Enterprise AI coding execution platform
Make AI codinga team workflow.
Assign work from Lark or the web. Run it on approved machines. Validate the result, keep the audit trail, and bring your own model account.
01Assign new work anywhereLark and web both start real tasks.
02Use lower-cost models safelySmaller tasks, tighter context, real validation.
03Validate and auditTests, cost and process records stay together.
30-day trial
Your machines
Your model account
READY
Team AI coding entry
Feishu / Web · BYOK · Local machine
New task entry
@Niumate fix the org switching issue after login
One entry connects team collaboration, approved machines and your own model accounts.
30-second product loop
One Lark message runs assignment, execution, validation and reporting.
A concrete loop: assign from Lark, let the task planner break work down, send smaller steps to lower-cost workers, reject a failed check, then return results and a confirmation card.
Lark group
Niumate Bot
Task planner
break down · route · review
Planner
Split into 3 small tasks
Lower-cost worker
qwen / deepseek worker
Validation loop
reject and retry
lint
passed
org-switch.spec
failed once, sent back
refresh org cache
retry passed
Lark approval card
Validation note generated
Changes and test results are back. The final merge is still a human decision.
The unmanaged layer
AI writing code is easy. Governing the result is not.
Who assigned it?Work starts in too many places.
Is it correct?“Done” is not acceptance.
What did it cost?Cost, process and accountability need records.
Niumate puts assignment, validation, cost and accountability into one chain.
The real engineering flow
A task lifecycle: assign, run, watch, accept, remember.
Users see a task moving forward, not a pile of configuration screens.
Send work from where people already are
Mention the bot in Lark, create a web task, or let automation dispatch on schedule.
Run on your own machines
Code, commands and repositories stay on approved machines. Connect your own provider account or intranet model.
Track progress and approve key requests
Follow progress from web or mobile. Identity and permission requests can be approved in Lark.
Tests and checks decide when work is done
Validation must pass before a task closes. The final merge remains your decision.
Every step becomes traceable knowledge
Who asked, how it ran, and what it cost are recorded. Summaries turn finished work into team knowledge.
Cost savings
Do not pay the strongest model for every step.
Use stronger models for planning and review. Let lower-cost models execute constrained steps. The point is not model swapping; it is smaller tasks, tighter context, and validation.
Plan firstBreak large tasks into executable steps.
Execute with lower-cost modelsGive each step only the context it needs.
Validate the resultFailed tests and checks send work back into the loop.
Planning / review modelClaude Opus tier$5 input / $15–25 output
Execution modelQwen3 Coder tier$0.2 input / $0.8 output
About 15–19xunit-price gap. Provider prices and real task results decide the actual account.
Opus-tier$15–25
Qwen-tier$0.8
Clear boundary: this must be tuned with your real tasks. It is not automatic just because a model is connected; strong models still have advantages on complex architecture work.
Estimate your team cost →
Single prompt
Cheap model alone
context loss · weak self-checking · unstable output
01Break down the taskturn large tasks into executable steps
02Package contextfeed only what the step needs
03Run toolsexecute commands and tests on approved machines
04Verify and reviewretry failures, review results, write reports
Guided result
Closer to frontier-model engineering results
for work that can be split, checked and run through tools
Three ways to adopt
From one person to an enterprise.
Validate with real work first, then scale by team size and boundary needs.
Start with real tasks
Use the web immediately during the 30-day trial. Connect Lark and model accounts when you are ready.
- No procurement cycle up front
- Upgrade later by team size
Govern AI coding work
Put members, machines, approvals, audit and cost attribution into one operating flow.
- Choose from the public plan catalog
- Manage assignment, validation and cost
Bring the stack into your boundary
Code execution, model inference, data storage and licensing can stay inside your boundary.
- Logical data isolation across departments
- Audit and accountability stay traceable
Differentiation
Why Niumate?
The real difference is where work runs, who accepts it, and whether the process is traceable.
Alternative
Boundary
Niumate difference
Personal remote tools
Great remote control, but mainly for one person.
Remote control is the base layer. Niumate also manages task planning, validation, approvals, audit and cost.
Official Remote Control
Can take over sessions that already exist on your computer.
Niumate can assign new work from Lark or the web to your own machine even when you are away.
Cloud coding agents
Remote assignment works, but code usually runs in an external cloud.
Code, commands and model sources can stay on your own machines and within your boundary.
Comparison based on public product behavior and documentation available as of 2026-07.
Personal coding tools are usually priced per person. Niumate is priced around team assignment, validation and governance: the part companies actually need to manage.
Trust boundary
Trust comes from boundaries and acceptance.
Tasks enter, run, pass validation, and become records. Each step keeps its boundary and accountability clear.
Task intake
Teams submit coding tasks from Lark, the web, or scheduled automation.
Planned execution
The task planner breaks work down, then routes it to the right assistant template and approved machine.
Validation
Tests and checks must pass before the task closes; failures return to the loop.
Audit and memory
Process, cost, validation results and summaries become traceable records.
Enterprise boundary
Code, models, commands and audit can stay inside your boundary.
Model accountsPublic providers · intranet models
Collaboration entryNiumate service · web · Lark
Approved machinesRepos · commands · tests · reports
Team governanceMembers · machines · approvals · audit
Pricing preview
Start with a 30-day trial, then scale by team size.
Prices load from the public catalog. All tiers share the same capabilities; scale and duration are the only differences.
Loading pricing…
Pricing did not load
-- members · -- machines · 30 days
Start trial-- members · -- machines · recommended
Buy now-- members · -- machines
Buy nowFAQ
The four questions teams ask before rollout.
Clear boundaries reduce procurement and rollout surprises.
Does the subscription include model usage?
No. You connect your own model account and pay model providers directly. The platform fee covers collaboration, task planning, validation and governance.
Where do code and data run?
Coding tasks run on approved machines. Repositories, commands and outputs can stay inside your boundary. Stronger boundaries can be evaluated through private deployment.
What happens after the trial ends?
Nothing is deleted. Existing data remains readable, new actions are blocked, and buying a plan restores write capacity.
Can we pay offline and get invoices?
Yes. Annual and enterprise purchases can go through offline transfer and invoice handling. Contact us for the manual path.
Start now
Run your first real task in the 30-day trial.
30-day trial
Your machines
Data retained