mobile architecture · auditMove AI out of the pilot phase and into how you work
In most teams a few people use AI tools on their own while the processes stay the same. We redesign your software development life cycle and repetitive workflows around AI and take them to production with measurable results.
Why do AI investments so often fail to pay off?
Pilots never reach production
A nice demo gets applause in the meeting, then nobody uses it in daily work. A pilot with no owner, no success criteria and no maintenance plan stays a pilot.
Tools without a process
Licenses were bought, but everyone uses them their own way. Code review, testing and documentation still run as they did before AI.
Security and cost are unclear
Nobody knows which data goes to which model, how output is verified or what the bill will be at the end of the month.
AI transformation in six areas
We're tool-agnostic: depending on your needs we weigh Claude, GPT, Gemini, open-source models and platforms such as n8n.
AI-assisted SDLC transformation
We rebuild requirements, coding, code review, testing and release around AI assistants and agents, with rules, templates and metrics.
Workflow automation with n8n
From form to CRM, from email to support ticket, from report to Slack. We turn repetitive work into observable flows with AI steps.
AI agents and assistants
Assistants that work with your documents, codebase and systems: an internal knowledge assistant, a support agent, a reporting agent.
AI in mobile products
We add AI features to your app: smart search, recommendations, summarization and on-device models, covering architecture, cost and privacy.
AI governance and security
Usage policy, data classification, model and tool selection, output verification and cost tracking, aligned with GDPR and corporate security requirements.
Team training and rollout
Coding with AI for developers and AI literacy for managers, hands-on with your own code and processes.
AI at every step of the development life cycle
A code assistant license isn't a transformation. The gains come once you define how each step works with AI.
Requirements and analysis
Draft user stories and acceptance criteria from requests; spot gaps and contradictions.
Design and architecture
Option comparisons, ADR drafts, exploring existing code and impact analysis.
Coding
Project rules, templates and safe workspaces for code assistants and agents.
Code review
An AI pre-review before human review: bugs, security and standards checks.
Testing
Generating test scenarios and unit tests, finding coverage gaps, analyzing flaky tests.
Release and CI/CD
Release notes, change summaries, failed-pipeline analysis and suggested fixes.
Operations and monitoring
Crash and log analysis, incident summaries, root-cause suggestions and classifying user feedback.
Documentation and knowledge
Up-to-date docs from code, a Q&A assistant for onboarding and a record of decisions.
Turn repetitive work into flows, and flows into measurable results
With n8n we connect your systems and add AI steps in between: classifying an incoming request, summarizing it, drafting a reply, routing it to the right person. At critical steps the decision stays with a human.
Flows can run on your own server, so you decide where your data lives.
- Sales and support: request classification, CRM records, draft replies
- Engineering: PR summaries, release notes, enriched bug reports
- Operations: report generation, invoice and document processing, approval flows
- Error handling, monitoring and cost tracking in every flow
From experiment to production in four steps
We start small, measure, and scale what works.
Discovery and opportunity map
We listen to your processes and team and prioritize use cases with high impact and low risk.
Pilot
We build a working solution in one or two chosen areas, with success criteria defined upfront.
Rollout
We take what works to other teams and put rules and training in place.
Measurement and governance
We track usage, quality and cost, and update as models and tools change.
Where would you like to start?
Scope is agreed on the first call, based on your team size and goals.
AI readiness assessment
Clarifies where you are and where to start.
- Process and tool inventory
- Priority use cases
- Risk and governance recommendations
- Roadmap and estimated impact
Pilot sprint
Gets one SDLC step or one workflow running.
- A working solution in your environment
- Success criteria defined upfront
- Handover and training for the team
- Results report and next steps
Transformation partnership
Ongoing support through the rollout.
- New flows and improvements every month
- Governance and cost tracking
- Team training and mentoring
- Quarterly leadership report
Common questions
Will our code and data be sent to the models?
You decide. Together we classify which data may go to which tool and, where needed, use enterprise-contract services, n8n on your own server or local models.
Which tools do you work with?
We aren't tied to a vendor. Depending on the need we consider Claude, GPT, Gemini and open-source models, code assistants and automation platforms such as n8n. We take your existing licenses into account.
Will AI replace our developers?
That isn't the goal. We cut repetitive work so the team can spend more time on design, review and decisions. Responsibility for the output stays with people.
How do you measure success?
We agree the metrics before the pilot starts: for example time to complete a task, review time, error rate or the number of manual steps. We measure before and after the same way.
Is this only for software teams?
No. SDLC transformation is for software teams; n8n automations and AI assistants are also used in sales, support, operations and finance.
Let's pick your first AI use case together
In a 30-minute call we'll listen to your processes and suggest the starting point that pays off fastest.