One workspace · many runtimes
quirq deploys your AI agents in one click, then shows you in plain dollars what they deliver, what they cost all-in, and whether the return is improving.
Token sellers keep scaling the price of tokens while offering little insight into how they are used. The bill tells you what the machine consumed. It says nothing about what your business got back.
Spend scales with consumption, not finished output. The meter runs whether or not the job gets done, and a retry loop costs the most of all.
No per-agent, per-task view of token, CPU, and infra spend. You see what the platforms want you to see, one pooled line item at a time.
Every component is a dev-ops and security challenge, and every agent is an orchestration challenge. Most teams stall before anything ships.
Existing analytics platforms track tokens. They do not account for the work delivered.
Every task an agent runs is checked against its definition of done, priced by the person who wanted it, and metered all-in: tokens, compute, API calls, and the human minutes it still needed. Here is a month, read as a ledger.
Illustrative feed of agent tasks. Each row shows a task, whether it was verified, the value it delivered, and its all-in cost.
Agents reuse what the workspace already learned, so the same job gets cheaper with every run.
Budget minus spend, tracked for every unit of work, every session. In June: a $31,170 gap on $6,830 of AI spend across 4,800 units of work.
Illustrative numbers from the worked quarter in the quirq whitepaper. Your ledger is built from your agents' real, verified output.
Run with any model, on any cloud or hardware. Your context lives inside your workspace, your data and privacy stay protected, and your agents increase cost efficiency with each run.
Spin up an environment with the press of a button. Capacity and specs are auto-provisioned to fit the job.
Deploy into the ecosystem you already run. From AWS to GCP to Azure, quirq has one-click integrations with them all.
Every environment ships with quirq analytics: the costs, time spent, and tasks of your agents, in real time.
A native engine builds your context, so agents interpret your goals and draw from what your organization already knows.
Use any model, harness, or framework within your environment. We do not pick your tools, you do.
Deploy a fleet of agents that self-coordinate to drive productivity, transparency, and value toward your goals.
One-minute deploy times let your organization move fast.
Cowork, OpenClaw, Hermes, or Claude Code. quirq gives you full terminal access. We do not pick the harness, you do.
Any model for your harness via OpenRouter. Built-in skills, context management, and memory optimization.
Dashboards with visibility down to the unit of work. Cost and outcomes are tracked per agent and per task.
Behind every number on the dashboard is one simple rule from our research. A task carries a budget set by the person who wants it done. The workspace snapshots the world before and after, verifies completion against a definition of done, and mints budget × completion as delivered value. Nothing is self-reported, so the ledger can be trusted.
Tokens meter consumption. Quirqs meter delivery.
The full calculus with worked arithmetic, the gaming defenses, and the open validation program.
Early access is open for teams running AI agents in production.