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Executive Summary

Enterprise QA teams and data migration consultants waste 40-60% of their time maintaining brittle automation scripts for legacy systems that lack modern APIs. When a vendor updates a legacy ERP interface or a mainframe terminal screen, existing test suites and migration scripts break, requiring manual rework that costs mid-market companies $50K-200K per migration project and delays product releases by weeks. These teams currently patch together Selenium, desktop RPA tools like UiPath, or resort to manual testing because their target systems are too old, too locked down, or too custom to expose APIs. You could build an LLM-powered automation platform that watches a human perform a task once on any screen (web, desktop, Citrix, terminal), then generates a self-healing automation that uses vision and reasoning instead of fragile selectors. The MVP would be a Chrome extension plus desktop agent that records 5-10 example workflows, converts them to natural language instructions, and reliably replays them against changing UIs with 85%+ success rates. Target price would be $8K-15K per seat annually for QA engineers and migration specialists, undercutting traditional RPA by 40% while requiring 70% less maintenance effort. This market is attractive now because LLM-based computer use just dropped from $5-15 per task to $0.10-0.50, making agentic automation economically viable for the 14 million knowledge workers globally who spend $2K annually on workflow automation, testing, and integration tools. The $28B market is growing as enterprises realize 60-70% of their critical systems will never get modern APIs, and the new Gemini Flash and Claude models finally deliver the reliability needed for production use cases. You are entering as commoditization hands you the core technology at pennies on the dollar. Start by cold emailing 30 QA managers at mid-market manufacturers and healthcare providers who run SAP or Oracle legacy stacks, offering to automate their three most painful regression tests for free in exchange for feedback. GTM would likely run through integration partners and data migration consultancies who bill $200-400 per hour and could white-label your tool to cut project time by 30-50%. The biggest risk is accuracy: if your agent cannot hit 85%+ task completion without human intervention, customers will revert to their painful but predictable scripted tools, so your first 90 days must obsess over reliability benchmarks across diverse legacy UIs before worrying about features or scale.

Executive Summary

Why Now

Google Gemini Flash (December 2024) moved computer use from $15/task frontier demo (Claude, GPT-4V) to $0.10-0.50 commodity capability in a production-grade API, crossing the economic threshold where UI automation becomes viable for high-frequency workflows like nightly test suites and hourly data syncs. Simultaneously, enterprises face QA labor shortages (average 45-day time to hire QA engineer) and DevOps pressure for faster release cycles (daily or continuous deployment now standard), making manual testing and scripted RPA bottlenecks. The shift from API-first SaaS to multi-cloud, hybrid, and legacy system reality means 60-70% of enterprise workflows still touch UI-only systems, creating durable demand for screen automation that traditional integration platforms cannot address.

Pain Points

QA teams spend 30-50% of their time maintaining brittle Selenium/Playwright tests that break when UIs change, taking time away from exploratory testing and new feature validation.

IT ops and data engineers manually copy data between legacy software without APIs and modern systems multiple times per day, creating error risk and wasting 5-10 hours per week per person.

Enterprises pay $50K-200K and wait 3-6 months to deploy traditional RPA bots for simple workflows because scripted automation requires centralized CoEs and developer resources.

MVP Preview

Visual workflow builder for screen automation -- User records clicks, types, and navigation on legacy apps (VB6, AS/400 green screens, old ERPs), then AI converts it into a reusable script with error handling. Output is a scheduled or on-demand task that mimics human interaction. Saves 15-40 hours/week for repetitive data entry or QA validation.

Cross-system data migration pipelines -- User defines source (legacy UI) and target (modern API, CSV, database) and provides sample records. Agent clicks through source UI to extract data, transforms it using LLM reasoning for field mapping, and writes to target. Eliminates $50k-200k consulting projects for one-time migrations.

Regression QA test suite generator -- User provides a list of critical user flows (login, invoice creation, report export) and expected outcomes. Agent clicks through the UI, captures screenshots at each step, and flags visual or behavioral deviations from baseline. Reduces QA cycle time from days to hours for systems with no automated test coverage.

Validation Questions

Model reliability and consistency -- LLM-based agents may produce non-deterministic results or fail on ambiguous UI states, making them unsuitable for mission-critical workflows without human review or fallback logic, limiting enterprise adoption

Security and compliance concerns -- Screen automation requires elevated permissions and access to sensitive data flows, triggering InfoSec review cycles and potential blockers in regulated industries like finance, healthcare, and government

Incumbent response and bundling -- UiPath, Microsoft Power Automate, and Salesforce could integrate LLM-based computer use into existing platforms and bundle with enterprise agreements, eroding standalone product differentiation and pricing power

LLM-based screen automation for legacy QA and data migration

Executive Summary

LLM-based screen automation for legacy QA and data migration

Executive Summary

Executive Summary

Why Now

Pain Points

MVP Preview

Validation Questions

Market Validation

Market Opportunity

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