- Test Lifter
Ship With Confidence. Back It With Evidence.
Your coverage is green, but your team still runs a manual pass before every release. Test Lifter turns requirements and code into test cases, then builds them in the framework you already use. Every test ties back to the behavior it proves and updates as the system changes.
Built for teams who want to walk into the release meeting with evidence, not a coverage number.
Testing Programs Fail in Five Familiar Ways
Manual Test Cycles
Automation starts weeks after the sprint does, because the test cases must be written by hand first.
Brittle Test Scripts
Scripts break on the release that changed a locator, and the fix is a patch applied under deadline.
Limited Visibility
A run report lists what was executed without saying which behaviors were exercised.
Regression Overload
Suites grow until they slow the pipeline they were meant to protect.
Undocumented Legacy Systems
There is no requirement to test against, so coverage gets measured against whatever the suite happens to contain.
Coverage Is Not Confidence
High coverage doesn’t mean you tested what matters. It only shows how much code your tests touched, not whether critical production paths are covered. As code changes faster than anyone can read it, your test suite must prove what the system does and what’s actually been tested.
Without Test Lifter
- Weeks lost writing test cases manually
- Brittle scripts break on every release
- Zero visibility into what's actually validated
- Regression suites grow unchecked, slowing pipelines
- Legacy systems ship with no documentation to test against
With Test Lifter
- Test cases and scripts generated in hours: forward-built from requirements, or reverse-engineered from an existing application for modernization
- Self-healing scripts absorb UI and API changes automatically, with minimal human intervention
- The TCC framework maps every component to a testable capability, so gaps are visible before execution
- Risk-based, intelligent test selection runs only what matters, for faster, parallel pipelines
- Legacy and undocumented systems get modernized automatically: user stories, BRDs, and test suites generated straight from the existing application
How Many Test Failures Are Real?
Know which failures deserve your attention before release.
A Dedicated Agent for Every Stage of the Release Cycle
Each agent owns a different layer of the release cycle, and every one writes
what it finds into a shared, always-current test record.
Five stages. One test record.
Stage 1 Requirement Analysis
Turns business inputs and code into testable requirements.
- Requirement Insight
- Code Intelligence
- Requirement Quality
- Test Plan and Strategy
Stage 2 Test Case Design
Derives cases from requirements and trims the overlap.
- Component Discovery
- Test Design
- Test Optimization
- Test Knowledge
Stage 3 Automation Script Design
Generates and ports scripts across frameworks.
- Automation Script Generator
- Automation Maintenance
- Script Migration
Stage 4 Test Execution
Runs the suite and separates real regressions from environment noise.
- Impact Analysis
- Execution Orchestration
- Failure Intelligence
- Self-Heal
- Execution Recovery
- Auto Bug Logging
Stage 5 Continuous Monitoring
Feeds production signals back into the suite.
- Quality Feedback
- Predictive Quality
- Velocity Dashboard
- Performance Intelligence
See How Test Lifter Works
Move from test coverage to confidence in every release with Test Lifter
What Each Stage Gives Back
Test Planning
Today : 2–3 hours
With Test Lifter 20 minutes
Test Case Design
Today : 3–4 hours
With Test Lifter 20 minutes
Test Execution
Today : 5–6 hours
With Test Lifter50 minutes
Closure & Reporting
Today : 1–2 hours
With Test Lifter 10 minutes
100% of Requirements Traced to Tests
Every test carries a line back to the requirement it proves, from day one.
Strengthen Confidence
Release sign-off shifts from a single green checkmark to an evidence trail your team can stand behind.
Accelerate Releases
AI-led test design and risk-based selection turn release cycles measured in weeks into cycles measured in days.
Reduce Maintenance
Self-healing scripts absorb UI and API changes automatically, cutting the SDET hours spent nursing brittle automation.
Empower Teams
QA engineers stop writing and maintaining scripts by hand, and tribal test knowledge becomes a shared, current suite.
Built for Controlled Testing Environments
Deployment model
On-premises deployment supported, alongside cloud and hybrid options.
Data handling
Test Lifter can run entirely within your own environment using private and open-source models, keeping all code and test data inside your boundary.
Auditability
Every agent action, from test generation to script execution, is logged with a timestamped trail you can export for compliance review.
How a Release Gets Evidence
Indium engineers combine agentic test generation with domain expertise and dedicated framework support for complex, multi-stack and legacy platforms. Each engagement moves through four stages.
Discovery and Opportunity Assessment
Assesses current Jira, Confluence, and ADO workflows, then prioritizes agent use cases by effort saved and quality gained.
Use cases prioritized by measurable impact.
Agent Design and Development
Designs agents for requirement reviews, test generation, self-healing scripts, and reporting, reviewed by engineers.
Governance and approval workflows built in from day one.
Deployment and Observation
Runs a scoped four-week pilot against a real slice of your application landscape, tracking adoption, accuracy, and defect triage time.
A go or no-go call, backed by real pilot data.
Evaluation and Continuous Optimization
Compares outcomes against baseline metrics for quality, cycle time, and defect leakage, then refines agent workflows for reliability.
Continuously refined against your own baseline.
An Indium engineer reviews and validates every generated test case before it ships.
Nothing reaches your pipeline as unreviewed AI output.
Pick the Release You Dread
Run a four-week Proof of Value on a slice of your application landscape. Your own numbers get captured before anything is touched, and the result is reported as a delta against them.
What you walk away with:
- A generated suite covering the paths that carry the risk
- Automation scripts running in your own pipeline
- Flaky tests identified and root-caused
- A go or no-go call, backed by pilot data measured against your own baseline
Frequently Asked Questions About Test Lifter
Test Lifter helps teams improve test automation coverage by identifying gaps beyond traditional code coverage. Its agentic AI analyzes critical business workflows and scenarios to determine whether important paths are adequately tested. This enables QA teams to expand meaningful test coverage, strengthen automation, and make more confident release decisions.
Application changes can cause automated test scripts to fail when UI elements, locators, or workflows are modified. Test Lifter addresses this with self-healing test automation, which detects changes during execution and automatically repairs affected scripts. This minimizes manual maintenance and helps teams keep their automated test suites reliable as applications evolve.
Yes. Test Lifter supports Tosca, Selenium, Playwright, and Appium for test script generation. As a certified Tosca partner, Indium also provides capabilities for working with Tosca-based automation. The platform’s Script Migration agent can help teams move existing test suites between supported frameworks, reducing the effort involved in framework migration.
Yes. Test Lifter can generate test cases from complex business requirements while keeping human validation in the process. Its Requirement Quality agent identifies ambiguity or gaps in source requirements before test design begins. Generated test cases are then reviewed and validated by an Indium engineer before being introduced into the testing pipeline, combining AI-driven test generation with human expertise.
Test Lifter can help teams build test coverage for undocumented legacy applications by deriving testable requirements from UI screens, images, and recorded application sessions. Its Code Intelligence capability can also generate user stories from application code. This gives teams a structured way to understand legacy systems, establish requirements, and create automation coverage without relying entirely on existing documentation.
Test Lifter reduces test automation noise by optimizing the test suite before and during execution. Its AI-powered test optimization capabilities identify duplicate and overlapping test cases, while Failure Intelligence helps distinguish genuine application defects from environment-related failures. The result is a leaner test suite and more actionable test results for QA teams.