AI Consulting
AI strategy, LLM/RAG integration, prompt engineering and AI agent development — for SMEs to enterprises
AI Strategy Workshop (One-Day Sprint)Use Case Discovery & PrioritizationLLM Vendor Selection (OpenAI / Anthropic / Google / Open Source)Prompt Engineering & Prompt Library
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A closer look
Led by Şükrü Yusuf KAYA — strategic consulting that moves AI from "demo" to "production." End-to-end support: from SMEs starting with ChatGPT/Claude to enterprises building RAG (Retrieval Augmented Generation) systems, AI agent architectures and LLM fine-tuning. Not AI hype or PR projects — we build AI solutions that produce measurable efficiency (20-60%), cost reduction and real user value. EU AI Act, GDPR/KVKK compliance and responsible AI principles are monitored throughout.
AI Strategy Workshop (One-Day Sprint)
Use Case Discovery & Prioritization
LLM Vendor Selection (OpenAI / Anthropic / Google / Open Source)
Prompt Engineering & Prompt Library
RAG (Retrieval Augmented Generation) System Setup
AI Agent Architecture & Tool Use
Enterprise ChatGPT/Claude Integration
Custom AI Chatbot Development
Process Automation (n8n, Make, Zapier + AI)
Data Analytics & AI-Powered Dashboards
AI Training (Employee & Executive Levels)
Responsible AI: Bias Testing & Explainability
EU AI Act & KVKK Compliance
Fractional AI Lead / AI-as-a-Service
Methodology
Our AI consulting follows a "ROI over hype" principle and runs in 4 stages: (1) Discovery Sprint (1 week): Use-case mapping, ROI estimate, feasibility scoring. (2) Prototype (2-4 weeks): Rapid prototype on the highest-value/lowest-risk use case — LLM selection, prompt design, first integration. (3) Pilot (4-8 weeks): Limited deployment with real users, KPI measurement, feedback loop. (4) Rollout & Handover: Production architecture, team training, monitoring dashboard. At every stage we explicitly ask "continue, pivot, or stop" — protecting future returns rather than sunk cost.
Who Is It For?
- SMEs: First AI step — integrating ChatGPT/Claude into team routines, setting up a customer-service chatbot.
- Scale-ups & startups: Adding AI features to products or building AI-native ones.
- Enterprises: RAG over corporate knowledge bases, AI agent architectures, team training.
- Holdings & groups: Cross-company AI strategy, governance.
- Professional services (legal, finance, healthcare): AI use in privacy-critical domains, on-premise / private LLM solutions.
- Executives & C-suite: One-day AI literacy / strategy workshop.
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What you’ll gain
- Operational efficiency: 30-60% time savings in customer service, content creation, reporting.
- Higher decision quality: AI-powered summarization and analysis raises the rate of "data-driven decisions."
- Cost reduction: Automation of repetitive work typically cuts operational cost by 15-35%.
- Product competitiveness: AI-differentiated products see materially higher acquisition/retention.
- Better employee satisfaction: Less "boring repetition," more time for creative work.
- Risk management: Responsible-AI foundation prepares you for regulatory shifts.
Why us?
- "Production, not demo": Many AI projects never leave internal demo. We design for production from day one.
- Vendor-agnostic: OpenAI, Anthropic Claude, Google Gemini, open source (Llama/Mistral) — we recommend what fits your cost/data policy; no reseller deals.
- ROI-driven: Estimated benefit and cost on the table from the start, updated with real results at every stage.
- Responsible AI: Bias testing, explainability, EU AI Act and KVKK compliance — built into the architecture, not bolted on later.
- Internal ownership: Your team can run the AI system independently after handover — we don't create permanent dependency.
- Turkish-first LLM expertise: Local experience in Turkish NLP, local data sets and KVKK requirements.
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Service process
01
Discovery Call (Free, 30 min)
First call to test whether your need is really AI (sometimes automation is enough), and scope/budget fit.
02
Discovery Sprint (1 Week)
3-5 stakeholder interviews, current process mapping, 8-15 use cases scored for ROI and feasibility.
03
Use Case Prioritization
Select 1-2 use cases at the intersection of high value + low risk; write success criteria and KPIs.
04
Architecture Design
LLM selection, RAG/agent architecture, data flows, KVKK & security, monitoring plan.
05
Prototype (2-4 Weeks)
A "living" prototype — testable with real data and limited users.
06
Pilot Deployment (4-8 Weeks)
Live use with a selected team/customer group; KPI measurement; weekly iteration.
07
Responsible AI Audit
Bias testing, explainability, EU AI Act and KVKK requirements review.
08
Rollout & Training
Full deployment; employee training; executive dashboard.
09
Handover & Ongoing Support (Optional)
Documentation for internal management; 3-6 month retainer for continuous fine-tuning.
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Prerequisites & expectations
Requirements: executive sponsorship, at least one technical coordinator (not necessarily a developer — a product/business analyst works), access to project data and a "this might not work, we may pivot" exploration mindset. For companies with low data quality, the first phase may be data hygiene (we discuss this openly). For teams under 5, the "one-day AI workshop + 4-week prototype" package is usually more effective.
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Frequently asked questions
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