B-01
Requirements without substance
Requirements are incomplete, ambiguous, outdated — everyone knows it, nobody has time to fix it. Every defect travels down the V-model cascade and gets expensive in test.
✕ CAPACITY, NOT COMPETENCE
Your V-model, accelerated by AI.
From requirement through planning, development and testing to continuous optimisation: we steward every step of your development process with AI — integrated into your existing toolchain, not bolted on beside it. And we bring every team cleanly up to speed on AI, with training and best practices. This is our core business, built on 15 years of embedded engineering practice. The second stage on the way to the AI-first company.
TOOLCHAIN PolarionJiraGitJenkinsConfluenceArtifactoryCodebeamerGitLabAzure DevOpsIBM DOORSJama ConnectGitHubBitbucketSubversionGerritGitHub ActionsAzure PipelinesTeamCityNexusSonarQubePolyspaceVectorCASTLDRARobot FrameworkpytestEnterprise ArchitectAWSMicrosoft AzureKubernetesREST / OpenAPIOpenAIAnthropic Claude
DIAGNOSIS / 01
Does this sound familiar?
From 15 years in R&D we know the same three findings in almost every development department. They are not a competence problem — they are a capacity problem: everything gets done, but everything only halfway.
B-02
Design guidelines by hearsay
Architecture and design guidelines: a slide deck from five years ago plus whatever the senior engineer keeps in his head. New colleagues guess, reviews have no reference.
✕ CAPACITY, NOT COMPETENCE
B-03
Docs and plans lag behind
Test plans, evidence, traceability get maintained "when there is time" — so never properly. Two weeks of emergency mode before every audit.
✕ CAPACITY, NOT COMPETENCE
This is exactly where AI changes the economics
Demanding, checking, updating, keeping current — the work nobody ever has time for is what the machine does tirelessly and consistently. Your engineers deliver judgement instead of drudgery. The systematics that used to exist on paper are followed through for the first time.
DISCIPLINES / 02
Six disciplines, one continuous process
In engineering, AI does not work as a single tool but as a continuous layer across the whole process — from requirement through planning, development and testing to continuous optimisation. No step is left out.
AI bolted on — the usual
Individual tools, used ad hoc by individuals: a copilot here, a chatbot there. One person's productivity rises briefly — the processes stay as they were. No team effect, no shared standard, no governance frame. A flash in the pan.
AI in the process — our approach
AI built structurally into roles, processes and toolchain: requirements are checked, tests derived, gates run continuously — held to the same quality and release standards as the rest of your code. Reproducible, auditable, team-wide. Deterministic, tested, self-hosted first.
The difference is not the tool — it is the depth of integration.
D-01
Application Lifecycle Management
Your ALM landscape becomes the AI hub: end-to-end traceability from requirement to test result, automated consistency checks across artifacts, AI-supported impact analysis on changes.
PolarionCodebeamerJiraConfluence
D-02
Requirements engineering
Let us be honest: requirements are rarely as good as the process handbook claims — and nobody has time to fix that. AI checks for ambiguity, contradictions, completeness and testability, proposes sharper wording and keeps the baseline current. Reviews that took days become a prioritised findings list in minutes.
PolarionCodebeamerReqIFDOORS migration
D-03
Software development
Code assistance that knows your architecture and coding guidelines. AI-supported code reviews as an additional instance before the merge. Automated documentation that stays in sync with the code.
GitGitLabGitHubEmbedded C/C++
D-04
Test automation
Derive test cases directly from requirements, prioritise regression suites intelligently, evaluate test reports automatically. From 15 years as test engineers we know where automation carries — and where it deceives.
JenkinsHIL/SILRobot Frameworkpytest
D-05
CI/CD pipelines
Pipelines with AI gates: automated findings assessment, intelligent failure analysis on broken builds, release notes at the push of a button. Everything runs deterministically in defined processes — no agent doing the unforeseen. Your artifacts stay traceable, from commit to release.
JenkinsGitLab CIArtifactoryDocker
D-06
Planning & project steering
Planning is part of the process too: AI keeps backlogs clean, surfaces dependencies and risks early, supports effort estimation and drafts status and progress reports from the real project data — not from gut feeling. Your project leads decide on a better basis, right inside Jira, Azure Boards and Confluence.
JiraAzure BoardsConfluenceRoadmaps
Cannot find engineers? Exactly.
Open positions for test and development engineers stay vacant for months — no recruiting budget solves that anymore. The realistic lever: free the engineers you have from drudgery. That is capacity the labour market can no longer provide.
Enablement over dependency — no sprawl, no shadow AI
Integrating AI cleanly today also means bringing the people along — in an orderly way, not as a sprawl of private tools bypassing IT. With our framework we enable every team — development, test, project management, leadership and IT — immediately and hands-on: training, best-practice playbooks and clear guardrails on what AI is for and where its limits lie. That way the return on investment comes fast, without everyone having to learn it all from scratch — we bring the experience in directly and pick everyone up right away. This orderly, holistic approach — from every stakeholder down to the IT infrastructure — is AI governance in practice: a team that masters AI, not an island of tools nobody operates, and no uncontrolled shadow AI.
TOOLS / 03
Integrated into your toolchain — not beside it
No two companies run the same toolchain. That is why we embed AI where your teams already work. A selection of the categories and tools we integrate into — whatever you run, we add to it.
TC-01
ALM & lifecycle
Siemens PolarionPTC CodebeamerIBM ELM (Jazz)Jama ConnectPerforce Helix ALMAzure DevOpsAtlassian JiraOpenText ALM/QCAras Innovator
TC-02
Requirements engineering
IBM DOORS / DOORS NextPolarionCodebeamerJama ConnectVisureSparx Enterprise ArchitectobjectiFReqIFHelix RM
TC-03
Modelling & MBSE
Sparx Enterprise ArchitectCameo / MagicDrawIBM RhapsodyEclipse CapellaMATLAB / SimulinkPTC ModelerPapyrus
TC-04
Version control
GitGitHubGitLabBitbucketAzure ReposGerritSubversion (SVN)Perforce Helix CoreMercurial
TC-05
CI/CD & automation
JenkinsGitLab CIGitHub ActionsAzure PipelinesTeamCityBambooCircleCIArgo CDTekton
TC-06
Build & artifacts
JFrog ArtifactorySonatype NexusConanCMakeBazelGradleMavenAzure Artifacts
TC-07
Test management & automation
XrayZephyrTestRailTricentis qTestpytestRobot FrameworkGoogleTestVectorCASTCantataTessyParasoftTPTVector CANoe
TC-08
Static analysis & code quality
SonarQubeCoverityPolyspaceKlocworkHelix QACLDRAAxivionCppcheckMISRA checking
TC-09
Collaboration & documentation
ConfluenceSharePointNotionMediaWikiAsciiDoc / AntoraSphinxDoxygen
TC-10
Issue & project tracking
JiraAzure BoardsGitLab IssuesGitHub IssuesRedmineYouTrackServiceNowOpenProject
TC-11
PLM & configuration (hardware)
Siemens TeamcenterPTC WindchillDassault ENOVIAAras InnovatorSAP PLM
TC-12
Container & infrastructure
DockerKubernetesPodmanTerraformAnsibleHelm
TC-13
Cloud & hyperscalers
AWSMicrosoft AzureGoogle CloudOpen Telekom CloudIONOS CloudHetznerOVHcloudSTACKIT
TC-14
APIs & integration
RESTGraphQLgRPCOpenAPI / SwaggerWebhooksOAuth2 / OIDCApache KafkaRabbitMQMQTTPostman
TC-15
Observability & operations
PrometheusGrafanaOpenTelemetryElastic / ELKLokiSentryDatadog
TC-16
AI platforms, LLMs & RAG
Azure OpenAIOpenAIAnthropic ClaudeGoogle GeminiGoogle Vertex AIAWS BedrockMistralMeta LlamaHugging FaceOllamavLLMllama.cppLocalAILangChain / LlamaIndexQdrant / pgvector
A selection, not an exhaustive list. Your tool missing? Name it in the initial consultation — the integration follows your process, not the other way round.
THE METHOD / 04
The V-AI-SCALER method
V for V-model, AI for artificial intelligence, SCALER because we take AI measurably from pilot to scaled routine operations — in the process, not as an island. We do not roll out AI blindly; we evaluate first where it carries, then proceed in five steps. Each stage delivers value on its own; you decide how far to go.
-
Assessment
We meet you where you stand. We evaluate process, data, IT infrastructure and team along the V-model — with the people who work in it, and in direct exchange with your IT. We sort each step: where does AI carry now, where is it a quick win, where must the process mature first? The result: a prioritised map — without expensive dead ends.
-
Foundation
Governance before tools: responsibilities, risk classification, data and access, self-hosted where needed. The frame that makes AI auditable — the basis from stage 1 that everything else builds on safely.
-
Pilot
A focused, bounded use case, productive in weeks, with before/after figures. This is where your return becomes measurable for the first time — proven, not claimed, at limited risk.
-
Integration
AI moves into processes, roles and toolchain — together with your IT, embedded in permissions, data protection and infrastructure. No parallel system, no shadow AI. Your engineers keep working in Polarion, Jira and Jenkins, just faster.
-
Rollout & operations
Roll out discipline by discipline, enable every team, establish metrics — then operations, monitoring and continuous optimisation. The measurable pilot becomes reproducible, scaling value.
We show the path and the outcome of every stage openly — the check catalogues, playbooks and the method behind them stay our craft.
REFERENCE / 05
Proven in live operations
REF-01 / ENGINEERING
AI integration into the ALM toolchain of a leading Munich-based technology company: Polarion, Jira, Git, Jenkins, Confluence, Artifactory — in live development operations, without breaking the process.
REF-00 / OUR OWN OPERATIONS
Our own development runs fully AI-driven: requirements, implementation, tests, pipelines. What we build into your operations carries daily production load in ours first.
Prerequisite: stage 1
AI in the development process needs settled governance — otherwise the rollout fails on data protection, works council or liability questions. Stage 1: Governance & Readiness creates that basis; both stages can be started with overlap.
NEXT STEP / 06
Where does your process lose time?
In an initial consultation we walk through your V-model and show you where AI has the biggest lever in your case — concretely, with your toolchain.
FAQ
Frequently asked questions
FAQ-20Where in the development process can AI be used?
AI can support the entire development and verification process: requirements, system design, architecture, module design, implementation, unit tests, integration test, system test and acceptance. The technical depth changes by phase: from management-level risk and evidence to concrete work on code, test data, interfaces and pipeline gates.
FAQ-11Do we have to switch our toolchain?
No — that is the point. We integrate AI into the tools your teams use today: Polarion, Codebeamer, Jira, Git, Jenkins, GitLab, Confluence, Artifactory — and many more, from DOORS through SonarQube to VectorCAST. No two companies run the same toolchain; the integration follows your process, not the other way round. A parallel system nobody looks into is the surest way to bury an AI adoption.
FAQ-21Do executives, engineering leads and teams need different AI views?
Yes. Everyone sees the same process map, but not the same level of detail. Executives and compliance need risk, cost, obligations and approvals. Engineering and QA leads need traceability, gates, dependencies and test strategy. Developers and testers need concrete artifacts, prompts, reviews, test cases and automation in their toolchain.
FAQ-16How quickly can our teams really use AI?
Faster than most expect — because nobody starts from zero. We bring a structured approach and the experience from our own AI-led operation directly to the table and pick up every team where it stands: development, test, project management, leadership and IT, with training and best-practice playbooks. That way the value comes early, instead of after months of self-teaching. The goal is a team that masters AI as a matter of course — not a handful of tools nobody operates properly.
FAQ-03How long until we are productive?
The governance foundation takes weeks, not months. A bounded pilot in the development process is typically productive in four to eight weeks. Company-wide expansion is then a question of prioritisation and your pace — we work in stages so every step delivers value on its own.