AI automation & digital products
AI Automation and Custom Software.
I turn repetitive work, fragmented knowledge or a product idea into a usable system. AI is added only where it helps, with provenance, human review and observability.
Where does it help?
Hazar Volga Ekiz leads product architecture and development; scope, data access and validation criteria are clarified before implementation.
- A RAG system that answers from institutional knowledge with sources
- Automation connecting repetitive team workflows
- Custom software, a prototype or MVP to validate with users
Scope
Discovery and risk frame
Workflow, data sources, human checkpoints and success measures are mapped.
Working prototype
A focused product surface tests the critical scenario with realistic data and access rules.
Production preparation
Observability, cost, security, backups and rollback are addressed according to scope.
How we work
01 · Business problem first
The process, user and measurable outcome are defined before a model is selected.
02 · Controlled pilot
Human approval, provenance and failure behaviour are tested in a narrow scenario.
03 · Gradual expansion
Validated flows expand with monitoring and rollback mechanisms.
Products and pilots
Each project is shown at its current stage; this portfolio is not a claim of production or regulatory readiness.
allplan.net.tr
Actively used · human-reviewed FAQ learning, RAG and CRM
View project
SIGNAL Broadcast Systems
Controlled technical pilot · self-hosted campaigns and automation
View project
BIMMetraj
Under development · model, quantity and quotation flows
View project
AffeXAI Cortex
Early access · invitation-only product venture
View projectFrequently asked questions
Does every process need AI?
No. When rules-based automation is cheaper and more reliable, I prefer it; AI is used where uncertain content needs interpretation.
Does a RAG system learn by itself?
I do not recommend uncontrolled learning. Candidate content passes provenance and quality checks before human approval into the knowledge base.
Is an MVP immediately production-ready?
An MVP tests assumptions. Security, load, backup, observability and acceptance testing are separately validated for production.
A workflow or product idea
Let’s clarify the problem and constraints first.
Share the current process, data sources and expected outcome so we can define the smallest verifiable step.
Discuss AI and software