Connect your repo. Brainsless reads your whole system, not just the prompts: the agents, models, tools, and the services and code around them, with the file and line each was found at.
Brainsless extracts the rules your AI is supposed to follow from your own prompts: what it must always do and what it must never do. Each rule becomes a test. You can reword them or add your own.
Behavior is a business number. Brainsless maps every user journey in your product and connects each behavior to the metric it affects, like conversion, accuracy, or escalation rate. The tests measure those numbers, not just whether an answer sounds right.
The World Compiler runs your whole system in an isolated sandbox with its own URL: your code, your tools, your services, not a mock. Inside it you run everything that affects how your AI behaves: the cases, the user journeys, the evals, the unit tests. One place to test your AI and actually improve it.
We compile your system and run it in a sandbox. Auth, database, queues and services, and the harness above them. Models, prompts, tools, retrieval, memory, guardrails, fallbacks, routing and orchestration.
The system itself executes, so its behavior here matches production. What remains is delta Δ, and closing it is the work.
Every eval passes, every component reports success, and the system still does the wrong thing for the person in front of it.
We build the journeys your users take, then run them across the combinations your product allows. Coverage widens as your product runs, and delta Δ narrows with it.
Agents got good at code because a compiler tells them when they are wrong. An agent editing the scaffold around a model gets no such answer, because the model's use of that scaffold is not deterministic and no compile step reports on it.
What stands in for one is your system run across the situations that matter, at n large enough for the result to hold. That is a verdict an agent can work against: it edits, reruns, and reads what changed, until the failures are gone and the numbers have moved.
Delta Δ is the distance between what the sandbox predicts and what production does. Every session read and every situation your users reach goes back into the sandbox, and delta falls with each cycle.
Once it is small enough, a result here is a prediction about production: accuracy, retention, resolution, conversion, measured as a value with an interval at a given n. The metric you are trying to move becomes something you can move here first.
An agent can pass every eval and still lose the customer. We find the behavior behind the number, fix it, and prove it moved.
Answers from your data instead of guessing.
Cites the source, or says it does not know.
Solves it on the first reply instead of looping.
Escalates only what a human needs to see.
Finishes the task without handing it off.
Answers the objection instead of discounting.
Checks the order before it approves a refund.
Says what it cannot do instead of stalling.
Confirms the fix worked before it closes.
Sends the easy turns to a smaller model.
Calls the tool with the schema it was given.
Runs the first setup step instead of linking docs.
How do you know a new version of the Driver is better than the last one? Not from the road. Twenty billion simulated miles against two hundred million real ones. One event, replayed with the traffic, the timing, the weather, the road users changed. Situations that never happened anywhere: a flooded street, an elephant on the road.
Your model meets its first hard situation in front of a customer, and the fix is a prompt someone edited on a hunch. That is not a model problem. It is a missing world. The World Compiler builds yours from your repo and measures how far it sits from production. That distance falls every cycle. Raise resolution rate here, it rises there.
Frontier models have converged. What moves the numbers now is everything built around them. Same model, unchanged weights, every time. Hover a row.
Across public harnesses, HAL measures a single model swinging up to 48 points. Public benchmarks, because coding is where the receipts are public. The mechanism is identical whatever the job.
We're building a future where anyone shipping AI can see how their system behaves, change it on purpose, and know the change is an improvement before a customer ever meets it. We are researchers and engineers, and we publish what we learn: five papers so far on long-context attention, memory systems, and what it costs to serve models at this size, each with the code and the hardware bill to reproduce it.
faster attention at a million tokens, from the law that a model reads a small, fixed number of memories per word
of ordinary RAM runs a million-token cache, instead of $180k of GPUs
tokens is where retrieval collapses, and why the memory we build is structured
tokens per second, single stream, trillion-parameter model on four GPUs, reproducible for about fifteen dollars
of what a model reads to write a single word comes from one small shared piece — 7% of the file — that everyone still ships at full size
The unit everything ships as. It pins application and agent code, prompts, workflows, tools, permissions, memory behavior, policies, retrieval, model selection and routing, plus the cases it passed and the evidence approving its release. You diff it, stage it, ship it, and roll it back as one complete artifact.
Teams whose AI talks to customers, makes decisions, moves money, handles sensitive information, or operates critical workflows. If a wrong answer costs you money, trust, or a regulator's attention, the behavior belongs under version control.
Found, not written. The World Compiler explores your world and replays production cases. We generate the inputs you would never think to write. Grades you hand out become permanent standards. Opted-in production traffic turns incidents, dislikes, and corrections into reproducible cases, and the suite only grows.
Never. Weights do not move. Every improvement lives in the behavior version (code, prompts, tools, policies, routing), readable, diffable, reversible in one click. You can't diff a weight update. You can diff a behavior version.
No gradients, ours or anyone's. Your data strengthens your versions and only yours: grades become your standards, and production conversations become cases only if you opt in. Off by default. Nothing you do improves another customer's AI.
Whichever one measures best for the task, always on your own key. Brainsless tests models inside your world and selects them per task on measured behavior, cost, and latency: Kimi K3, DeepSeek, GLM and Qwen among the open ones, OpenAI, Anthropic or Google on your account. The choice is pinned in the behavior version and proven like every other change. We never serve tokens and never resell them; the bill stays with your provider.
It goes into your world overnight and runs the full suite: every case found, every grade given. Morning brings the verdict: quality, cost, and speed, side by side with what you run now. Swap in one click, or don't. Your suite is the benchmark.
Evals are one stage inside the loop. Eval tools hand you the framework and the homework: write the tests, run them, decide. Here you get the failure found for you, the cause traced, the fix written and proven, and a PR waiting for your review. You approve it, stage it, watch it in production, and reverse it if you don't like what you see. Observability tells you what happened. Brainsless gives you the controls.
Yes. Prompts, configuration, every case, every grade: exported as readable files, anytime, no cancellation required. What stays is the machinery: the World Compiler, the discovery, the gates. The files are yours. The loop is the service.
Three tiers, all metered in usage credit. Developer: $50/month, $500 in credit. Startup: $500/month, $5,000 in credit. Enterprise: custom, talk to sales. Sign up free and get $10 in credit to try it first. Model tokens are never on our bill: they run on your own provider key.
Own how your AI behaves.
Connect the repo you already run. Every change ships tested, reviewed, and reversible.
