Quality Intelligence for the enterprise SDLC

AI can write your code. It can’t decide what your business meant.

Spec2TestAI finds the defects in your requirements before any code is written, keeps a record of what the business decided, and tests the software against it with evidence.

Works with Jira, Azure DevOps, GitHub Copilot, Cursor and Claude.

Requirement checkRequirements Intelligence
Sample story
User story, as written
As a shopper, I want search results to load fast so I can find products quickly.
Findings
“fast” is already decided
Search responds within 2 seconds on mobile. Product owner, 3 Sep 2026. Kept in the project knowledge base.
“quickly” has no measurable outcome
Open question for the product owner. The AI suggests answers; a person decides.
32 quality measures checked Not ready for build: 1 open question
The platform

Code is abundant. Good specs are scarce.

Coding agents amplify whatever they are given, including a requirement nobody questioned. One platform keeps three things true from the first sentence to the release.

Decided

Every requirement is checked on 32 quality measures. What it leaves open becomes a question for the people who can answer it. Each answer is kept in the project knowledge base as a decision, with who decided and when, and informs every later requirement.

Covered

Tests and synthetic test data are generated from the requirement with decision tables and cause-effect models, so coverage is by construction and traces back to the sentence it came from.

Proven

Spec2RunAI runs the tests in plain English against the live application and reports which steps were verified, with evidence a business owner can sign.

Comparison diagram showing Without Spec2TestAI versus With Spec2TestAI. On the left, business intent and vague requirements pass through the spec gap into coding agents and software, creating weak feedback, late discovery, and brittle outcomes. On the right, Spec2TestAI acts as the specification intelligence layer, taking in domain knowledge, stakeholder questions, dependencies, acceptance criteria, and quality rules to clarify, analyze, cross-check, enrich, generate tests, and maintain traceability before feeding coding agents and software, with validation and predictive quality closing the loop.
How It Works

Enterprise AI Operational Framework

From business intent to measurable outcome, Spec2TestAI makes the specification the governed source of truth for AI-driven delivery.

1

Intent

Business goals

2

Requirements

Quality analysis

3

Specification Intelligence

AI-ready spec

4

Execution

Spec-to-test &
agent-driven automation

5

Verification

Coverage &
predictive quality

6

Governance

Quality gates &
auditability

7

Measurable Outcome

Less rework,
faster delivery

Requirement Intelligence· Defect Prevention· Specification Intelligence· Test Generation· Predictive Quality· Enterprise Observability
What happens at each stage
1

Intent

Quality starts with the business goal. Product objectives and stakeholder alignment define what “done” actually means before a single story is written.

2

Requirements

AI analyzes each user story and its acceptance criteria across 32 quality measures, detecting ambiguity and enhancing the requirement for clarity, completeness, and business value.

3

Specification Intelligence

The platform ingests project artifacts and builds cross-story intelligence, so every decision is informed by a cumulative, traceable knowledge base rather than a single isolated ticket.

4

Execution

Approved specifications drive the work: spec-to-test generation, governed spec-to-code prompts, and agent-driven automation that turns intent into working assets.

5

Verification

Your manual test scripts run as written, in plain English, with coverage and requirement-to-test traceability, plus predictive quality analysis that pinpoints what each code change puts at risk.

6

Governance

Quality gates, auditability, and enterprise controls make quality provable — evidence for regulators, auditors, and leadership rather than assurances. Every test run ends in a sign-off-ready evidence report.

7

Measurable Outcome

Less rework, faster delivery, and higher confidence in every release.

The AgileAI Labs enablement layer — Requirement Intelligence, Defect Prevention, Knowledge-Aware AI, Test Generation, Predictive Testing, and Enterprise Observability — works continuously across all seven stages, learning and improving with every cycle. The result: shift left, reduce defects, improve delivery quality, and accelerate enterprise AI adoption with confidence.

◔
32AI quality measuresacross the SDLC
</>
0hard-coded selectorsdecisions on the record
✓
3,000concurrent test casestested in a single run
↗
Traceabilityrequirement to releaseend-to-end visibility

Spec2TestAI™ in Action

From an ambiguous story to executable quality — in minutes · or explore the full demo library →

Where do you want to start?

For engineering leaders

You run AI coding agents

Agents build what the spec says, right or wrong. Give them requirements that have been questioned and decided, delivered into Copilot, Cursor and Claude through Spec2Code AI.

For QA leaders

You run a QA team

Run the manual test cases you already have, as written. No scripts, no hard-coded selectors, and a report that shows which steps were verified.

Spec2RunAI™ is an AI-native no-code test automation tool for teams that want to run their existing manual test cases in plain English without building or maintaining test automation code. It is available on its own as the test executor, and it is the execution layer of Spec2TestAI™ for requirements-to-release traceability.

Running Insurity, Guidewire, Salesforce, SAP, Workday, or ServiceNow? Read the practical guide to what has to be tested and why suites break on vendor release calendars.


Defect prevention · the economics of quality

The cheapest defect is the one
that never gets written.

A defect found in a requirement costs a conversation. The same defect found in production costs a release, and with coding agents it is built faster and tested by the same guess.

1×
Requirements
← we act here
Design
Coding
Testing
5×–100×
Production

Fixing a defect after delivery often costs around 100× what it costs to fix during requirements and design; on small, non-critical projects the ratio is closer to 5:1 — Boehm & Basili, “Software Defect Reduction Top 10 List,” IEEE Computer 34(1), January 2001. Their baseline covers requirements and design together. The unlabeled bars show the direction of the gradient, not measured multipliers.

40–50%

of software project effort is spent on avoidable rework.

30–60%

of tester time goes to finding and building test data.

70%+

of web test breakages are caused by element locators.

Rework: Boehm & Basili, IEEE Computer 34(1), 2001. Locator breakage: Hammoudi, Rothermel & Tonella, “Why do Record/Replay Tests of Web Applications Break?”, ICST 2016 — 300 application versions, 722 breakages. Test-data figure reflects typical industry variation. Spec2TestAI and GenRocket address all three — prevention, test data, and maintenance.

Quality Risk Audit

Send us one backlog. Get the defects before the code.

We score one backlog of up to about 250 stories on 32 measures, find the conflicts across them, and price what each defect would cost you later. About a week. Fixed fee, fully creditable toward a follow-on engagement. No source code needed.

Request a Quality Risk Audit → — request a demo

Partners

Spec2TestAI is how we turn ambiguous requirements into executable quality — on day one. By auto-generating scenarios and automation boilerplates, preserving live requirement-to-test traceability, and surfacing high-risk journeys early, we're cutting cycle time and defect leakage while giving leaders audit-ready confidence. This is the practical engine behind our shift from Quality Engineering to Quality Intelligence.

Works with your stack
Jira Azure DevOps GitHub Copilot Cursor Claude GenRocket · partner AWS Bedrock Azure OpenAI
Built for enterprise trust
Behind-firewall execution
Source code never accessed
Per-tenant data isolation
Full AI decision audit trail
SOC 2 Type II attested
Synthetic data, no production PII
Reviews

Spec2RunAI is listed on SourceForge, Slashdot, and Top Business Software. Reviews on those sites are written by users; if you run Spec2RunAI, we would value yours.

Request a demo

See it on your own requirements.

Send a backlog or book a demo. We will show you what your requirements leave open before anyone builds from them.

Request a demo →