Route every AI task to the right model or private environment. Enforce budgets, keep data inside your boundary, and ship with confidence.
The problem
Teams adopt models fast. Costs spiral, sensitive data leaves the company, and every new tool adds another layer of custom glue.
Teams default to expensive frontier APIs for every task. Without smart routing and budgets, monthly bills grow faster than the value delivered.
Code, prompts, and internal data leave your boundary every time you call a third-party model. Regulated and IP-sensitive teams cannot accept that.
Multiple models, agents, and tools mean custom routing logic, inconsistent policies, and no single place to review, approve, or stop work.
Control Room
Model routing, workflow control, cost optimization, and privacy — built as an enforceable system, not a dashboard you only look at.
Send each task to the best model for quality, latency, and cost — frontier APIs, open-source LLMs, or your private infrastructure.
Orchestrate multi-step tasks, approvals, and automations across coding agents and tools with clear ownership and audit trails.
Smart routing, caching, and continuous optimization reduce spend while preserving quality. Budgets and alerts keep costs predictable.
Encryption, data minimization, zero training retention, and private VPC / on-prem routing so sensitive work never leaves your environment.
Enforce organization policies, safety checks, and human gates so AI usage stays compliant, auditable, and under your control.
Monitor progress, costs, and quality. Retry, reroute, stop, or escalate based on policy and budgets — not just traces after the fact.
How it works
Control Room sits between your team and every model or environment. One place to plan, route, monitor, and approve.
A developer or agent submits a task. Policies and budgets are attached automatically.
The system breaks work into steps and routes each to the best model or private environment.
Tasks run with full visibility into costs, outputs, and progress against policy.
Human gates, verification checks, and approvals keep delivery safe and accountable.
Outcomes
Route simple tasks to cheaper models. Cache and optimize so spend tracks value.
Sensitive code and prompts stay inside your VPC or on-prem. No training on your data.
Use GPT, Claude, Llama, Mistral, or your own models without locking into one vendor.
One control plane for routing, budgets, reviews, and promotion — not scattered tools.
Built for teams who already feel the pain
Start with engineering teams using AI coding agents heavily, then expand into platform and enterprise governance.
Developers using LLMs for coding, testing, and debugging who need cost control and consistent quality without slowing velocity.
Organizations that require privacy, auditability, and policy control before AI can touch production systems or customer data.
Teams that cannot risk proprietary code or data leaking into vendor training sets or third-party logs.
Buyers who need flexibility across cloud, on-prem, and hybrid setups with secure routing to private resources.
Why Control Room
Most LLMOps tools help you watch AI. Coding agents help you run it. Control Room governs the work itself — from requirement to delivery.
| Capability | LLMOps platforms | Coding-agent tools | Control Room |
|---|---|---|---|
| Gateway, tracing & cost visibility | Yes | Limited | Yes |
| Execute engineering tasks | No | Yes | Yes |
| Independent coder–reviewer loop | No | Varies | Built in |
| Deterministic completion checks | Limited | Varies | Required |
| Requirement-to-delivery state | No | Partial | Complete |
| Outcome-based cost accountability | No | Limited | Yes |
Pricing
Start with a team plan. Move to enterprise when you need SSO, advanced controls, and dedicated support.
For small teams getting started with governed AI
For engineering teams running AI coding agents at scale
For regulated and large organizations