chromie provides reliable browser automation that keeps mission‑critical workflows running despite changes in site layouts, selectors, or edge cases. Its platform combines deterministic tool calls with an audit trail, ensuring actions are predictable while still allowing AI‑driven intelligence where needed. The self‑healing agents automatically detect and resolve DOM drift, enabling users to build new automations or augment existing ones without manual fixes.
Funding
Funding not disclosed
Founders
Product
Problem
Web automation scripts often break when websites change layout, DOM structure, or encounter edge cases, leading to production failures and costly manual fixes for mission‑critical workflows.
Solution
Chromie provides a browser automation platform that combines AI‑driven intelligence with deterministic tool guardrails. The system lets users create new automations or augment existing ones, automatically selecting the appropriate skill for each step in the workflow. When selectors break or the DOM drifts, self‑healing tools detect the failure and re‑resolve targets without human intervention, learning from each invocation to improve reliability. Every tool call is logged with inputs, outputs, latency, and context, delivering a full audit trail for debugging, compliance, and traceability. The platform also offers execution replay, task‑aware routing, and bot‑detection evasion to ensure stable operation at production scale.
Target Audience
Primary customers are engineering and operations teams that run mission‑critical web workflows—such as e‑commerce checkout, fintech transactions, SaaS onboarding, and data‑extraction pipelines—that require high reliability and traceability.
Features
- Deterministic tool calls that guarantee predictable outcomes for critical steps
- Self‑healing selectors that automatically re‑resolve targets when the DOM changes
- Runtime skill selection that matches the appropriate automation skill to the current task context
- Live execution trace with full audit logs of each skill invocation for compliance and debugging
- Execution replay and task‑aware routing for post‑run analysis and error recovery
- Bot‑detection evasion mechanisms to maintain reliable operation in protected environments
- Auditable actions and past‑run learning to continuously improve automation reliability