Held leadership roles at companies such as Anthropic (Claude) and OpenAI (ChatGPT).
Graduate-level AI education at Stanford and Harvard universities.
20,000+people trained.
50+companies led through digital transformation and AI projects.
6countries served.
100+AI projects delivered.
I design your company's end-to-end AI transformation, providing high cost advantages from training to MLOps processes.
Institutions I Serve
Şükrü Yusuf Kaya is an independent AI architect/consultant delivering end-to-end transformation support — RAG architecture, agentic AI systems, LLM integration, LLMOps discipline, AI governance, and enterprise AI training — across Turkish and EMEA enterprise teams.
Engagement scope spans three primary service lines: (1) Enterprise AI Consulting — reference architectures from strategy to code, vendor selection advisory, governance setup, and production rollout planning. (2) Enterprise AI Training — 50+ role-based programs from executive workshops to engineer cohorts, role-based labs, and sector-specific transformation tracks. (3) AI Solution Architecture — RAG system design, agentic workflow design, fine-tuning cookbooks, and LLMOps infrastructure implementation.
Sector experience covers Turkey's banking, insurance, legal, healthcare, retail, manufacturing, and public-sector clients. Each sector carries distinct regulatory weight — BDDK and KVKK in banking, SEDDK in insurance, KVKK and clinical confidentiality in healthcare, audit-trail mandates in the public sector. Engagements therefore start with sector-aware vendor selection (on-prem, sovereign cloud, open-source) rather than a generic technical recommendation.
Production focus is the common backbone of every engagement. The most prevalent failure mode I see across Turkish and EMEA enterprise teams is the 'demo works but cannot ship' POC. Each consulting deliverable is therefore working reference architecture + documentation + team capability ramp — not a slide deck. Vendor-neutral, KVKK/GDPR-compliant, governance-first.
Training, consulting and delivery — all three lines point at the same goal: keeping the system working in production.
Comprehensive training programs from basic AI to advanced deep learning. Corporate and individual training options available.
Sample program flow
Scope is set according to the organisation's maturity level.
The three service lines answer different questions. See which one fits you today, in one table.
| Comparison | AI Training | AI Consulting | AI Project |
|---|---|---|---|
| Problem it solves | The team doesn't know | Direction is unclear | No working system |
| Typical duration | 1 day – 4 weeks | 4–12 weeks | 8–24 weeks |
| Main deliverable | Capability and usage policy | Roadmap and architecture | A system running in production |
| Who it is for | The whole organisation | Decision makers | Product and IT teams |
| Technical prerequisite | Yok | Yok | Var |
| What comes next | Application | Development | Maintenance and measurement |
Most organisations use all three in sequence: training for a shared language, consulting for direction, a project for a working system.
AI Consultant: An AI consultant is an independent expert who turns an organisation's business goals into AI systems that can reach production — running use-case prioritisation, architecture design, model strategy and governance decisions together with the client.
artificial intelligence consultant · AI advisory · enterprise AI consulting
AI consulting is the work of choosing the right question before any code is written: which use case, which architecture, which model and which accountability frame.
The demo is impressive; in production the cost, latency or accuracy does not hold.
Dozens of ideas exist, but none has been measured for real value.
Off-the-shelf or your own system — the data-ownership and total-cost balance is unresolved.
Risk classification and documentation are required under the EU AI Act, GDPR/KVKK or ISO 42001.
The in-house team exists; an independent outside view is needed for architecture and prioritisation.
| Comparison | Independent consultant | Agency / integrator | In-house team only |
|---|---|---|---|
| Time to start | 1–2 weeks | 4–8 weeks | 3–6 months (hiring) |
| Vendor independence | Var | Yok | Var |
| Fixed cost burden | None | For the contract term | Permanent |
| Knowledge retained in-house | Via handover and training | Usually low | High |
| Depth on architecture decisions | High | Variable | Depends on the team |
| EU AI Act / GDPR coverage | Var | As an add-on | With internal legal |
| Capacity for scaled delivery | Limited | High | Medium |
For most organisations the answer is not a single column: set direction with a consultant, scale with an agency, sustain with the in-house team.
AI awareness training is a corporate program that helps non-technical staff understand in a single session what AI can and cannot do, where to apply it in their own work, and which risks it carries.
AI projects stall when the organisation does not share a language. This program builds that common ground before the pilot starts.
The difference between machine learning, deep learning and generative AI; what is possible today and what is marketing.
Department-level use cases, prioritised with a value/effort matrix.
Participants pick a task from their own work and solve it live with AI.
Hallucination, data privacy, GDPR/KVKK and the EU AI Act; what should never be delegated to AI.
Approval flow, logging and human oversight; a closing session where the organisation writes its own rules.
Scope and duration are adapted to the organisation's maturity level and participant profile.
A good AI trainer is not someone who presents slides, but someone who gets participants to produce a working output from their own data.
Corporate AI training is an expensive investment of time. The bill for picking the wrong trainer is far larger than the training fee.
Why it mattersSomeone who only teaches and someone who has shipped a system answer the same question differently. A trainer who has never seen what breaks in production teaches methods that are correct in class and useless in the field.
How I handle itI have taken enterprise RAG, agentic systems and LLMOps setups to production; every pattern I teach has a field counterpart.
Why it mattersTeaching model architecture to executives and awareness to engineers wastes both groups' time.
How I handle itExecutive, product and engineering tracks run separately; joint sessions exist only to build shared language.
Why it mattersA workshop on canned demo data never surfaces the real internal blockers — data access, format, permissions.
How I handle itLabs run on the organisation's own sample data; the output is a working prototype that remains after the training ends.
Why it mattersTraining that skips hallucination, data leakage, GDPR/KVKK and the EU AI Act accelerates the organisation while accumulating risk.
How I handle itEvery program has a dedicated risk and compliance module; its output is a draft of the organisation's own usage policy.
Why it mattersA satisfaction survey does not measure learning. Lasting impact only shows weeks later.
How I handle itA capability assessment and a 30-day follow-up session are included in the program.
These questions apply to every AI trainer you evaluate, not only to me. Ask for the answers in writing.
Data privacy, trust, verification, and sustainable delivery are non-negotiable design principles in enterprise AI work.
I never treat enterprise data like a generic training set; access, logging, and sharing rules are defined from day one.
I do not ship LLM workflows without validation layers, human review, and risk classification.
Instead of overbuilding on day one, I design systems your team can actually operate and sustain.
Choose your industry to see where AI can create measurable value, which systems it can integrate with, and how the first pilot can be designed.
Finance
In banking and finance, AI value comes not only from automation but from building secure information flows across risk, compliance, document analysis, customer operations, and decision support.
Decision-support systems that analyze customer data, behavioral signals, and historical decisions with explainable models.
A sourced RAG-based search and answer system across policies, procedures, regulations, and internal compliance documents.
A knowledge support panel that helps agents deliver correct, current, and grounded answers.
An analytics layer that prioritizes suspicious transaction patterns, anomaly signals, and operational risk indicators.
Best first pilot: a grounded enterprise knowledge assistant over internal policy and procedure documents.
Is your industry not listed?
We can review your current processes, data sources, and operational bottlenecks together to design a safe, measurable AI pilot roadmap for the first 30 days.
Instantly test how much efficiency you can achieve in your business processes with enterprise AI integration.
MONTHLY
$25,000
YEARLY
$300,000
Learn how to integrate RAG and LLM architectures without compromising your company's data security in this 15-page summary.
Clients follow task tracking, weekly reporting, and model performance visibility through one secure panel.
Actions, owners, and due dates stay visible in one panel.
Weekly summaries and decision notes are shared in executive language.
Cost, accuracy, and usage trends are actively monitored.
Professional Delivery System
Task tracking, reporting, and model performance visibility live inside one secure workspace.
Expertise Paths
Role, industry and solution pages help visitors understand the right use case, delivery model and working scope at a glance.
Expertise surface
30
live landings
Quick entry paths
3
direct paths
Detected signals
9
content signals
Quick entry paths
Solution Pages
Clear consulting entry points across solution pillars such as enterprise RAG, AI agents, private LLM, governance and architecture audits.
10
solution pages
Featured page
Production-grade RAG systems that provide grounded, secure and auditable access to internal knowledge.
Core solution layer
Closest entry point and related paths
Role-Based Pages
Role-specific entry pages for different decision makers such as CTOs, COOs, HR, legal and operations leaders.
10
role-focused pages
Featured page
Technical leadership consulting to move AI initiatives from isolated PoCs into secure, scalable and production-ready architecture.
Decision-maker framing
Closest entry point and related paths
Industry Pages
Context-specific AI consulting surfaces for banking, healthcare, ecommerce, manufacturing and regulated sectors.
10
industry pages
Featured page
Banking-focused AI systems that provide secure, grounded and auditable access to regulations, policies, procedures and internal knowledge.
Context and regulation
Closest entry point and related paths
Proof Layer
These landing pages are not isolated promises. They sit inside a connected consulting system reinforced by related projects, use cases, training assets and adjacent expertise paths.
Solution Proof Layer
Solution Pages
Production-grade RAG systems that provide grounded, secure and auditable access to internal knowledge.
The project, use-case and training proof layer behind the solution landing.
Leading signals for this bundle: kurumsal rag • rag danismanligi • knowledge retrieval • kaynakli cevap
Selected assets
Next paths
Role Proof Layer
Role-Based Pages
Technical leadership consulting to move AI initiatives from isolated PoCs into secure, scalable and production-ready architecture.
Proof and support resources that reinforce the decision-maker narrative.
Leading signals for this bundle: cto yapay zeka • ai mimari danismanligi • kurumsal ai mimarisi • rag mimarisi
Selected assets
Industry Proof Layer
Industry Pages
Banking-focused AI systems that provide secure, grounded and auditable access to regulations, policies, procedures and internal knowledge.
Sector-specific proof and delivery signals that support contextual expertise.
Leading signals for this bundle: bankacilik yapay zeka • bankacilik rag • uyum asistani • mevzuat retrieval
Selected assets
Quick answers about enterprise AI consulting, training, RAG and AI governance
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