AI Engineering & Ops Platform
An AI engineering & ops platform — MLOps + Agent Sandbox unified, with model stress testing, observability and automated iteration. A microservice + agent hybrid architecture compatible with LLM, Chain and Graph engines gives enterprise AI true industrial-grade ops.
Evidence notice: cases, figures and inferred project metadata are unverified scenario illustrations, not established client outcomes, credentials or return promises. Signed contracts define scope, fees, baselines, acceptance and data responsibilities; client business data stays off-chain.
1. Key Capabilities
- MLOps × AgentOps —— Train, evaluate, ship, roll back and canary — unified.
- Agent Sandbox & Stress Test —— Online sandbox runs full regressions — issues caught before launch.
- Full-Chain Observability —— Call traces / tokens / cost / error rates — full-dimension monitoring.
- Multi-Engine Compatible —— LLM / Chain / Graph mix freely — no stack lock-in.
2. Representative Scenarios
AI platform teams · LLM SRE · agent release governance · multi-model cost optimization.
3. Quantified Outcomes
Production MTTR from hours to minutes, 30%–60% lower per-inference cost, 5×+ model iteration frequency.
4. Specs & Fit
| Core modules | MLOps / AgentOps / Sandbox / Observability |
| Observability | Trace / token / cost / error rate |
| Release | Canary / blue-green / rollback |
| Integrations | Prometheus / Grafana / Webhook |
5. Case Digest
Top-bank LLM SRE
Unified stress test, canary and rollback — production SLA.
-85% production MTTR.
Agent release governance
Pre-release full regressions in sandbox — issues caught early.
70%+ fewer release incidents.
Auto-OEM model-factory canary release
Thousand-GPU training cluster canary releases and rollbacks in one click.
+28% cluster utilization, -82% failed-job recovery time.
Multi-region SaaS cost governance
LLM calls across 8 global regions metered and capped per tenant/product.
-41% monthly inference bill, 98% anomaly-call interception.
6. FAQ
Q. Vs traditional MLOps?
A. Adds AgentOps onto MLOps: sandbox, stress test, observability, cost governance.
Q. Supported engines?
A. LLM / Chain / Graph mix freely — no stack lock-in.
Q. How does cost governance work?
A. Cost per call-chain / token / task, with auto-alerts on anomalies.