Cortad deeply learns how your AI behaves from every simulated and real conversation, shows your team and coding agents what to change and why, and measures the impact.
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One place for your AI across development, production, and iteration.
Cortad independently reviews your AI app in action. Your coding agent calls Cortad to run realistic user journeys through your actual app and measure how closely it behaves as intended. Your coding agent uses those behavioral insights to make improvements. Cortad runs your app again to measure the difference.
To improve retention or customer satisfaction, start with what happens in your AI conversations. Cortad reads 100% of your production conversations, not a sample, applying up to 100 customizable checks per reply. Did the user get what they wanted? Was the answer accurate? Was the issue resolved? Did they leave satisfied or frustrated?
See results within seconds in a live behavioral dashboard, organized by behavior and use case, with unusual changes flagged. Every conversation reveals opportunities to improve and deepens Cortad’s understanding of your users.
Cortad builds a model of your users, their use cases, and your AI’s behavior from your code and production conversations. This makes simulated users and journeys more closely reflect real needs, questions, and reactions. Trials run against your actual app on your machine, with its prompts, tools, retrieval, memory, and database.
Cortad creates repeatable trials for your core behavior and focused trials for what you want to improve, from resolving more issues to reducing frustration. Your coding agent makes the changes. Cortad reruns the cases using the same behavioral checks as production, measuring progress against your objective and checking whether anything else regressed.
Every production conversation and trial informs what Cortad tests and recommends next.
Cortad runs against the app already running on your machine. It reads your code once, finds the endpoints your AI answers on, and runs selected cases against them.
Read the runtime receipts alongside the replies. Missing credentials, unavailable dependencies, and unobserved tool activity remain evidence gaps, so you can tell what the run actually tested.
A component can report success while the conversation still fails the person asking for help.
Cases come from your reviewed rules, source findings, and supported workflows. Select an agent, a group, or the available suite. Each report separates the cases you ran from the catalog and the work still awaiting inputs.
A plausible patch is only a proposal. The agent can inspect the source and write a regression check that reproduces the failure before editing the app.
Follow the approved repair through its check, edit, restart, and verification. Rerun the cases and compare unchanged contracts with the earlier run. Unsettled results and regressions stay in the report.
Connect supported production sources to inspect recorded sessions, errors, latency, model and tool activity in your World. See which observed usage patterns your tests cover and where the gaps remain.
The visible Production tab refreshes every 15 seconds; provider connections sync about every five minutes. Sandbox results measure the selected cases. Conversion, retention, and customer outcomes need their own production evidence.
Turn a business goal into a concrete behavior to test. These are examples of what to examine; improvements in customer metrics require measurement in production.
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.
Bring that discipline to your AI app: exercise difficult situations against the app you already run, inspect the full exchange, and test an approved repair. Keep the baseline and rerun together. Connected production observations help you decide which situations to test next.
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
One full test of your AI app. Simulated users hold realistic conversations with your actual app, on your machine and through your own model key, and every reply is checked with up to 100 behavioral checks. You see where it failed, how often, the reply that proves it, and the file to change. Runs and reruns are included in both plans.
AI companies whose business depends on how their production agents behave.
Your coding agent, such as Claude Code, Codex or Cursor, calls Cortad after it changes your AI. Cortad runs your app and hands back what went wrong, with the evidence. Your agent makes the fix, and Cortad runs your app again to measure the difference. Cortad never edits your code.
Cortad writes them from your prompts, your code and what your product is for, and adds cases from your production conversations once you connect them. You can add your own checks and metrics in plain words. Your coding agent can read the results but cannot edit the tests.
Your app runs on your machine, and your environment variables never leave it. Cortad reads your code to understand your app, never your .env files, keys or data. Production conversations are read only if you connect them.
No. Cortad does not fine-tune models on your repository or your conversations, and you choose which production sources to connect.
Your first run is free. Hobby is $99 a month and Growth is $499 a month, with runs included, 100,000 or 1,000,000 production messages analyzed a month, and unlimited teammates. Your app answers on your own model key, and that usage is yours. If you don't find value in your first 30 days, we refund you. See every line on the pricing page.
Own how your AI behaves.
Connect your app. Understand the failures. Review a repair and keep the proof.
