ENTERPRISE AGENTOPS PLATFORM
Surogate is the full-stackplatform to design autonomous AI agents, deploy them on your infrastructure,and continuously improve them from production data — without stitching together multiple tools.
Available on DenseMAX Appliances and major clouds: AWS, GCP, Azure, Oracle Cloud.
Most organizations have noshortage of AI ideas. The hard part is making them work reliably — at scale, onreal data, with real consequences when something goes wrong.
Surogate is the platform thattakes you from an AI concept to a reliable, autonomous AI system running yourbusiness workflows.
It manages the entire agentlifecycle: design, deployment, observability, and continuous improvement. Everything runs on your own infrastructure — your servers, your cloud, yourdata.
Runs on Kubernetes, on your on-premise serversor any major cloud. Your data never leaves your perimeter. Full air-gapdeployment available for regulated industries.
Built from the ground up for agentic workloads —not just model serving. Agents that reason, use tools, coordinate with eachother, and execute multi-step workflows reliably.
Autonomous agent runtime withskills, tools, and sub-agents
Complete execution traces andanomaly alerts
Built-in model training andfine-tuning engine
Continuous agent improvement loopfrom production data
Git-style Data Hub for models,datasets, and agent definitions
Role-based access control,guardrails, and audit logs
Available on DenseMAX Appliancesor AWS, GCP, Azure, Oracle Cloud
A demo agent and a production agent are different problems. Surogate gives you the observability, versioning, and safeguards to run agents reliably — not just impressively.
Every LLM call, every tool invocation, everysub-agent handoff, every failure — logged and inspectable. Know what youragents are doing, always.
Production traces feed back into training. Each improvement cycle produces faster, more accurate specialized models. Youragents evolve — automatically.
01
Design agents from modular building blocks:skills that define what agents know how to do, tools that connect them to external systems and APIs, and models trained or imported into the platform.Your team's experts define skills in plain language — no ML backgroundrequired.
02
Ship agents as containerized applications on Kubernetes. Configure skills, tools, and models per deployment. Autoscaling,resource allocation, and version-controlled agent artifacts ensure consistent,repeatable rollouts on your infrastructure.
03
Every agent run generates a full execution trace— every decision, every tool call, every sub-agent step, every error. Visual trace viewer, session replay, anomaly alerts, and performance dashboards giveyour team complete visibility into production behavior.
04
Traces from production become training data. The platform fine-tunes smaller, faster Specialized Language Models (SLMs) on youragents' actual workflows. Improved models are evaluated, approved, and promoted back into production. The longer your agents run, the better they get.
Agents that reason, plan, and execute. Composeagents from skills, tools, MCP integrations, and sub-agents. Hierarchical architectures for complex enterprise workflows.
Skills are the building blocks of agent capability — precisely defined tasks with clear inputs, outputs, and success criteria. Domain experts define them. The platform builds them.
Full execution traces for every agent run.Visual trace viewer, step-by-step session replay, anomaly detection, and operational dashboards. You always know what your agents are doing.Role-based access control, built-in guardrails, metrics, traces, and logs for end-to-end visibility.
Production traces become training data. Training data produces better Specialized Language Models. Better models go back intoproduction. Agents improve automatically over time.
Train and fine-tune models on your data, foryour workflows — using LoRA, QLoRA, or full fine-tuning. Reinforcement learning(GRPO, DPO, PPO) for alignment. Native C++/CUDA engine for maximum GPU efficiency.
A central, versioned registry for all AI assets:models, datasets, agent definitions, skills, and tools. Git-style branches,commits, and tags. Single source of truth across the entire platform.Apply DPO, PPO, and GRPO to build safer, more aligned AI systems tailored to enterprise policies.
Role-based access control, project isolation,agent guardrails, content filters, and automated red-teaming. Full audit logs.Budget caps and per-team cost tracking. Compliance-ready by design.
GPU-accelerated inference with KV-cacheoptimization, tensor parallelism, and multi-batch support. Models served at thespeed your agents need.
On-premise with DenseMAX Appliances, or on AWS,GCP, Azure, or Oracle Cloud — in your own VPC. Hybrid deployments supported.Air-gap mode for fully isolated environments.
Deploy agents that execute end-to-end businessworkflows — document processing, data extraction, routing, CRM updates,compliance reporting — with full auditability.
Specialized agents for legal, finance,healthcare, and engineering. Trained on your domain's data, terminology, andcompliance requirements.
Embed intelligent agents directly into yourenterprise applications — for support, sales assistance, onboarding, and more.
Run inference and fine-tuning on data thatcannot leave your infrastructure. On-premise deployment with full air-gapcapability.
Serve legal, HR, marketing, finance, andoperations with isolated, parallel agent deployments — managed centrally fromone platform.
Meet the audit, traceability, and datagovernance requirements of financial services, healthcare, and the publicsector — built into the platform, not bolted on.
Every agent and model deployedthrough Surogate is tested before it goes live — and monitored continuously inproduction. Standard benchmarks establish the baseline. Custom evaluationsmeasure what actually matters for your business.
Standard benchmarks: MMLU, ARC, GSM8k, TruthfulQA, HellaSwag, HumanEval, and more
Safety and security: prompt injection, PII leakage,jailbreak, toxicity, bias
Custom domain evaluations: define your own datasets andjudge models
Continuous evaluation pipelines: catchreg ressions before they reach users
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 Surogate.
Deploy on AWS, GCP, Azure, Oracle Cloud, or on‑prem
Enterprise security, RBAC, and auditability
Optimized for NVIDIA GPU architectures
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For engineers and architects
Training engine: native C++/CUDA, designed to reach near hardware limits on modern NVIDIA GPUs
Fine-tuning: LoRA, QLoRA, full fine-tuning — BF16, FP8,NVFP4 mixed-precision
Reinforcement learning: GRPO, DPO, PPO workflows built in
Inference: GPU-accelerated with vLLM, KV-cache offloading,tensor parallelism, LoRA adapter stacking
Deployment: Kubernetes-native, multi-node clusters,autoscaling, containerized agents
Data Hub: Git-style versioning — branches, commits, tags,PRs, diffs — import/export HuggingFace and ModelScope
MCP support: native Model Context Protocol for tool ecosystem integration
Security: RBAC, project isolation, automated red-teaming,content filters, audit logs, SSO
Open-source core available: surogate.ai — pre-training,full fine-tuning, multi-GPU, Blackwell-native
Full technical documentation and open-source repository:surogate.ai
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Tell us about your workflows and infrastructure. We'll design a deployment that fits your team, your data, and your compliance requirements.
Deploy on AWS, GCP, Azure, Oracle Cloud, oron-premise
Enterprise security, RBAC, full audit trail
Agent lifecycle management fromday one
Enterprise AI that delivers: secure, sovereign-by-design, predictable, at scale. From POC to production with DenseMAX appliances, Digital Automation, and Surogate Agents.
