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.
Key Capabilities
Four key capabilities required to put this product into production.
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.
Representative Scenarios
AI platform teams · LLM SRE · agent release governance · multi-model cost optimization.
Download Solution Playbook
Generate a printable white paper for this module
Opens a playbook page with key capabilities, scenarios, outcomes, specs and case digest — use the browser's Save as PDF to distribute.
Case Studies
Filter anonymous scenario illustrations by industry, scenario and scale. Metrics and inferred customer, duration, stack and deployment metadata are unverified, not established customer outcomes.
Finance
1 caseIllustration · metrics unverified
Top-bank LLM SRE
Unified stress test, canary and rollback — production SLA.
- Customer
- Top-tier Banks / brokers / insurers
- Duration
- 8–12 weeks
- Tech stack
- Multimodal backbone
- Deployment
- Private deployment
Government
2 casesIllustration · metrics unverified
Agent release governance
Pre-release full regressions in sandbox — issues caught early.
- Customer
- Top-tier Government & public sector
- Duration
- 8–12 weeks
- Tech stack
- Multi-agent
- Deployment
- Private deployment
Illustration · metrics unverified
Multi-region SaaS cost governance
LLM calls across 8 global regions metered and capped per tenant/product.
- Customer
- Multinational Government & public sector
- Duration
- 12–20 weeks
- Tech stack
- Speech / ASR-TTS
- Deployment
- Private deployment
Manufacturing
1 caseIllustration · metrics unverified
Auto-OEM model-factory canary release
Thousand-GPU training cluster canary releases and rollbacks in one click.
- Customer
- Top-tier Process / discrete manufacturers
- Duration
- 8–12 weeks
- Tech stack
- Multimodal backbone
- Deployment
- Edge / on-prem
Specs & Fit
Core modules | MLOps / AgentOps / Sandbox / Observability |
Observability | Trace / token / cost / error rate |
Release | Canary / blue-green / rollback |
Integrations | Prometheus / Grafana / Webhook |
FAQ
Vs traditional MLOps?
Adds AgentOps onto MLOps: sandbox, stress test, observability, cost governance.
Supported engines?
LLM / Chain / Graph mix freely — no stack lock-in.
How does cost governance work?
Cost per call-chain / token / task, with auto-alerts on anomalies.
Quantified Outcomes
We only ship products that deliver ROI — here are the measurable metrics after enterprise rollout.
Production MTTR from hours to minutes, 30%–60% lower per-inference cost, 5×+ model iteration frequency.
Defined scope. Accountable delivery.
Staged pricing & acceptance
Discovery, pilot and rollout have separate scope and fees. Contracts define baselines, evaluation samples, acceptance metrics and responsibility. Results are not unconditionally guaranteed.
Customer business data stays off-chain
Business records and personal data are not written to a blockchain. Confirm deployment region, model providers, access, retention and deletion in writing before work begins.
Clear contracting & delivery responsibility
Before signing, obtain and verify the legal entity, registration, jurisdiction, delivery owner and dispute terms. DAO collaboration does not replace contractual responsibility.