AIGENCY: Meet the Homegrown AI Powerhouse That's Redefining the Future in Turkey

There’s something undeniably exciting about seeing cutting-edge technology being born not in Silicon Valley, but right here—on home turf. AIGENCY is Turkey’s first homegrown AI startup, and it’s not just making noise; it’s making serious moves. Built by eCloud Software Technologies, AIGENCY is powered by a jaw-dropping 210 billion parameters. Seventy billion of those come from Meta’s LLaMA 3 model, but the rest? Completely developed in-house.

That alone is impressive—but what really caught my attention was how they've managed to train and optimise the system in Turkish. Turkish is a language rich in nuance, where a single word can shift meaning depending on context, tone, or placement. Processing that with accuracy isn’t just clever—it’s revolutionary for natural language processing.

But the brilliance doesn’t stop there. AIGENCY has launched a fleet of virtual assistants, each laser-focused on a specific field:

• Algın: A startup-savvy assistant supporting entrepreneurs.

• Altay: A medical AI that analyses patient data and provides health insights.

• Tural: A digital legal mind simplifying complex laws.

• Umay: A therapist AI offering mental wellness guidance.

• Alparslan: A technical sidekick for engineers.

• Göbeklitepe AI: A walking (well, talking) encyclopaedia on one of the world’s oldest sites.

What I genuinely admire is their courage. Launching something this ambitious in a developing country takes guts—and succeeding at it? That’s inspiring. They’ve built something powerful, something purposeful, and from the looks of it, they’re only just getting started. They’re not content with being Turkey’s first; they’re aiming to become one of AI’s best.

In this piece, I had the chance to speak with the brilliant minds behind AIGENCY. We talk tech, vision, and what it means to build something that’s not just smart—but truly meaningful.

1. Can you share the story behind the creation of AIGENCY? What was the main need or inspiration?

AIGENCY was born from the need for a local AI system, instead of depending on foreign ones. After the pandemic, AI became a need, not just a luxury. As eCloud Software Technologies, we asked, “Why can’t Turkey have its own AI?” Our inspiration came from both our passion for technology and the idea of a national solution.

2. How did you decide to develop such a large model in a developing country like Turkey?

It wasn’t easy. Big projects need money, data, hardware, and people. But we believed that if we planned everything carefully, we could manage the difficulties. We built a hybrid model focused on Turkish, trained with local data, and aimed to sit at the same table as global companies. We thought, “If we don’t build our own model, we can’t shape our own future.”

3. What is AIGENCY’s strategy to compete with big companies like OpenAI and Anthropic?

We don’t want to copy others. Our goal is to create a unique user experience. AIGENCY stands out with sector-based AI assistants, voice cloning, 278K token reading capacity, and context skills in Turkish. As a small and fast team, we can quickly respond to local needs. Our goal is not to compete, but to be an alternative power.

4. What are the specialties of your team? How did you manage such a big project together?

Our team includes experts in AI engineering, language modelling, data science, interface development, security, and cloud systems. But the most important thing we share is faith, determination, and team spirit. Our 12-person core team worked very hard. Our motivation was: “If this system isn’t ours, whose is it?”

5. What advice would you give to young developers, entrepreneurs, or AI enthusiasts in Turkey?

Don’t give up. AI is not just about code—it also needs patience, vision, and research. Use open-source tools, but don’t just copy. Create local and meaningful projects. Start small but think big. And remember: AI is not only about tech—it’s also about culture.

6. Will AIGENCY be more than a chatbot in the future? What is your strategy for the global stage?

Yes, that’s our goal. We want AIGENCY to be a smart digital companion that learns, gives suggestions, understands context, and works with decision support systems. It will adapt to the user’s style, purpose, and habits.For the global stage, we first aim to lead in Turkish-speaking regions, then expand to other languages and integrate into niche areas in different countries.

7. Is there an experimental feature that excites you the most?

Yes, we’re working on voice-controlled personal avatar assistants. Users will talk to a digital character made with their own voice. These avatars will respond based on their field—like business, education, health, or law. This will make AI feel more human. The project is currently in closed testing, and it excites us the most.

8. How did you combine your 140B parameter model with META’s LLAMA3?

AIGENCY has a layered modular design. We use LLAMA3 for its strong language base. Then we add our 140B parameter Turkish layer, which improves cultural understanding, idioms, and emotional tone. We also taught LLAMA3 to learn over time. So, we combined global power with local intelligence.

9. What was the biggest technical challenge, and how did you solve it?

The hardest part was standardizing data. Turkish has a lot of variety, and meanings change with context. We built special annotation systems and manually classified thousands of data lines. Then we cleaned the data using a technique we call “layered context filtering.”

10. How long did it take to train this large AI model, and what problems did you face?

It took 6 years total, with 4 years of nonstop training. We faced hardware issues, power outages, and data consistency problems. Since Turkey doesn’t have many infrastructures for long-term training, we used hybrid solutions. Our biggest success was optimizing data and computing to make the model efficient.

11. What does having 140 billion parameters mean? What does it help with in real life?

More parameters mean deeper understanding. With 140B parameters, AIGENCY can understand long texts, connect complex ideas, and keep context. In real life, it helps write consistent academic papers, assist doctors with reports, or give legal suggestions.

12. How does AIGENCY understand and answer user questions?

First, the question goes through a pre-processing stage. Then it checks the content, intent, context, and user history. It uses its parameter network to find the best answer. The response is improved by the language model, security filter, and style engine before it’s shown to the user.

13. What kind of infrastructure do you have to give fast responses?

AIGENCY runs on a high-access server with multiple GPUs. Different parts of the model work on different machines at the same time. We also use caching and partial prediction to reduce delays. The average response time is now just 3–6 seconds.

14. Does AIGENCY keep learning over time?

The core model is static, so it doesn’t learn new things from the internet. But each user session is dynamic. Thanks to our "learning boost" system, the model can learn from the user during the session. When you create your own assistant, it becomes even smarter.

15. What was the hardest part of working with a language like Turkish?

The biggest challenge was semantic variety and context changes. One word in Turkish can have 7–8 meanings depending on the context. Because Turkish is an agglutinative language, we created a morphological analysis layer to find word roots. Without it, the model made mistakes.

16. How did you reduce the risk of AI hallucinations (made-up answers)?

We built a 3-layer accuracy filter:

1. Source-based response check

2. Semantic consistency test

3. Security filter

   
   

 Especially for academic and legal answers, this system helps reduce fake information.

17. How does AIGENCY evaluate data while generating responses? What kind of ranking or filtering does it use to ensure the most accurate information is presented?

The model first analyzes the context of the question, then pulls the semantically closest concepts from its extensive language network. During this process, our algorithm called “sequential information weighting” ensures that the most relevant data units are prioritized. In other words, the information presented to the user is generated based on maximum reliability and contextual relevance.

18. Is it easy to adapt this model to other languages? Will there be multilingual versions of AIGENCY?

Our infrastructure is designed to support multilingual operation. It already supports English, while Arabic and German versions are currently in testing. (Users are already experimenting with it in many world languages during the beta phase.) However, each language may require a separate language-layer training. While our top priority is achieving excellence in Turkish, multilingual AIGENCY versions are definitely part of our near-future roadmap.

19. Does AIGENCY customize its responses based on each user input, or does it give similar answers to everyone?

AIGENCY is designed to detect contextual differences. The same question posed by different users—coming with different backgrounds, prompts, or role settings—will yield personalized answers. Inputs from personalized assistants lead to entirely tailored responses. In short, AIGENCY doesn’t just consider “what is asked” but also “who is asking.”

20. In your opinion, how critical is it for a country to develop AI in its own language in terms of digital independence?

Digital independence is now as strategic as border security. Language is the key to that independence. An AI that can think in its own language ensures that a nation’s cultural heritage, scientific development, and collective will are represented in the digital world. Otherwise, a digitally shaped culture driven by external models emerges. That’s why AIGENCY is not just a technology—it is also a project of national consciousness.

Thanks to the AIGENCY team for answering our questions and sharing valuable insights. We’re excited to follow their journey as they shape the future of AI.