Airtop

Automation with Airtop means running many browser sessions reliably, not orchestrating workflows across apps — Airtop doesn't compete with a tool like Zapier or Make for connecting SaaS apps by API; its automation surface is a fleet of cloud browsers you can launch, drive, and tear down programmatically when the target system only exposes a web UI. A published agent in Airtop is a saved Extract or Act configuration that can be triggered on a schedule or by webhook, so a login-and-scrape or fill-and-submit routine becomes a repeatable job instead of a one-off script.

Reliability here is mostly a session-management problem: sessions need saved profiles to stay logged in across runs, proxy routing to avoid being flagged as a bot, and Live View available so a person can step in when a page changes in a way the agent doesn't handle cleanly. We design for all three from the start rather than treating them as fixes patched on after a pipeline breaks in production.

Cost and throughput are governed by Airtop's credit system: sessions, API calls, and proxy bandwidth all draw from the same credit pool, and concurrent-session limits scale by plan, from a handful of simultaneous sessions on entry tiers up to roughly a hundred on Enterprise. We size the automation design against your actual plan's session ceiling and credit budget rather than assuming unlimited parallel browsers.

Scheduling and webhooks are available from the Starter tier up, so most recurring-job automation doesn't need external orchestration — Airtop can trigger its own agents on a timer or in response to an inbound webhook. For automation that has to coordinate Airtop sessions alongside other systems, such as writing extracted data into a CRM or spreadsheet, we still typically hand that final step to a lightweight external trigger rather than trying to do everything inside Airtop.

We scope automation work by first cataloguing which of your target workflows genuinely require a browser, versus ones that have an underlying API you should call directly, which is faster and doesn't consume browser-session credits.

Introduction Airtop enables practical automation that respects real-world constraints while delivering tangible value.

• Automate data collection and transformation pipelines across on-prem and cloud environments with Airtop controlling execution.
• Coordinate device-level actions in manufacturing lines through Airtop’s event-driven workflows.
• Enforce process policies in order fulfilment and logistics using Airtop to trigger safe, auditable actions.
• Orchestrate cross-system tasks such as ticketing, provisioning, and alerting with Airtop at the center of the orchestration graph.
• Implement guardrails that prevent unsafe automation paths while enabling rapid iteration via Airtop-driven experiments.
• Use Airtop to enforce retry, backoff, and failure handling patterns in critical operations.
• Leverage Airtop telemetry to validate automation outcomes against business KPIs and SLAs.
• Integrate with existing CI/CD pipelines to promote automation changes safely under Airtop governance.
• Scale automation to multiple sites or regions without rearchitecting core workflows using Airtop abstractions.
• Adopt a phased automation program that starts with low-risk processes and expands as confidence grows with Airtop.

These use cases illustrate how Airtop can be applied to improve efficiency, reliability, and governance in operational automation.

Airtop's AI is browser understanding, not model training — the product doesn't ship anomaly detection or predictive analytics; its AI layer reads a live web page and turns it into something an agent can act on or extract data from. The Extract API takes a natural-language instruction — get the price and availability for every item on this page — and returns structured JSON from whatever page the browser session is looking at, without you writing a CSS selector or XPath that breaks the next time the site redesigns.

The Act API is the execution counterpart: instead of scripting click, type, and wait calls against brittle DOM selectors, you describe the action in plain language — log in with these credentials, add this item to the cart, submit the form — and Airtop's models resolve that instruction against the current page state. We use this pairing wherever a target site has no usable API: internal tools, legacy portals, and consumer sites that only expose a browser UI become reachable to an agent workflow.

Where Airtop constrains this work is authentication and session realism: sites behind OAuth, two-factor, or CAPTCHA-gated logins need saved profiles and Airtop's residential proxy network to behave like a real user session rather than a bot that gets blocked on the first page load. We plan profile and proxy strategy alongside the extraction and action design, since a well-designed prompt against a blocked or logged-out session produces nothing.

We're also explicit about what Airtop's AI is not built to do: it has no built-in model-monitoring dashboard, drift detection, or governance layer for a broader machine-learning program, and it doesn't replace an analytics or BI stack. Its AI is purpose-built for one job — making a live browser session legible and controllable to an agent — and we scope engagements around that boundary rather than around a generic AI roadmap.

Engagements in this track typically start with a short spike against your actual target sites to confirm extraction accuracy and login reliability, then scale into a scheduled or agent-triggered Extract and Act pipeline once that's proven, built on Airtop's Python or TypeScript SDK rather than raw API calls for anything beyond a handful of endpoints.

Introduction Airtop brings AI into production with clear guardrails and measurable outcomes.

• Deploy anomaly detection on manufacturing lines without moving data outside trusted domains using Airtop.
• Implement AI-powered decision support in customer support workflows, with human review where needed.
• Use Airtop to run lightweight forecasting models that guide inventory decisions in real time.
• Orchestrate model scoring across multiple data sources, maintaining lineage and provenance with Airtop.
• Automate rule-based and probabilistic recommendations while preserving auditability through Airtop dashboards.
• Monitor drift and trigger safe retraining cycles aligned with risk tolerance.
• Provide explainable AI narratives that operators can act on, supported by Airtop logs.
• Leverage AI-assisted anomaly alerts to reduce downtime and improve quality with Airtop gates.
• Coordinate AI outputs with existing control systems via Airtop’s interoperable interfaces.
• Scale AI services across regions with consistent policy enforcement through Airtop governance.

These scenarios illustrate concrete AI-enabled enhancements Airtop enables in day-to-day operations.