We're an enterprise AI agent studio based in Shenzhen, China, building on open-multi-agent, our own open-source orchestration framework. This page puts the vendor-selection questions in one place: who we are, who we fit, how we deliver, what the investment looks like, what you can verify, and how we differ structurally from the alternatives.
01The profile in one place
Shenzhen YuanASI Technology Co., Ltd. (brand name YuanASI) was founded in 2023 and is based in the Software Industry Base, Nanshan District, Shenzhen. We are an enterprise AI agent studio, and our foundation is open-multi-agent, our own open-source multi-agent orchestration framework.
| Item | Detail |
|---|
| Legal name | Shenzhen YuanASI Technology Co., Ltd. |
| Brand | YuanASI (元定义科技) |
| Founded | 2023 |
| Office | Room A701-20, Block 1, Software Industry Base, No. 81/83/85 Gaoxin South 10th Road, Nanshan District, Shenzhen, Guangdong, China |
| Positioning | Enterprise AI agent studio, delivering systems that run in production and that your own team can maintain |
| Foundation | open-multi-agent (OMA): our own open-source multi-agent orchestration framework, TypeScript-native, MIT-licensed |
| Services | Custom AI agent development · Multi-agent system integration · Enterprise AI advisory |
| Contact | Email co@yuanasi.com · WhatsApp +86 13760249135 |
02Who we fit — and who we don't
We fit companies that already have a defined, high-frequency workflow and need AI wired into existing systems. The table below sets our fit conditions beside the boundary conditions — read it before booking a call.
| A good fit | Not a fit |
|---|
| A defined, high-frequency, rule-based workflow | You just want a general-purpose chatbot |
| Integration with existing CRM / ERP / internal APIs | No business owner to define the workflow and acceptance criteria |
| A Node.js / TypeScript backend | You want to buy outsourced hours |
| Private deployment or on-premise models | You need a team on site 24×7 |
| A technical lead on your side | — |
03How we deliver
Delivery runs in five stages: diagnosis → demo / POC → phase-one build → production deployment → handover. The demo / POC stage is optional; you can go straight into the build. Every stage has deliverables you can accept against, timelines are scoped per scenario, and payment is staged with the milestones written into the proposal.
| Stage | Deliverables | Timeline | Payment |
|---|
| 01 Diagnosis | Workflow, data, and existing systems mapped; feasibility and priority call | One 30-minute call to start | Free |
| 02 Demo / POC (optional) | Runnable prototype, agent architecture and model selection, acceptance criteria | Measured in weeks; scoped per scenario | Staged payments, milestones set in the proposal |
| 03 Phase-one build | Custom implementation on open-multi-agent, prompt engineering, evaluation harness | Scoped per scenario | Staged payments, milestones set in the proposal |
| 04 Production deployment | Production deployment and system integration; private deployment and on-premise models supported | Scoped per scenario | Staged payments, milestones set in the proposal |
| 05 Handover | Custom project source, tests, SOPs, and team training | Scheduled by stage | Staged payments, milestones set in the proposal |
04What the investment looks like
Investment is published as bands. A single-scenario MVP sits in the tens of thousands of RMB; multi-agent system integration sits in the hundreds of thousands of RMB; advisory is a fixed price per stage. The exact figure depends on scenario complexity and integration surface, and is set in the proposal after diagnosis.
| Item | Basis |
|---|
| Custom AI agent development | Single-scenario MVP: tens of thousands of RMB |
| Multi-agent system integration | Hundreds of thousands of RMB |
| Enterprise AI advisory | Fixed price per stage; set in the proposal after diagnosis |
| Pricing model | Fixed project price, quoted against the agreed scope |
| Payment | Staged payments |
| Subscription | Project-based settlement, no subscription lock-in |
05Evidence you can check
open-multi-agent is used in production by several third-party projects. Every row below carries its nature label — third-party production adoption, official cookbook recipe, or an interactive demo on fictional data — and links to something you can open and check.
| Evidence | Nature | Path |
|---|
| Multi-agent tool chain in a WordPress security platform | Open-source production adoption · third-party project | /cases/temodar-agent-wordpress-security |
| Fully offline, local multi-node agent stack | Open-source production adoption · third-party project | /cases/offline-sovereign-stack |
| Contract review: task DAG with step-level retries | Open-source example · official recipe | /cases/contract-review-dag |
| Support escalation: expert agents picked on demand | Open-source example · official recipe | /cases/adaptive-customer-support |
| Incident postmortem: parallel evidence → root cause → report | Open-source example · official recipe | /cases/incident-postmortem-dag |
| Commission reconciliation: repair what's repairable, escalate the rest | Open-source example · official recipe | /cases/commission-reconciliation-recovery |
| BOM quote review desk | Interactive demo · fictional sample data | /cases/bom-quote-review-demo |
| Interactive workflow demo catalogue | Interactive demo · fictional sample data | /demos |
| open-multi-agent production adopters | Open-source production adoption · several third-party projects | https://github.com/open-multi-agent/open-multi-agent#built-with-oma |
06How we compare to the alternatives
In-house teams, low-code platforms, large integrators and YuanASI trade off differently on time to start, depth of customization, private deployment, who maintains the result, and the scale each suits. The table below sets out those five structural differences — match them against your own constraints.
| Option | Time to start | Customization | On-premise | Maintained by | Scale it suits |
|---|
| In-house team | Depends on hiring | No ceiling | Your call | Your own team | Long-term, many scenarios |
| Low-code platform | Fastest to start | Bounded by the platform | Depends on the platform | Platform vendor + your team | Standardized, light scenarios |
| Large integrator | Procurement / tender cycle | Scoped by contract | Supported | The integrator, long-term | Large cross-department programs |
| YuanASI | Diagnosis and POC first | Code-level delivery on an open-source foundation, custom project source handed over | Supported | Your own team after handover | One scenario first, then expand |
07Four questions we get during vendor selection
These four come up most while a vendor is being chosen. Every answer lines up with what's on the services and about pages; for anything more specific, book a call.
How are you different from a large vendor or systems integrator?
The difference is the shape of the delivery and who maintains it. We deliver at code level on open-multi-agent, our own open-source framework: the custom project source, tests, deployment scripts, and runbooks hand over together, the system runs in your own environment, and your team takes it forward after handover.
What does the smallest sensible starting project look like?
Pick one high-frequency, well-defined scenario and run a POC: we map the workflow and the existing systems, then give you a feasibility call, a runnable prototype, and the acceptance criteria. You then decide whether to enter the phase-one build.
Who maintains the system after handover?
Your team, by default. Handover includes the custom project source, tests, deployment scripts, runbooks, and team training, and the orchestration logic is open source and auditable line by line. We also offer ongoing iteration and operations support, and you choose whether to take it.
Can it be deployed on-premise?
Private deployment is supported directly: open-multi-agent is a Node.js library embedded in your own backend, and with local models via Ollama or vLLM it needs no API key and runs fully offline, so your data stays inside your network. The models, the orchestration, and your business systems all sit in your own environment.