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02 · Enterprise Transformation

AI Transformation Consulting

Safe, auditable and regulation-aware integration of large language models and agent-based systems into your workflows — not limited to finance, for companies of every sector and size.

The problem: AI stuck in pilots

Most organizations have tried AI; few have taken it to production. The reason is usually structural, not technical: tools are bought before a clear use case is chosen, models are expected before the data infrastructure is ready, teams are set loose before a policy is written. The result: scattered pilots, unmeasured benefit and unmanaged risk.

We fix the order: workflow and data first, then architecture, tool selection last.

What we deliver

  • LLM and agent architecture design — model selection by use case, RAG / tool-use architectures, human-approval layers
  • Data infrastructure and validation pipelines — data preparation, quality controls, output validation and monitoring
  • Privacy-compliant system architecture — KVKK/GDPR-aware processing, retention and access design
  • In-house AI usage policy — which data, which tools, which approvals; written and auditable
  • Transformation roadmap — company-specific prioritization and phasing, from SME to conglomerate

How we proceed

  • Current-state analysis — inventory of workflows, data assets and team capability
  • Use-case selection — prioritization on an impact/feasibility matrix
  • Pilot and measurement — a single workflow, with success metrics defined up front
  • Scale-out — policy, training and monitoring

Frequently asked

Do you only work with financial institutions?
No. Quantitative model consulting is finance-focused; AI transformation is sector-agnostic. Manufacturing, retail, healthcare, services — we work with any organization that has workflows and data.
Which models or tools do you recommend?
We are tool-agnostic and take no vendor commissions. Selection is driven by use case, data sensitivity, budget and in-house capability — and the reasoning is documented in writing.
Does our data go to the model?
That is the core design question. For personal data under KVKK/GDPR, options such as anonymization, on-premise models or enterprise APIs with data-processing agreements are laid out in writing, together with their risks.

Let's start with your institution's question.

The first meeting is for jointly clarifying which of our practice lines your need falls into. It creates no obligation.

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