major clouds: AWS, GCP, Azure, Oracle Cloud.
An enterprise-grade LLMOps platform to accelerate the development and deployment of generative AI applications. It unifies deployment, fine-tuning, evaluation, safeguarding, and optimization, streamlining the journey from experimentation to large-scale adoption.
Available on DenseMAX appliances or leading clouds: AWS, GCP, Azure, Oracle Cloud.
KV-aware routing, GPU sharding, replicas, and disaggregated serving for production-grade performance.
Kubernetes-native scaling
Git-like Data Hub for models & datasets
LoRA & full fine-tuning pipelines
RL alignment (DPO, PPO, GRPO)
Guardrails with RBAC & observability
Quantization for NVIDIA GPUs
Start from templates or deploy custom apps via an intuitive UI. Go from POC to production in days.
RBAC, audit trails, guardrails, and full‑stack observability keep systems secure and compliant.
KV caches, GPU sharding, replicas, and quantization maximize throughput while controlling cost.
Scale across multi-node clusters with reliability and elasticity for demanding inference and training workloads.
Launch from templates or deploy custom apps through a simple UI with CI-friendly APIs.
Role-based access control, built-in guardrails, metrics, traces, and logs for end-to-end visibility.
Git-like branching, tagging, PRs, diffs, and interactive viewers. Explore & transform datasets, import/export from/to HuggingFace and ModelScope.
Production-ready pipelines for LoRA and full FT, synthetic data gen, reward models, and embeddings.
Apply DPO, PPO, and GRPO to build safer, more aligned AI systems tailored to enterprise policies.
Train smaller, faster, cost‑efficient models optimized for specific use cases and SLAs.
Benchmark with MMLU, ARC, GSM8k, TruthfulQA, HellaSwag, and more. Red-team for toxicity, bias, misinformation, PII, and harms.
Optimize models for specific NVIDIA GPU architectures to improve throughput and lower latency.
Deploy domain‑aware copilots for support, sales, and operations with safe, monitored outputs.
Ground responses on your knowledge base with evaluation loops that track accuracy and drift.
Generate, classify, and moderate content at scale with auditable policies and human‑in‑the‑loop.
Enforce RBAC, PII safeguards, and traceability for finance, healthcare, and public sector.
Run workloads where they fit best—on‑prem clusters or your preferred cloud regions.
Distill and quantize to meet strict SLAs while reducing inference spend.
Benchmark models with standard datasets and perform targeted red‑teaming to uncover failure modes early. Close the loop with metrics that track quality, safety, and business outcomes over time.
MMLU, ARC, GSM8k, TruthfulQA, HellaSwag, etc.
Toxicity, Bias, Misinformation, PII leakage, etc.
Continuous evaluation pipelines
Answer Accuracy
Toxicity Rate
Latency (P95)
Cost / 1k tokens
Tell us about your use case and infrastructure preferences. We'll share a tailored walkthrough of DenseMAX Studio.
Deploy on AWS, GCP, Azure, Oracle Cloud, or on‑prem
Enterprise security, RBAC, and auditability
Optimized for NVIDIA GPU architectures
This form is a static demo. Connect to your backend or form service to enable submissions.
Request a guided demo or talk to our team about configurations, pricing, and delivery.
Enterprise AI that delivers: secure, sovereign-by-design, predictable, at scale. From POC to production with DenseMAX appliances, Digital Automation, and Surogate Agents.
