Avery

Avery is a platform for building AI agents that run locally on your machine instead of in the cloud. You describe what you want automated in plain language, and Avery uses frontier AI to design and test the workflow, then compiles it into inspectable software made of rules, code, local models, and controlled cloud calls. The result is a deterministic agent you can audit line by line, not an opaque prompt that improvises every time it runs. Once the design phase finishes, the agent behaves like traditional software with reproducible outputs and traceable logic that does the same thing every time you run it.

The problem it addresses is trust. Enterprises and compliance heavy teams hesitate to hand sensitive workflows to cloud AI because they cannot see what it does with the data, predict its behavior, or guarantee it will act the same way twice. Every run is a fresh roll of the dice, and the audit trail amounts to a prompt and a response with no visibility into the reasoning in between. Regulators want explanations, not probability distributions. Avery closes that gap by producing agents that behave like code you wrote yourself. Data never leaves your device unless you explicitly authorize an external connection, every step is logged in plain English, and outputs are reproducible because the agent is compiled from tested logic, not improvised from a prompt at inference time.

Under the hood the system analyzes your task description, tests the workflow on real data samples, and repairs itself until all checks pass. If a step fails, the system diagnoses the failure, adjusts the logic, and tries again. Once locked, the agent runs as a graph of rules and local models with optional cloud calls only for steps that require them. A model routing layer picks the cheapest reliable option for each step, prioritizing local inference when it can handle the job. The platform reports that about seventy percent of steps run entirely on device at zero cost, which keeps bills predictable even when usage scales. You are not paying per token for every minor subtask when a local model can handle it just as well.

Access controls are approval gated across the board. Every external connection, whether to an email service, calendar API, Slack workspace, file storage, or third party data source, requires explicit authorization before the agent can use it. There is no background phone home, no silent data sharing, and no surprise network activity. You decide what goes out and when. Audit trails capture every decision the agent makes so you can trace exactly why a particular output appeared, which matters enormously in regulated industries where you need to explain automation to auditors, compliance officers, or legal teams reviewing a process before it goes into production.

The product fits operations teams, finance departments, compliance officers, legal teams, HR functions, and any organization that handles sensitive workflows but cannot ship customer data to a third party black box. If your company policy or regulatory environment forbids sending data to external AI providers, Avery gives you an alternative that stays on premise without sacrificing the capability of modern language models. It brings the intelligence of frontier AI into your local environment while keeping the guardrails of traditional enterprise software. The value proposition is not just privacy; it is control over behavior that you can verify rather than hope for.

Where it differs from typical agent frameworks is the compilation step. Most agent tools run prompts at inference time and hope for consistent results. Behavior depends on model temperature, random sampling, and whatever the model happens to do that day. Debugging means staring at logs of unpredictable outputs. Avery treats the prompt phase as a design step that produces a frozen artifact. That artifact is inspectable rules and code, not a string you send to an API each time. You can review it, version control it, run it through code review, and know exactly what it will do before you deploy it to production. It is the difference between hoping your assistant does the right thing and proving that it will.

Pricing follows a freemium model. The free tier includes five agents, one app, and fifty runs per day, which is enough to prototype and test workflows without paying anything. The Pro plan at twenty nine dollars per user per month raises those limits to fifty agents, ten apps, and a thousand daily runs, covering most team use cases. Enterprise customers who need on premise deployment, SSO, custom integrations, and unlimited usage contact sales for a tailored quote. The free tier makes it accessible for individual developers to evaluate, while the paid tiers unlock the scale that growing teams need.