SCNET · Enterprise IT · Ankara, Türkiye

Sanal Çekirdek

Reduce repetitive work. Bring decisions closer to reliable data.

Intelligent operations is an approach that shifts repetitive work to automation while keeping exceptions, approvals, and accountable decisions visible. Sanal Çekirdek redesigns workflows around that balance, combining enterprise RAG, AI agents, and deterministic automation in a governed, observable way.

Understand the process before automating it

We measure waiting, repetition, data entry, approvals and exceptions in the current flow. Rather than accelerating a poorly designed process, we remove unnecessary steps and clarify decision points and ownership.

  • Process mining and observation
  • Business-rule and decision inventory
  • Exceptions and human intervention
  • Data source and system ownership

The right mix of RAG, agents and deterministic automation

Workflow engines or RPA suit deterministic rules, RAG suits answers grounded in enterprise knowledge, and agentic AI can support multi-step tool use. The choice depends on task ambiguity and the impact of error.

  • Document and request classification
  • Knowledge retrieval and summarization
  • Cross-system task execution
  • AIOps event enrichment and resolution workflows

Governed decisions and human approval

The data an agent can access, the tools it can call and the transaction or risk thresholds it can handle are explicitly defined. High-impact steps can require approval, dual control or recommendation-only mode.

  • Role- and purpose-based permission
  • Pre-action policy checks
  • Human approval and separation of duties
  • Action logs, rationale and rollback

Observable automation in production

An automation can appear successful while creating latency, cost, misrouting or hidden manual correction. We monitor technical behavior alongside process outcomes and user feedback.

  • Task success and retry rate
  • Human intervention and exception queues
  • Model/tool latency and cost
  • Quality sampling and version comparison

How we work

  1. Define the business outcome and decision owners
  2. Establish baseline measures
  3. Design the integrated solution and operating model
  4. Run a controlled pilot and scale
  5. Measure results and update the roadmap

How success is measured

  • Cycle time, waiting and rework
  • Autonomous completion and human intervention
  • Incorrect decisions, exceptions and rollbacks
  • Cost per transaction and user satisfaction

Frequently asked questions

Which processes are suitable for automation?

High-volume, repetitive processes with accessible data and clear success criteria are strong candidates. For ambiguous, high-risk or rapidly changing work, decision support with human approval is a safer starting point.

How does agentic AI differ from RPA?

RPA is effective for predefined steps. Agentic AI can plan and choose tools based on context, which requires stronger permission, validation and observability. Many workflows can use both.

Does automation remove the human role?

The goal is to move people from operating every step to handling exceptions, quality, decisions and improvement. Task design and change management are therefore as important as the technical build.

Choose the first automation candidates, risk boundaries and measurement method together.

Identify automation opportunities