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Too many disconnected surfaces
Chat tabs, prompt docs, scripts, and model dashboards all drift apart. No one owns the full flow.
AI Workforce Control Plane
Run specialist AI teams through one orchestrated control layer.
HiAi routes tasks into the right roles, the right models, and the right tools. API-heavy execution stays fast. Local control roles keep policy, routing, and runtime behavior in check.
API-first execution lanes with local watchdog control
Role routing across Claude 4.6, GPT-5.x, Gemini 3.x, GLM-5, Kimi K2.5, Qwen
Tools can run through CLI wrappers, APIs, or MCP connectors
Live Orchestration Map
One task entering multiple execution lanes
Routing Active
Joe Intake
Policy Gate
Strategy Team
Code Team
Research Team
Tool Bus
Control Core
Joe + policy + supervisor
routes tasks, validates risk, opens the right lane
Model Routing
Claude 4.6 -> GPT-5.3 codex -> GLM-5
Tool Lane
CLI skills, API calls, and connectors
Watchdog
Local control checks health, latency, and policy
Control Core
Joe + policy + supervisor
Receives the request, applies policy, and decides the execution lane.
Joe Intake
Policy Gate
Strategy Team
Code Team
Research Team
Tool Bus
Execution Lanes
Model routing, tool calls, and watchdog checks stay under one system.
Why This Exists
Most teams do not have an AI problem. They have an orchestration problem.
The raw models are already available. The missing layer is controlled routing, role design, tool execution, and memory that survives beyond one session.
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No stable routing logic
Teams know which model feels best today, but there is no durable system behind that choice.
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Tools stay bolted on
CLI tasks, APIs, and connectors exist, but they are not governed as one operating surface.
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Outputs do not compound
Without memory and closeout, every new request starts from scratch and the system never matures.
Execution Flow
How the system actually moves a task from request to delivery.
The point is not “many agents talking.” The point is controlled movement: one intake layer, role-aware routing, model-aware execution, tool calls, validation, and memory updates.
Pipeline Diagram
API-first execution with local control checkpoints
Durable lanes, recoverable state
01 Entry
Intake + policy read
Joe classifies the request, loads workspace rules, and opens the correct execution path.
02 Route
Role routing
Supervisor logic assigns specialist roles by domain, urgency, and required confidence level.
03 Route
Model selection
Each role gets a primary model with backup chains from the live pipeline registry.
04 Route
Tools + skills
Execution can call APIs, CLI tasks, MCP connectors, or internal automation skills.
05 Route
Validation loops
Policy, QA, and close checks push failed outputs back into the right lane instead of shipping noise.
06 Persist
Memory update
Accepted results write state, artifacts, and learnings back into the long-term system.
Runtime Lanes
Local control
Joe, watchdogs, policy and queue health stay close to your runtime.
API specialists
Reasoning, coding, research, and content roles can run on the strongest model for the job.
Validation closeout
Outputs re-enter the graph until acceptance criteria, policies, and artifact rules are satisfied.
Team Topology
A complete AI workforce, built as connected specialist lanes.
Teams are not random personas. They are controlled execution clusters with routing rules, default models, fallback chains, and a clear place in the flow.
Topology View
Control core coordinating specialist teams
15+ configurable teams
Control Core
Joe + Policy + Supervisor
Routes every task into the right team, model lane, and validation path.
Selected Team
Strategy & Planning
Business direction and decision framing
Planning lane
Default Model Stack
Primary
Claude Opus 4.6
Fallback chain stays role-specific and can swap to any current pipeline model.
Responsibilities
Chief Strategy Officer
Market Intelligence Lead
Innovation Catalyst
Why This Matters
This team is not a static chatbot preset. It is a configurable execution cluster with role templates, model preferences, fallback rules, and handoff expectations inside the wider orchestration graph.
Model Routing
The right model for every role. Replace any model without breaking the system.
The registry stays current with your pipeline. Roles point at capabilities, not frozen vendor decisions. Swap primaries, keep fallbacks, and preserve execution behavior.
Live registry, not stale screenshots
Update the stack as your docs and pipeline evolve. Routing logic stays role-aware.
Primary + fallback chains
If latency spikes, quota drops, or quality slips, the graph can move the role into its backup path.
Cloud reasoning, local safety
Heavy execution can stay API-first while local lanes keep control, health, and sensitive operations close.
Routing Board
Capability lanes mapped to current stack
Claude 4.6GPT-5.xGemini 3.xGLM-5Kimi K2.5Qwen 3.5
Strategic Planner
Planning lane
Primary
Claude Opus 4.6
Fallback A
gpt-5.4
Fallback B
Gemini 3.1 Pro Preview
Code Architect
Engineering lane
Primary
gpt-5.3-codex
Fallback A
Qwen3 Coder Next
Fallback B
GLM-5
Risk Validator
Governance lane
Primary
Claude Sonnet 4.6
Fallback A
Kimi K2.5 Thinking
Fallback B
DeepSeek V3.2
Governance Layer
Memory, policy, and tool trust are part of the architecture.
Layer
Policy graph
Organization rules, forbidden actions, workspace constraints, and escalation logic live above the role layer.
Layer
Knowledge memory
Accepted outputs, artifacts, and lessons are stored for reuse rather than disappearing into isolated sessions.
Layer
Audit trail
Model choice, tool calls, retries, and close decisions remain inspectable across workflows.
Why It Converts
Buyers need to see control, not just capability.
Most AI landing pages oversell output and undersell control. Your differentiator is not “we use many models.” It is that the system routes, validates, remembers, and stays governable under real operational load.
Policies sit above execution
The control layer decides which tasks can route where, which tools may execute, and when a human-contact path is safer.
Memory compounds system value
A closed loop that stores learnings and artifacts gives buyers a reason to stay. That is stronger than one-off prompt output.
Tool trust must be visible
Make it explicit that CLI skills, API calls, and MCP connectors can be gated, traced, and swapped without rewriting the product story.
Operating Surface
The rest of the stack stays clear, controlled, and extensible.
Once the orchestration story is clear, the supporting product story should be simple: isolation, topology, tools, observability, and durable execution.
Isolated workspaces
Every customer or project keeps its own memory, stack rules, artifacts, and governance scope.
Role-aware topology
Teams can stay minimal or expand into deeper specialist graphs without changing the product model.
Durable pipeline
Long-running flows keep checkpoints, validation loops, and resumable state across execution.
Hybrid runtime
Local control roles and API-heavy execution can coexist without muddying responsibilities.
Tools and skills bus
Execution can reach CLI automation, direct APIs, or MCP connectors under one routing layer.
Observability by default
Health checks, queue metrics, traces, and service visibility stay part of the operating picture.
Deployment Surface
Built on a stack that can stay modular as your runtime grows.
The infrastructure story should reassure technical buyers: durable orchestration, observable services, flexible tool access, and clean data boundaries.
Data + Memory
Persistent state, artifacts, and retrieval layers
- Postgres and vector-backed memory for tasks, artifacts, and reusable knowledge.
- Queue and cache services keep long-running orchestration recoverable.
- Artifact storage remains cleanly separated from role and policy logic.
Orchestration Runtime
Durable graphs, role supervisors, and validation loops
- LangGraph-style runtime patterns for route, execute, validate, and close.
- Supervisor logic decides team handoffs instead of leaving routing implicit.
- Long tasks can checkpoint, resume, retry, and preserve context under load.
Tools + Visibility
Connectors, probes, and traces without connector lock-in
- Tools can be exposed through API calls, CLI wrappers, or MCP when it fits.
- Metrics, health probes, and queue visibility support real operational use.
- Every execution path should remain inspectable enough for buyers and operators.
Use Cases
Built for teams that need repeatable execution, not one-off prompting.
The product makes the most sense where requests already flow through real operational lanes with review, tools, constraints, and delivery ownership.
Product and engineering teams
Route planning, implementation, code review, QA, release notes, and post-release analysis through one system instead of multiple disconnected tools.
Faster throughput without losing technical control.
Content and growth operations
Coordinate research, drafts, SEO structure, editorial review, visual prompts, and distribution as one execution graph.
More output with stronger brand consistency.
Regulated or controlled environments
Keep local control roles, tighten policy routes, and limit where tools and models can operate based on workflow sensitivity.
Operational AI without pretending governance is optional.
Technical Trust
Show the system as operable, not magical.
This section should reassure technical buyers with concrete architectural qualities rather than generic vanity numbers.
Controlled Execution
Routes, retries, and validation are explicit
The product behaves like an operating layer: work enters, gets classified, routed, checked, and either closes or re-enters the graph.
Tool Interop
CLI, API, and MCP fit under one surface
Tool access is framed as a capability bus, not a checkbox list. That keeps the product story extensible as workflows change.
Operational Memory
Artifacts and learnings stay in the system
Stored state and reusable knowledge create a compounding reason to adopt the platform instead of treating it like disposable chat output.
Choose Your Deployment
One platform, three deployment formats. Leave an email for hosted access, or choose the delivery model that fits your team.
Managed VPS Bot
Cloud / VPS Delivery
A hosted VPS deployment for teams that want the system running without managing the infrastructure themselves.
Product access still rolls through the waitlist. Leave an email and we will contact you when this delivery lane opens.
Built for: Teams that want a hosted bot on VPS instead of self-managing the runtime.
Dedicated Deployment
RECOMMENDEDDedicated Hardware Delivery
Dedicated local workstation with HiAi pre-configured for private execution, controlled upgrades, and deterministic team workflows.
What you get:
- Full local processing lane for private drafts and sensitive workflows
- Pre-configured architecture and governance guardrails
- Isolated memory and context profiles per project
- Role-level model routing with controlled fallback chains
Built for: Teams that need local-first execution and private infrastructure ownership.
Enterprise Program
Private Enterprise Program
Enterprise-grade deployment designed around your compliance, integrations, and rollout model with managed migration from pilot to production.
Includes:
- Dedicated project isolation across departments and teams
- Advanced local + hybrid processing architecture
- Custom integrations, governance policies, and role templates
- On-premise or private cloud deployment
- Team onboarding, operational training, and architecture support
Built for: Organizations with strict compliance and multi-team operating requirements.
Frequently Asked Questions
What hardware do I need for HiAi?
For the managed VPS bot, a small VPS is enough. For dedicated deployment, we recommend at least 32GB RAM, 12GB+ VRAM, and fast NVMe storage for local lanes.
Do I still need external model subscriptions?
Not mandatory. HiAi supports local and API models. You can run local-first, API-first, or hybrid, with role-level fallback chains for reliability.
How is data handled across cloud and local modes?
You control the routing policy. Sensitive workflows can stay local while non-sensitive workloads run through cloud providers. This is configured per role and per team.
What is the difference between tiers?
Managed VPS Bot is the hosted path. Dedicated Deployment is a private hardware path. Enterprise Program adds deeper rollout planning, integrations, governance, and operating support.
Bring your orchestration stack into one system.
Leave an email for hosted VPS access, or contact us for dedicated and enterprise deployment paths.