Consulting services, end to end
AI automation consulting for companies that need a practical path from strategy to production. We design agentic workflows, AI agents, RAG knowledge systems, integrations, governance, and measurable live operations.
AI Automation Opportunity Discovery
We surface repetitive, data-driven work that needs decision support, and prioritize a pilot portfolio by value, feasibility, risk, and data readiness.
- 01Prioritized use-case portfolio
- 02Clear investment decision
- 03Fast pilot kickoff
End-to-End AI Automation Transformation Program
From strategy to platform setup, from pilot development to organization-wide rollout — every step structured under a single program.
Frequently asked questions
How long does it take to get an AI automation into production?
It depends on how ready your data and processes are, not on the model. A scoped pilot on one well-understood workflow moves faster than a broad programme, because discovery and governance stay bounded. We structure engagements so a measurable pilot comes before organisation-wide rollout, which keeps the timeline visible and the risk contained.
Agentic AI workflow architectureDo we have to replace our existing systems?
No. Most enterprise AI work sits on top of the systems you already run — CRM, ERP, ticketing, document stores — rather than replacing them. The integration layer matters more than the model: standardised connections let agents read from and act on existing services under explicit, auditable permissions.
Connecting agents to legacy systems with MCPHow do you handle data governance and KVKK compliance?
Governance is scoped at the start rather than retrofitted. That means deciding what data may leave your network, where processing happens, how long records are kept, and who approves an automated action. For Turkish organisations, KVKK shapes which workloads can use hosted models and which need self-hosted or regional deployment.
KVKK and AI: what you can and cannot sendWhat does an AI automation engagement cost?
Cost is driven by scope, integration depth, and how much preparation your content and data need — not by a licence fee. The largest variable is usually integration and knowledge work, followed by the cost of operating the system after launch. Budgeting for the capability rather than the software is what keeps a programme from stalling after the pilot.
The total-cost worksheetHow do we know whether it worked?
By measuring business outcomes rather than activity. Query volume and time-saved estimates track usage, not value. A defensible model compares total cost of ownership against observable results — cases resolved, cycle time removed, revenue influenced — against a baseline captured before launch.
An enterprise AI ROI frameworkLet's put your AI automationinto orbit
Tell us the process, customer journey, or team capability you want to improve. We will map the right next step for AI automation consulting, an AI assistant, corporate training, or a measurable pilot.
Email Us[email protected]For inquiries, strategy, and demos