Fully offline
Physically isolate sensitive and production-control networks; keep data, inference, and logs local.
Products
One product system across the three critical paths of enterprise agents.
The business workbench enters the task, engineering governance controls delivery, and model distribution provides stable supply within a local-first boundary.
Deployment and access
Local-first, with sensitive information screened at the gate.
Combine deployment modes according to isolation, model source, and data class. Start with one scenario and expand only after the boundary is verified.
Private intranet
Deploy inside the enterprise network with domestic compute, operating systems, databases, and private models where needed.
Hybrid cloud
Keep sensitive data local while calling public capabilities under explicit routing and policy controls.
Dedicated VPC
Use dedicated cloud resources and logical isolation for independent network, permission, and audit boundaries.
Business execution → Engineering delivery → Models & compute
Three layers, one operating loop.
Each product can stand alone or combine around one scenario.
- 01
AoristWorker
Enterprise agent workbench · Make business work executable
Turn business problems into agent tasks, connect knowledge, real tools, and people, and retain successful execution as team capability.
Business problem
AI tools sit outside role workflows, so output still needs manual copying, approval, filing, and follow-up.
Best fit
- Growth, operations, and office teams
- Legal, advisory, and content delivery teams
- Enterprises that need one AI work entry
Delivery artifacts
- Task and workflow templates
- Tool and knowledge connection configuration
- Output and approval records
- Operating guidance
Operating outcomes
- One AI work entry
- Reusable agent templates
- Searchable business data
- Auditable delivery records
Capabilities
Real tool access
Bring browsers, files, documents, sheets, calendars, and business systems into one workbench.
Business task execution
Continuously produce source packs, content, follow-up, reports, and delivery packages.
Agent distillation
Turn expert judgment and repeatable execution into reusable agent templates.
Data and knowledge retention
Make projects, customers, sources, logs, and outcomes searchable business assets.
Team governance and replication
Replicate permissions, audit records, collaboration history, and process templates.
Operating evidence
- Task history
- Human approval records
- Delivery packages
- Source and operation records
Start with
One frequent role workflow with representative inputs, outputs, and approval points.
Governance boundary
Tool access, data scope, and automation permissions are configured against the enterprise system and security policy.
- 02
AGMesh
Engineering governance & delivery · Make agent delivery manageable
Bring goals, tasks, roles, execution, verification, and evidence into one engineering governance chain.
Business problem
Agent work lacks clear scope, verification, and review ownership, so completion claims are hard to check.
Best fit
- AI and software engineering teams
- Multi-agent engineering delivery
- Complex projects that require review and replay
Delivery artifacts
- Project rules and task contracts
- Role and execution records
- Test, build, and review evidence
- Review and repair ledger
Operating outcomes
- Traceable AI engineering
- Reviewable delivery evidence
- Reusable team standards
Capabilities
Project rule injection
Load the stack, test commands, deployment constraints, and collaboration rules.
Task Contract
Define goals, non-goals, acceptance, risk, and rollback before execution.
Agent role orchestration
Coordinate Explorer, Executor, Verifier, and Orchestrator responsibilities.
Verification and review gates
Use tests, builds, browser checks, and code review as completion evidence.
Evidence-chain archive
Record execution, verification, decisions, and follow-up repairs.
Operating evidence
- Task Contract
- Test and build output
- Review decisions
- Change and repair records
Start with
One engineering task with project rules, verification commands, and explicit acceptance criteria.
Governance boundary
AGMesh governs the delivery process; it does not replace the final accountability of business and security owners.
- 03
ELYGate
Model and compute distribution · Make model supply governable
Unify models, GPUs, APIs, accounts, credits, and budgets so requests, usage, metering, and review follow one policy.
Business problem
Providers, accounts, quotas, and consumption are fragmented, making authorization, metering, and attribution difficult.
Best fit
- Enterprises using multiple models and providers
- Organizations that govern team or project quotas
- AI platforms that need cost attribution and audit
Delivery artifacts
- Resource and account inventory
- Routing, quota, and approval policies
- Usage and cost ledger
- Alert and operating rules
Operating outcomes
- Allocatable compute
- Attributable cost
- Auditable permissions
- Replaceable providers
Capabilities
Resource access
Connect model APIs, GPU clusters, private compute, and inference services.
Model access
Support local Qwen, DeepSeek, and GLM models alongside controlled access to closed-source models.
Smart routing
Allocate channels by task, capability, cost ceiling, priority, and availability.
Quota and budget
Set tokens, concurrency, budget, and alerts by team, project, customer, or app.
Cost audit
Retain usage details, team bills, task cost, and anomalous calls.
Permissions and security
Control models and approval actions by role, department, project, and data boundary.
Operating review
Connect resource use to business output, task results, and capacity recommendations.
Operating evidence
- Routing records
- Permission policies
- Quota alerts
- Usage by project and team
Start with
An inventory of model providers, teams, projects, budgets, and approval rules.
Governance boundary
Routing, provider capability, and SLA depend on the actual resource contract and runtime environment.
Combination model
Combine by scenario without replacing every existing system at once.
| AoristWorker | AGMesh | ELYGate | |
|---|---|---|---|
| Best fit | Growth, operations, and office teams · Legal, advisory, and content delivery teams · Enterprises that need one AI work entry | AI and software engineering teams · Multi-agent engineering delivery · Complex projects that require review and replay | Enterprises using multiple models and providers · Organizations that govern team or project quotas · AI platforms that need cost attribution and audit |
| Delivery artifacts | Task and workflow templates · Tool and knowledge connection configuration · Output and approval records · Operating guidance | Project rules and task contracts · Role and execution records · Test, build, and review evidence · Review and repair ledger | Resource and account inventory · Routing, quota, and approval policies · Usage and cost ledger · Alert and operating rules |
| Operating outcomes | One AI work entry · Reusable agent templates · Searchable business data · Auditable delivery records | Traceable AI engineering · Reviewable delivery evidence · Reusable team standards | Allocatable compute · Attributable cost · Auditable permissions · Replaceable providers |
| Governance boundary | Tool access, data scope, and automation permissions are configured against the enterprise system and security policy. | AGMesh governs the delivery process; it does not replace the final accountability of business and security owners. | Routing, provider capability, and SLA depend on the actual resource contract and runtime environment. |
Start with your real workflow.
Prepare your current workflow, core friction, and expected result so Aorist can assess the product combination and delivery path.