Entry point
AI potential analysis
We analyze processes, tools, data flows, manual tasks, risks and automation potential.
- Roadmap and business case
- Quick wins
- Risks and data readiness
- Implementation plan
Services
From AI potential analysis to production automation.
We do not build slideware. We analyze, design, implement, validate and operate automation systems that work in real business processes.
Workflows
Process automation & hyperautomation
Automation across CRM, ERP, knowledge bases, email, documents, APIs, databases and internal tools.
- Workflow orchestration
- API integrations
- Approval logic
- Human-in-the-loop approvals
QA automation
AI Test Factory
AI-supported QA automation for test generation, failure analysis, regression testing, test data, reporting and quality dashboards.
- Automated checks
- Quality gates
- Test analytics
- Release confidence
Worker systems
AI agents & Automation Hub
AI worker systems that analyze, execute, monitor and improve business processes.
- Multi-agent workflows
- Control center
- Dashboards and logs
- Permissions
Documentation
Knowledge base & documentation automation
Automated documentation generation, change tracking, review workflows, templates and compliance-oriented documentation pipelines.
- Knowledge base automation
- Release notes
- Workflow documentation
- Review flows
Healthcare
Care & healthcare automation
Digital intake, patient onboarding, structured care-case intake, document automation and workflow automation for care teams.
- Structures information
- Prepares recommendations
- Supports teams
- No medical or legal decisions
Revenue ops
CRM & sales automation
Lead capture, qualification, email workflows, business system integration, appointment booking, follow-up automation and pipeline visibility.
- CRM automation
- Lead routing
- Follow-up sequences
- Dashboards
Products
Custom AI SaaS & product development
We turn repeatable automation solutions into internal tools, SaaS products, dashboards and customer-facing platforms.
- Internal tools
- SaaS MVPs
- Dashboards
- Scalable product architecture
Technical approach
Reliable AI automation needs more than prompts.
We combine structured workflows, AI models such as OpenAI and Claude, human approvals, quality checks and monitoring so automation can run inside real operations.
Deterministic where possible
Rules, validations and structured data stay clear and traceable.
AI where it creates value
OpenAI, Claude, Codex-assisted work and AI coworkers support analysis, documentation and decisions.
Human control
Approvals stay built in where responsibility, risk or compliance requires them.
Test before scale
Every pilot is checked for quality, edge cases, data flows and user acceptance.
Operations, not handover chaos
Logs, roles, monitoring and clear ownership make automation maintainable.
Workflow orchestrationAI assistantsAI coworkersAPI integrationBusiness system integrationKnowledge work automationQuality gatesAudit logsMonitoringSecure data handling
FAQ
Answers to common questions before the first pilot.
How long does a first pilot take?
A focused workflow pilot usually takes 2-4 weeks when the process, data access and stakeholders are available.
Do you work with our existing tools?
Yes. The normal case is integrating existing systems, data sources, knowledge bases and approval processes.
Is company knowledge sent to AI providers?
It depends on the use case. We review data classes, model choice, approvals and safeguards before productive workflows are built.
Can AI only suggest actions instead of executing them?
Yes. Many useful systems start as assistance with human decisions and become more automated only when confidence is earned.
What happens after the pilot?
You get a clear scale decision: stop, improve, operate in production or transfer the pattern to more processes.