Speisekarte
Speisekarte
Agentic AI built for software quality.
The end-to-end platform that reads requirements, generates tests, executes them, and maintains your suite — on your infrastructure, on your terms.
Die Herausforderung
CI/CD ships faster. QA capacity does not. Regulators are now part of the conversation.
60–70%
of QA effort is spent maintaining existing tests — not writing new ones
DORA
Solvency II · PSD2 — quality is now an audit topic. Regulators want evidence chains, not screenshots.
Generic
AI ≠ QA AI
AI ≠ QA AI
Coding assistants generate code. They do not execute, monitor, or maintain a test suite.
Traditionelle QA-Tools nutzen KI nicht nativ
Manuelles Skripting und getrennte Automatisierung können bei kürzeren Release-Zyklen nicht mithalten.
Generische KI-Assistenten generieren Tests ohne Governance
No audit trail, no project context, no integration with the QA lifecycle — and no data residency control.
Keine Rückverfolgbarkeit zwischen Anforderungen, Tests und Ausführung
Quality reporting becomes guesswork. Regulator-facing evidence is incomplete or missing entirely.
Quality teams need their own AI
Agentic, governed, and on your infrastructure — not a generic productivity tool for individual engineers.
Fähigkeiten
Six capabilities. One platform. Built specifically for the QA function.
From requirements to audit-grade evidence. Not a generic coding assistant — agentic AI specialised in software quality.
01
Requirements → Test Cases
From Jira, ALM, or spec text to executable test cases — automatically.
- Positive Fälle, negative Fälle und Grenzfälle
- Connects to Jira · ALM · Confluence
- Free-text input from PMs or BAs
- Reduzierter technischer Vorbereitungsaufwand
02
Multi-framework Generation
Production-ready scripts across all major frameworks. No proprietary runtime.
- Selenium · Cypress · Playwright
- Robot Framework
- Open standards in, open standards out
03
Autonomous Maintenance
Agents detect stale, broken, and redundant tests and refactor them automatically.
- Erkennung von veralteten oder redundanten Tests
- Self-healing scripts after code changes
- Impact analysis on every commit
04
Coverage & Gap Analytics
Functional coverage maps, gap recommendations and risk-weighted scoring.
- Funktionale Abdeckung Analyse
- Gap recommendations and new scenarios
- Redundanz- und Duplikaterkennung
05
Intelligent Execution Analysis
Separate application failures from script failures. Pattern detection across runs.
- App- vs. Skriptfehlerklassifizierung
- Fehlermustererkennung
- Einblicke zur Weiterentwicklung der Suite
06
Audit-grade Traceability
Requirement → test → execution → result. Signed, archived, retrievable on demand.
- Full DORA / Solvency II / PSD2 evidence chain
- RBAC, audit logs and retention policies
- Regulator-facing evidence in one query
Vergleich
Why Qualigentic?
Generic AI is a productivity tool for individual engineers. Qualigentic is a platform for the QA function.
| Fähigkeit | Generic AI Assistants | Qualigentic |
|---|---|---|
| Generate test code from requirements | Suggestion only | ✓ Production-ready |
| Execute tests, not just write them | Nein | ✓ |
| Maintain the suite autonomously | Nein | ✓ |
| Multi-framework output (Selenium, Cypress, Playwright, Robot) | Teilweise | ✓ |
| Requirement → test → execution → archive chain | Nein | ✓ |
| Data residency / on-premise option | Cloud only | ✓ On-prem available |
| DORA / Solvency II / PSD2 audit evidence | Nein | ✓ |
| Pricing model | Per-token (developer seat) | Platform + services |
ChatGPT, Claude direct, GitHub Copilot, Gemini Code Assist suggest code. They do not own the QA function.
Architektur
Open standards in. Open standards out. Customer infrastructure throughout.
Multi-model AI support, agentic orchestration and deployment options to fit every context — including regulated industries.
Eingaben
Funktionale Anforderungen
User Stories / Spezifikationen
Bestehende Tests
Jira · ALM · Confluence
Free-text from PMs
→
Qualigentic Platform
RAG-Engine
Project context, test patterns, organisational memory
Agentic Orchestration
Multi-step planning, tool use, human-in-the-loop checkpoints
Multi-LLM Layer
Llama · Mistral · Anthropic · Azure OpenAI · AWS Bedrock
Fine-Tuning (PEFT / LoRA)
Customer-specific adaptation. Weights stay on customer infrastructure.
Governance & Audit
RBAC · Signed evidence chain · Retention · DORA-aligned controls
CI/CD-Integration
GitHubGitLabAzure DevOpsJenkinsBitbucket
→
Ausgaben
Selenium-Skripte
Cypress-Skripte
Playwright-Skripte
Robot Framework
Coverage reports
Audit evidence
Bereitstellungsoptionen
On-Premise
Your data centre. Open-source self-hosted models (Llama, Mistral). No data egress under any condition. Recommended for banking, insurance, healthcare and government.
Private Cloud
Your tenant. Azure AI Foundry, AWS or GCP — customer-owned. Bring-your-own model and keys. Region pinning for EU, US, JP data residency.
SaaS
Caixa Mágica managed. Fastest time-to-value for non-regulated workloads. EU-hosted, SOC 2-style controls, Anthropic / OpenAI / Azure OpenAI selectable.
Tiering is by capability, not by deployment. All tiers support on-premise, private cloud and SaaS. Regulated clients can start on-premise from day one — no lock-in to a cloud-only path.
Pakete
Tiers by capability, not by deployment.
From first AI adoption in QA to mission-critical operations in regulated industries. All tiers support on-premise, private cloud and SaaS.
Vorspeise
QA teams adopting AI for the first time
- Multi-Framework-Testgenerierung
- Requirements → executable scripts
- Dashboards und Berichte
- Single-team scope
- Any deployment model
Am beliebtesten
Beruflich
Teams scaling QA across products
- Alles im Starter
- Autonomous test maintenance
- Coverage & gap analytics
- Intelligent execution analysis
- CI/CD deep integration
- Multi-team scope
Unternehmen
Mission-critical operations, regulated industries
- Alles im Profi
- Full DORA / Solvency II / PSD2 audit chain
- Fine-tuning (PEFT / LoRA) on customer infrastructure
- SLA + dedicated success engineering
- Co-created roadmap
- QA / TestOps consulting
Regulated clients can buy Starter on-premise from day one. Commercial flexibility is part of the offering.
FAQ
Häufig gestellte Fragen.
How is Qualigentic different from a generic AI coding assistant?
Generic AI assistants like ChatGPT, GitHub Copilot or Gemini Code Assist suggest code — they do not execute, monitor or maintain a test suite. Qualigentic is an agentic platform built around the QA function: it reads requirements, retains project context through RAG, generates production-ready tests, runs them, analyses failures, and maintains the suite — all with a governed audit chain.
Which frameworks are supported?
Selenium, Cypress, Playwright and Robot Framework are first-class. There is no proprietary runtime — all generated scripts use open standards. Additional frameworks can be enabled via the platform's adapter layer in the Enterprise tier.
Can Qualigentic run fully on-premise?
Yes — and this is available from the Starter tier, not just Enterprise. On-premise deployments use open-source self-hosted models (Llama, Mistral and others). Fine-tuning weights (PEFT / LoRA) never leave the customer's perimeter. No data egress under any condition.
How does Qualigentic handle regulatory compliance requirements?
The platform produces a signed audit evidence chain from requirement through to archived result, designed against DORA Articles 6 & 9, Solvency II Pillar 2, and PSD2 Article 95. Enterprise-tier customers get full RBAC, retention policies, and regulator-facing evidence retrievable on demand.
What does a typical pilot look like?
A time-boxed 6–8 week engagement: one application, one framework, one defined success criterion. Weeks 1–2 cover discovery and environment setup; weeks 3–5 run the first generation and execution cycles; weeks 6–7 validate coverage and produce a regulator-facing evidence sample; week 8 delivers an executive readout with ROI calculation and expansion plan. The pilot fee is credited 100% against the year-one platform fee on conversion.
Which AI models does Qualigentic use?
Qualigentic is model-agnostic. Supported options include open-source self-hosted models (Llama, Mistral), Anthropic Claude (preferred partner), Azure OpenAI / OpenAI, and AWS Bedrock. Customers choose the provider that fits their compliance scope — no Qualigentic lock-in.
Does Qualigentic replace QA engineers?
No. It removes the manual scaffolding, maintenance and triage work — freeing engineers to focus on test strategy, complex scenarios and quality engineering. Humans approve, escalate and override at every step of the agentic loop.
Los geht'sAgentic AI for Software Quality.
Agentic AI for Software Quality.
Lass uns reden.
Personalised demo, no commitment. We'll show how Qualigentic fits your context — frameworks, infrastructure and governance. Pilot fee credited 100% on conversion.