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Newsroom

TRAI’s May Workshop Brings Together Turkey’s AI Ecosystem

Our CEO joined TRAI Mayıs Çalıştayı to connect with Turkey’s AI leaders and reflect on the growth of the local AI ecosystem.

May 12, 2025
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Last week, our CEO Rıza Egehan Asad had the pleasure of attending the TRAI ''Mayıs Çalıştayı'', held at the peaceful Kemer Country Forest House.

The event brought together leading minds from across Turkey’s AI ecosystem for a day filled with honest conversation, thoughtful group work, and inspiring panels, all focused on the future of artificial intelligence in Turkey.

Reflecting on the day, Rıza Egehan Asad shared that one of the most valuable takeaways was witnessing just how much the ecosystem has matured over the years. The collaborative energy and open exchange of ideas reinforced the importance of creating spaces where people from diverse backgrounds can share, listen, and shape what’s next together.

Our CEO, Rıza Egehan Asad, took part in the TRAI May Workshop, joining leaders from across the AI ecosystem.
Our CEO, Rıza Egehan Asad, took part in the TRAI May Workshop, joining leaders from across the AI ecosystem.

He left the workshop feeling more energized, with fresh questions, renewed motivation, and a stronger sense of community.

A heartfelt thank you to Türkiye Yapay Zeka İnisiyatifi (TRAI) for the kind invitation and for continuing to create such meaningful opportunities for connection and growth in the Turkish AI ecosystem.

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All About Dot

Best AI for Teams: Dot vs. ChatGPT

A detailed comparison of Dot and ChatGPT, focusing on model options, data control, workflow automation, and business integrations.

May 8, 2025
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Choosing an AI tool is not just a matter of convenience. It shapes how a company handles tasks, workflows, and future growth.

Many teams turn to ChatGPT because it is well known and easy to start with. However, businesses that need more than simple conversations often find themselves looking for deeper functionality.

Dot is designed for teams that want to move beyond basic chatbots. With advanced agent orchestration, full data control, and flexible integrations, Dot gives businesses a platform that grows with their needs.

This post compares Dot and ChatGPT side by side to help you find the best fit for your team’s goals.

Model Options: One Path or Multiple Choices

Choosing the best AI for teams starts with flexibility. Model variety often makes the difference between a good experience and an outstanding one.

  • ChatGPT limits users to OpenAI’s models such as GPT 4.5, GPT 4, or GPT 3o.
  • Dot allows businesses to choose from multiple models including Cohere, Anthropic, Mistral, and Gemini (and also ChatGPT) depending on their needs.

Having multiple model options means teams can optimize performance for different industries such as finance, healthcare, or customer support.

When searching for the best AI for teams, flexibility is no longer a nice to have. It is a must have.

Data Control: Managing Your Own Information

Data security is a top priority for every business that deals with customer information, financial records, or sensitive projects.

  • ChatGPT stores all user data on OpenAI’s servers without offering customizable hosting options.
  • Dot offers full data control by letting businesses choose between cloud hosting, on-premise hosting, or a hybrid setup.

This flexibility is crucial for industries that need to meet regulatory standards or simply want to keep sensitive information in house.

For companies that prioritize security when selecting the best AI for teams, Dot offers a clear advantage.

Functionality: More Than Just Chatting

When it comes to real business needs, AI should do more than chat. It should work alongside teams and drive progress.

  • ChatGPT functions mainly as a conversational assistant.
  • Dot operates as a complete AI platform where multiple AI agents can collaborate, handle tasks, and automate workflows.

What is an AI agent?

An AI agent is a specialized digital assistant built to perform a specific task. Instead of giving general answers like a chatbot, an AI agent focuses on completing actions, moving projects forward, and working with other agents to manage full workflows.

Here is a quick view:

ChatGPT vs. Dot
ChatGPT vs. Dot

With Dot, you can create an entire system where agents pull data, analyze results, and complete tasks in sequence without human intervention.

This shift from conversation to orchestration is what sets Dot apart when teams look for the best AI for teams that can truly support operations.

If you are already interested in how a single platform can manage all your AI needs, you might also enjoy reading this deep dive into Dot's capabilities.

Customization: Tailor AI to Your Needs

Customization defines how far a team can go with their AI tools.

  • ChatGPT offers limited customization unless you dive into coding or use external APIs.
  • Dot provides a no code environment where teams can:
    • Build their own AI agents
    • Create cross agent workflows
    • Adjust agent behavior visually without technical skills

Even though Dot is designed to be no code friendly, it is also built for technical teams. Developers can customize agents further, integrate deeper into company systems, and create highly specific workflows based on department or team needs.

Dot acts like a flexible AI framework. It gives technical teams the tools they need to build tailored solutions without starting from scratch, making it one of the best AI for teams that include both business users and technical experts.

Integrations: Connecting with the Tools You Already Use

A good AI platform should connect easily with the tools your business relies on every day.

  • ChatGPT offers basic API access for custom integrations but few ready made options.
  • Dot includes native integrations with major platforms such as:
    • Slack
    • HubSpot
    • Salesforce
    • Zendesk
    • And many others

For businesses that value seamless automation, this level of connectivity makes Dot one of the best AI for teams aiming for efficiency and ease of use.

See the full list of apps and integrations Dot works with here.

Pricing: What Are You Really Paying For?

Understanding pricing is about more than looking at monthly fees. It is about knowing what each platform unlocks for your team.

  • ChatGPT offers a basic free plan with limited capabilities.

The paid plan, ChatGPT Plus, costs $20 per month and gives access to GPT 4 Turbo. For businesses, OpenAI offers ChatGPT Team and Enterprise plans. These include admin tools, API credits, and usage policies but can become costly as team size and usage grow.

  • Dot provides a flexible pricing model built for businesses.

Teams can get started with a free sign up and pay-as-you go model that includes access to Novus models, ready made agents, and app integrations. Paid plans offer multi model access, enterprise grade support, custom agent creation, on premise deployment options, and scalable workflows based on team and company needs.

Quick Overview:

ChatGPT vs. Dot
ChatGPT vs. Dot

Choosing the best AI for teams means looking at flexibility.

Dot gives businesses room to scale smartly, and customize their plan based on real usage and team size.

See all Dot plans and pricing details here.

Conclusion: Why Dot is Built for Business Success

While ChatGPT offers a simple and familiar experience for individuals and casual tasks, businesses often need more. They need flexibility, control, deeper integration, and real workflow automation.

Dot is designed from the ground up to meet these needs. It gives businesses the power to:

  • Work across multiple AI models
  • Maintain full control over data and deployments
  • Build no code and custom coded workflows
  • Integrate easily with existing tools and systems
  • Scale operations efficiently with flexible pricing

If your goal is to find the best AI for teams that will not just keep up with your needs but help you grow faster and work smarter, Dot stands out as the platform of choice.

Open your free Dot account today and start building a smarter AI-driven operation for your team.

Frequently Asked Questions

What is the difference between ChatGPT and Dot?

ChatGPT is a conversational tool, while Dot is a full AI platform built for business workflows, data control, and integrations.

Is Dot better than ChatGPT for business use?

Yes, Dot offers more flexibility, security, and workflow automation features that are critical for business environments.

Can I use Dot without technical skills?

Yes, Dot has a no code interface for non technical users while also allowing technical teams to customize it fully.

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Newsroom

Future of AI Agents at Webrazzi XYZ 2025

Our CEO joined Webrazzi XYZ 2025 to discuss Dot, AI agents, and what comes next for businesses in the post-ChatGPT era.

May 7, 2025
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As AI continues to reshape entire industries, events like Webrazzi XYZ 2025 remind us how valuable it is to be part of conversations that go beyond the surface.

Our Co-Founder and CEO, Rıza Egehan Asad, joined a panel of forward-thinking leaders to talk about how brands are navigating the post-ChatGPT era and how the AI landscape is evolving rapidly from hype to real-world application.

In his talk, Egehan explored the role of AI agents in modern business environments and how Dot, our all-in one AI platform, is helping companies build smart workflows tailored to their unique needs. He emphasized the growing importance of model flexibility, data control, and automation, elements that are no longer optional but necessary for businesses looking to stay competitive.

The energy in the room, fueled by meaningful questions and shared visions of the future, made this event especially memorable for our team.

A heartfelt thank you to the Webrazzi team for organizing such a thoughtful and future-focused event. We're proud to have contributed to the discussion and excited to keep building what comes next.

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Novus Voices

The New Oppenheimer Era: Artificial General Intelligence and the Race We Can’t Control

AGI isn’t a bomb, but treating it like one could be the biggest mistake of all. Are we racing toward something we can’t control?

May 6, 2025
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On July 16, 1945, the world crossed a threshold it could never return from. The first atomic bomb was tested, and nothing was ever the same. Today, a similar race is unfolding around Artificial General Intelligence (AGI). While the US accelerates, experts warn: this is no Manhattan Project. An uncontrolled superintelligence could be the single greatest risk to humanity’s future.

How we approach AGI will shape the next century. The question is not just how fast we can go, but whether we should.

That morning in New Mexico, as the sun rose over the desert, history split open. The Trinity test marked the dawn of the atomic age. J. Robert Oppenheimer, leader of the Manhattan Project, would later recall a line from the Bhagavad Gita:

“Now I have become Death, the destroyer of worlds.”

Today, the US stands at another such inflection point. This time the target is not a bomb, but the most powerful form of AI ever imagined: Artificial General Intelligence. Unlike narrow AI systems, AGI refers to machines capable of performing any intellectual task a human can. Think of a system that can write poetry, diagnose illness, and make complex political decisions, all at once.

So, is the AGI race another Manhattan Project moment? Or is this speed a dangerous miscalculation?

The Illusion of a Clear Target

The Manhattan Project had one goal: build a bomb. The scientists involved understood the physics, had a clear plan, and could measure progress.

AGI is different. There is no fixed target, no shared definition of what “success” looks like. What do we mean by intelligence? High scores on standardized tests? Artistic ability? Empathy? Without clear benchmarks or consensus, AGI becomes a moving target.

And while nuclear science relied on observable physical phenomena, AGI’s foundation is more ambiguous. How will we know when we’ve succeeded, if we don’t even know what we’re measuring?

Why the US Is in a Hurry

In Washington, AGI is increasingly seen through a geopolitical lens. Rising competition with China has heightened the sense of urgency. In 2023, the US-China Economic and Security Review Commission submitted a report to Congress urging massive investment in AGI, likening it to a modern-day Manhattan Project.

OpenAI co-founder Greg Brockman has called for rapid expansion, leading huge supercomputer efforts while publicly pushing for acceleration. Under the Trump administration, this momentum intensified. AGI is now seen by some as a strategic weapon, and the US seems unwilling to fall behind.

The Risk of the Wrong Analogy

Not everyone agrees with this approach. A group of influential voices, including Scale AI CEO Alexander Wang, former Google CEO Eric Schmidt, and Center for AI Safety Director Dan Hendrycks, published a report titled “The Super Intelligence Strategy.” Their warning is clear:

“Moves to develop a super weapon will pressure rival states to respond aggressively, increasing global instability. Let’s not forget, the Manhattan Project didn’t lead to lasting peace.”

Their concern is that framing AGI as an arms race, something to win at all costs, may lead to the development of systems too powerful to control. And the world won’t have the luxury of second chances.

From the report:
"Launching a Manhattan Project for AGI assumes rivals will quietly accept long-term imbalance or devastation. But that assumption is flawed. A project aimed at dominance is likely to provoke countermeasures, escalating tension and undermining global stability.”

Schmidt’s name on the report is especially notable. Not long ago, he had been an outspoken advocate of aggressive US competition with China in advanced AI. In a recent essay, he even described DeepSeek as a turning point in that race.

A New Concept: Mutual AI Failure

The report introduces another key idea: Mutual AI Failure. This describes a scenario in which rival nations build hostile AGI systems, refusing to shut them down, leading to a new kind of uncontrolled arms race.

The Pentagon has already begun integrating AGI into military planning. China and Russia are closely observing, and rapidly building their own systems. As this escalates, AGI becomes not a shared scientific endeavor, but the frontline of a new cold war.

The Third Way: Responsible AGI Strategy

According to the report, today’s AI politics fall into two extremes. On one side, the doomsayers believe the only solution is for all countries to slow down. On the other, the optimists insist development should speed up, assuming good outcomes will follow.

The authors argue for a third path. Instead of obsessing over “winning,” nations must focus on building systems that are controllable and safe. The US, they say, should lead not by racing ahead, but by discouraging risky development elsewhere.

That means expanding cyber capabilities to neutralize adversarial AGI projects, and tightening access to advanced chips and open-source models. In other words, security first, not just supremacy.

A Civilizational Choice

Personally, I believe unchecked AGI development could become a technological disaster if we are not careful. Unlike nuclear weapons, once control over AGI is lost, getting it back may prove impossible.

A super intelligent system would influence decisions that shape every aspect of life. And we’ve already seen how basic, rule-based algorithms, for instance social media algorithms, can impact behavior and society. If even those systems can distort our lives, what happens when we hand the steering wheel to something vastly more capable?

History has shown this again and again. Rushing into power without responsibility carries immense cost. In the age of AI, we must remember the lesson of Oppenheimer.

The road to disaster is often paved with ambition and good intentions.

And this decision may end up in the hands of leaders like Donald Trump and Xi Jinping.

How we handle AGI will define the century ahead. Will we charge ahead blindly, or proceed with care?

China and the US are making their moves.

The rest of us are watching, holding our breath.

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AI Dictionary

What Is AI Model Deployment? Cloud, On-Premise, Hybrid Explained

Understand ai model deployment and how cloud, on-premise, and hybrid setups affect control, speed and compliance.

May 5, 2025
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Choosing the right AI model is just the beginning. The real value begins when that model is actually in use, supporting your team, automating decisions, and powering real-time results. That’s where ai model deployment comes in.

It’s the bridge between innovation and execution. Whether you're automating customer support, analyzing financial documents, or creating AI agents, how and where your model is deployed determines how effective it can be.

In this blog, we’ll unpack what ai model deployment really means, walk through the three main deployment strategies — cloud, on-premise, and hybrid — and help you understand which setup makes the most sense for your organization.

What Is AI Model Deployment?

AI model deployment is the process of making a trained model operational. It moves the model from testing and experimentation into a real-world environment where it can process inputs, generate outputs, and serve users.

This involves:

  • Hosting the model somewhere (in the cloud, on-premise, or a mix)
  • Connecting it to your business systems, interfaces, or agents
  • Ensuring it responds reliably and securely
  • Monitoring for performance, version control, and fallback behavior

Once deployed, the model becomes a live service. It's no longer just potential, it's embedded into operations, decisions, and customer interactions.

Why AI Model Deployment Is a Strategic Decision

How you deploy a model affects more than infrastructure. It shapes your user experience, compliance posture, and total cost of ownership.

Key factors impacted by deployment choice:

  • Latency: How fast your system responds to user inputs
  • Data privacy: Where your data travels, and who handles it
  • Scalability: How easily your system grows with demand
  • Customization: Whether you can fine-tune or configure the model
  • Cost: Infrastructure, API usage, maintenance, and bandwidth

For example, a cloud-based model might be cheaper at first but become costly at scale. An on-premise setup might meet strict compliance rules but require IT resources to manage.

That’s why ai model deployment is rarely just a technical decision. It’s a balance of speed, control, security, cost and it should align with your goals.

The Three Main Deployment Strategies

Most enterprises deploy AI models in one of three ways, each with distinct strengths.

Cloud Deployment

Here, the model runs on a third-party platform and is accessed via API. This is the most popular option for teams getting started quickly or without dedicated infrastructure.

Benefits:

  • Quick setup, no server management
  • Automatic updates and scaling
  • Pay-as-you-go pricing model

Considerations:

  • Data travels outside your environment
  • Response times may vary under high load
  • Limited ability to audit or customize the model

This type of ai model deployment works well for early-stage teams, non-sensitive use cases, or when speed to market is a priority.

On-Premise Deployment

With this approach, the model runs within your own private infrastructure — either on local servers or a secured private cloud.

Why teams choose it:

  • Full data control
  • Higher compliance and privacy
  • Ability to customize, tune, and inspect models
  • Stable performance independent of external networks

But it also requires:

  • Upfront investment in infrastructure
  • DevOps and MLOps resources to manage the system
  • Careful planning to scale and maintain

On-premise ai model deployment is common in finance, healthcare, and government where trust, compliance, and control are critical.

Hybrid Deployment

Hybrid means using a combination of cloud and on-premise systems. It allows you to match each workflow to the most appropriate environment.

For example:

  • General requests go through a cloud-hosted model
  • Sensitive data or region-specific tasks are handled locally
  • One agent calls a local model, while another uses a remote one

Why hybrid works:

  • Flexibility to balance cost and control
  • Easier compliance management
  • Less risk of vendor lock-in
  • Supports multi-region or global architectures

This style of ai model deployment is growing fast, especially for companies with distributed teams or mixed security needs.

How to Choose the Right AI Model Deployment Approach

There’s no one-size-fits-all answer. But there are a few key questions that can guide your decision:

  • What kind of data are you processing?
    If it includes personal, medical, or legal data, on-premise or hybrid may be better.
  • How fast do you need responses?
    For real-time applications like customer service, cloud can offer faster deployment, but not always better latency.
  • Who manages your infrastructure today?
    Teams with no internal DevOps support may start in the cloud and later shift as capacity grows.
  • Is flexibility a priority?
    Open-source or hybrid deployment keeps your options open and avoids being tied to a single provider.
  • Are you preparing to scale?
    Costs in the cloud can spike with usage. On-premise becomes more efficient at scale.

The right ai model deployment strategy should fit your current needs and support your future roadmap.

What Hybrid Deployment Looks Like in Action

Let’s say you’re at a regional bank using AI to support small business loan applications.

Your system pulls in documents, checks credit profiles, summarizes risks, and prepares a draft loan decision. Here’s how ai model deployment would look in each setup:

  • Cloud: The full process runs through a remote API. It’s fast to set up, but every customer document travels outside your organization.
  • On-Premise: The model is hosted within your infrastructure. All data stays local, and IT manages the system. This ensures compliance but requires more overhead.
  • Hybrid: You process sensitive application data using a local model. But once a decision is made, a cloud-based model writes a customer-friendly summary for email delivery.

This layered approach lets you balance control, cost, and automation  and is similar to the hybrid use cases we describe in this article.

The Role of Open-Source in AI Model Deployment

Open-source models like Mistral, LLaMA, and DeepSeek have made ai model deployment more accessible than ever. Teams can now run powerful models locally  without being locked into a specific vendor.

Why open-source deployment is gaining traction:

  • Run models inside secure environments
  • Customize fine-tuning for specific use cases
  • Avoid API usage limits and variable pricing
  • Maintain full control over deployment and monitoring

If your organization values flexibility, privacy, or model transparency, open-source deployment is often the preferred route.

Conclusion: AI Model Deployment Is a Long-Term Choice

AI isn’t just about what models you use, it’s about how you use them. And that begins with smart, intentional ai model deployment.

Whether you're just starting with a simple cloud API or managing complex hybrid systems across departments, your deployment strategy shapes the experience, reliability, and trust behind every AI-powered result.

There’s no perfect answer for everyone. But by understanding your data, compliance needs, and team capabilities, you can make the kind of ai model deployment decisions that grow with you, not against you.

Start with what fits now. Plan for what comes next. And treat deployment not as a backend task, but as the infrastructure of your AI success.

Frequently Asked Questions

What’s the easiest way to get started with ai model deployment?
Cloud deployment is usually the fastest to begin with. It lets you run models through APIs without infrastructure setup. Perfect for prototypes or first integrations.

Does ai model deployment require coding skills?
Not necessarily. Many platforms offer no-code interfaces, prebuilt workflows, or visual builders. However, advanced configurations may require technical expertise.

Is hybrid ai model deployment too complex for smaller teams?
Not at all. With the right setup, even small teams can mix local and cloud-based tools. The key is to start small and add layers only as needed.

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AI Academy

Build Your Dream Team: Using AI Agents

Build your dream team with ai agents. Automate tasks, manage workflows, and scale faster with smart assistants.

May 3, 2025
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Not every role on a team needs to be filled by a person. Some roles are better handled by smart digital teammates that work on demand, operate 24/7, and adapt fast. These digital teammates are called ai agents  and they’re becoming essential in modern workflows.

In this post, we’ll walk you through how ai agents help you scale your team without scaling headcount. We’ll cover how to organize them, what kinds of tasks they handle, and how to build your own agents with Dot.

Whether you’re in marketing, sales, operations, or product, there’s likely a process today that ai agents could own tomorrow. Let’s take a look.

What Are AI Agents and Why Do They Matter?

AI agents are autonomous systems that:

  • Understand objectives
  • Decide how to achieve them
  • Take actions independently
  • Collaborate with other agents or tools
  • Adapt to new input or feedback over time

Unlike simple bots that wait for instructions, ai agents can:

  • Handle ongoing tasks without needing constant input
  • Trigger other agents or systems when conditions are met
  • Update their behavior based on user goals or changing data

They’re not just task-doers. They’re decision-makers with context.

This means that, instead of a human needing to coordinate every detail, your ai agents can:

  • Draft a report
  • Summarize market research
  • Pull the latest sales numbers
  • Build a personalized email
  • Trigger follow-ups — all without manual oversight

And when you combine multiple ai agents, they can operate like a real team.

They’re built using large language models but go a step further. You can assign goals instead of line-by-line instructions. For example, instead of saying, “Pull the last 3 articles and summarize them,” you can just say, “Keep me updated on industry trends.” The agent figures out the how.

This gives you a new kind of worker, one that doesn't need follow-ups or nudges. Just one goal, and it’s off.

AI Agents Are Not Just Another Chatbot

Let’s get one thing straight: ai agents are not chatbots with a fancy name. A chatbot responds to inputs. An ai agent moves things forward.

Here’s the difference:

  • Chatbots need you to do the thinking; agents take initiative.
  • Chatbots work in isolation; agents can trigger other agents or apps.
  • Chatbots need prompts; agents work on goals.

That’s why businesses looking to automate real tasks are turning to ai agents instead of relying on chat-only tools.

Three Smart Ways to Structure Your AI Agents

Once you stop thinking of agents as tools and start treating them like team members, the question becomes: how should I organize them?

Here are three common structures companies are using today:

1. Function-Based Groups

You build agents for each skill or business function. For example:

  • One for data gathering
  • One for drafting content
  • One for reviewing or editing
    Each agent becomes specialized and reusable across projects.

2. Chain of Agents

Think of it like an assembly line. One ai agent performs a task and hands the result to the next agent, and so on.

A basic setup could be:

  1. Research agent gathers the data
  2. Summary agent condenses it
  3. Messaging agent turns it into a social post or email

3. Supervisor Model

This approach uses a lead agent to manage a group. The supervisor gives instructions to other agents, monitors their outputs, and collects everything into a final result. This is ideal for more complex or multi-step processes.

These models can also be combined. You might use chains within teams, or a supervisor to oversee multiple parallel agents. It’s flexible and easy to iterate.

Where AI Agents Work Best

AI agents are most effective when used in workflows that are repetitive, logic-based, or time-sensitive. Let’s break it down by department.

Marketing

  • Create blog outlines and summarize competitor content
  • Generate social copy variations
  • Track campaign performance and report results

Sales

  • Draft email follow-ups tailored to CRM entries
  • Score inbound leads based on activity
  • Summarize call transcripts for next steps

Operations

  • Generate recurring reports from databases
  • Monitor system statuses and flag anomalies
  • Handle internal ticket routing

HR and Legal

  • Review resumes and highlight top matches
  • Summarize policy documents
  • Help with compliance checks and reporting

Once you see results in one area, it becomes easier to identify other repetitive tasks that ai agents can take over.

Getting Started: Build AI Agents in Dot

You don’t need to code to create useful agents. Our product Dot gives you two easy ways to get started:

  1. Focused Mode
    • You give one agent a single clear task
    • Ideal for research, summarization, or content generation
    • Choose the model and data source, Dot handles the rest
  2. Playground Panel
    • Combine multiple agents into a team
    • Set up supervisor or chain workflows
    • Test how agents interact and fine-tune the flow

Want a real-world example? Here’s a common use case:

Weekly Competitive Summary

  • Input: List of competitor websites
  • Step 1: Research agent pulls updates
  • Step 2: Analyst agent highlights pricing and messaging changes
  • Step 3: Report agent creates a slide-ready summary
  • Step 4: Delivery agent sends the report to your inbox every Monday

And if you’re looking for a full walkthrough, we’ve got you covered: Agent Creation 101: Turn Manual Workflows Into Autonomous Routines

Why Companies Prefer AI Agents Over Traditional Tools

Tools follow rules. AI agents follow intent.

That distinction matters. A static automation tool is great if the input never changes. But the moment you need adaptation — different formats, inconsistent timing, unique phrasing, static tools break. AI agents adapt.

Companies also appreciate that:

  • Agents are reusable across workflows
  • They can be trained with company-specific data
  • They integrate with existing platforms
  • They get better with feedback

And because Dot supports multiple models, you’re not locked into one approach. You can choose the right level of power, speed, or privacy depending on the task.

Small Start, Big Results

Here’s how most teams successfully introduce ai agents into their workflow:

  • Start small: Pick one task, like summarizing customer calls
  • Choose one agent: Build and test using real data
  • Measure: Track time saved and result quality
  • Share wins: Show team members the outcomes
  • Scale up: Add agents for related tasks

This bottom-up approach helps everyone build trust in the system. Once you’ve done it once, it becomes second nature to identify new places where agents can help.

Frequently Asked Questions

How are ai agents different from AI chatbots or assistants?
AI agents are proactive and can work in teams. Unlike assistants that just respond to prompts, agents take initiative, manage workflows, and complete tasks across tools.

Can I trust ai agents with sensitive tasks like reporting or customer replies?
Yes, especially when using a platform like Dot that supports permissions, review steps, and agent-level supervision. You stay in control of the final output.

Do ai agents require training every time I use them?
Not at all. Once configured, ai agents operate using saved workflows and logic. You can update them, but you don’t need to reprogram each time.

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Podcast

Our First Podcast Episode Is Live! And We Have a Story to Tell

What does it take to build from scratch? In Episode 1, the Novus founders share what kept them going.

April 30, 2025
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The first episode of ‘’Açık Kaynak’’, presented by Novus, is now live on Spotify and YouTube!

In this pilot episode, Novus co-founders Egehan and Vorga sit down for an open, unscripted conversation about how their journey began from unlikely beginnings to building an AI company focused on agent orchestration.

We called it "Açık Kaynak" not just for its technical roots, but because it stands for something we believe in: being open, sharing the process, and keeping the conversation real.

We recorded the full episode in Turkish, which you can listen to on Spotify or YouTube. Or, if you prefer, you can continue reading this blog post in English, where we've gathered some highlights from what Egehan and Vorga discussed in the first episode.

Açık Kaynak is now on Spotify!
Açık Kaynak is now on Spotify!

Why Launch a Podcast?

Running a fast-growing AI company is no small task.
Between product development, partnerships, and building a team, launching a podcast might seem like an unlikely priority.

But for Egehan and Vorga, the decision felt natural.

As the AI scene rapidly grew in Turkey, they noticed something missing: a space for honest, accessible conversations about artificial intelligence without the heavy technical jargon or empty hype.

At one point in the conversation, Vorga sums it up perfectly:

"We realized there wasn't enough deep, genuine conversation about AI here. If we can inspire even a few people, it’s worth it."

Açık Kaynak was born out of a simple idea:
To share real experiences, and to explore what AI means not just for engineers, but for anyone building a business, navigating a career, or dreaming about the future.

And as they joke with a laugh, maybe it’ll save them from explaining AI orchestration in every sales meeting too.

The Realities of Startup Life

One of the most refreshing parts of the episode is how openly the founders talk about the real, often messy side of building a startup.

Throughout the conversation, they revisit key moments, the long nights, the growing pains, the personal sacrifices, and reflect on what those moments really meant to them.

Egehan recalls his earlier attempts at starting a startup during university, where he handled everything alone. Even in Novus’ early days, both founders were working around the clock to get things off the ground.

“Building something meaningful means doing a lot of things you never thought you would do. And it means doing them anyway,” Egehan shares.

It’s this kind of unfiltered honesty that sets the tone for the podcast, not just celebrating what worked, but being real about what it took to get there.

Milestones That Matter

Of course, not all memories are about the struggle.
Some moments have been truly defining reminders that their once-crazy idea had turned into something real.

Both founders light up when they recall a moment from their time in the Venture Lane program in Boston, during a dinner gathering with leaders from across various industries.

After being selected for this exclusive entrepreneurship program, they were introduced to a room full of influential people and publicly recognized for building something revolutionary.

“Being introduced to a room full of influential people and hearing them say we were working on something revolutionary... It was one of those moments you never forget,” Egehan shares.

Later that same week, they met the Chairman of Starbucks through another program event.

He told them without studying at MIT or Harvard they had still made it to Boston, were building something meaningful, and had even been featured in the Boston Globe as one of the Top 10 local startups. He described this as both a big achievement and a bit of luck.

Vorga recalls how excited they were after that meeting especially when the chairman personally shared his email and contact details with them before leaving.

Novus, the Community, and the Future of AI

After describing their own successes and challenges, they also talk about Novus.

They explain that Novus' mission has evolved beyond product development into something bigger: building a community. They talk about creating ecosystems where Turkish AI entrepreneurs, not only in Turkey but around the world, can share knowledge, resources and support.

Through projects such as Novus Research, they also aim to contribute to the academic and open source world and combine business needs with research innovation.

Open source development is at the heart of modern artificial intelligence.

“Today's AI breakthroughs no longer happen behind closed doors. They're being built by thousands of people contributing to open projects, and that's a very powerful thing,” says Egehan.

They see Novus not just as a company, but as a participant in a much larger movement towards democratized AI innovation.

Our CEO Egehan and our CRO Vorga talk about entrepreneurship, AI and technology.
Our CEO Egehan and our CRO Vorga talk about entrepreneurship, AI and technology.

What Comes After Novus

As the episode draws to a close, the conversation shifts toward the future  both for Novus and for themselves.

There’s the classic founder dream of a successful exit, of course. But more importantly, they talk about the hope of seeing what they’ve built used around the world.

“I dream of one day writing a book, creating content, creating things that make people think, feel, and connect, that’s what drives me.” Vorga shares a more personal goal.

Egehan echoes the same sentiment, envisioning a future where they continue building new projects, new communities, new ideas, long after Novus' story reaches its final chapter.

Final Thoughts

The first episode of Açık Kaynak sets a refreshingly honest tone for the series.
It’s not about AI buzzwords or glorifying startup life. It’s about real people, chasing real dreams, building real technology, and trying to do it in a way that truly matters.

As the episode wraps up, there’s a clear sense that this is just the beginning not only for Novus, but for the wider AI ecosystem they hope to contribute to.

One thing is certain: the future they’re building isn’t just about machines.
It’s about people and the connections they make along the way.

As mentioned earlier, this blog post offers just a brief recap of the episode.
If you’re curious to hear more from Vorga and Egehan, you can listen to the full conversation on Spotify or YouTube.

Açık Kaynak is just getting started!

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Newsletter

Novus Newsletter: AI Highlights - April 2025

Dot is live! Plus: Açık Kaynak videocast launch, ChatGPT politeness costs, and Novus’ inspiring moments this month.

April 30, 2025
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Hey there!

Duru here from Novus, and I’m excited to bring you the highlights from our April AI newsletters. As Dot officially goes live and AI headlines continue to surprise, this month has been packed with launches, lessons, and a few quirky turns in the world of artificial intelligence.

From our own product launch and new videocast series to a full AI-edited newspaper, I’ve rounded up the most noteworthy moments and insights to keep you in the loop.

If you’d like to stay even more up to date, don’t forget to subscribe to our bi-weekly newsletter for the latest stories and behind-the-scenes updates from Novus.

Now, let’s dive in!

April 2025 AI News Highlights

Our Beloved All-in-One AI Platform Dot Is Live!

Dot is finally here, and it’s everything we hoped for. Whether you want to let Dot pick the best model for your task or choose from GPT-4, Claude, Mistral, and DeepSeek yourself, it’s all just a click away.

Even better? You can build your own AI agents without writing a single line of code. Connect them into workflows, integrate with tools like HubSpot and Notion, and let them handle the heavy lifting in the background.

Key Point: Dot brings models, agents, and integrations together in one place to make enterprise AI more usable, flexible, and powerful.

🔗 Further Reading

Being Polite to ChatGPT Is Costing Millions

Sam Altman recently shared that those extra “please” and “thank you” messages we send to ChatGPT add up, costing OpenAI tens of millions in compute power.

It’s funny, but also a reminder: even small inputs consume real resources. And as AI scales, the environmental impact grows too.

Key Point: OpenAI reports that polite language is driving millions in extra compute costs, prompting questions about AI’s hidden energy footprint.

🔗 Further Reading

Novus Launches Açık Kaynak: A New AI Videocast Series

This month, we launched our new videocast, Açık Kaynak. Hosted by our co-founders Egehan and Vorga, it’s all about honest conversations in AI, covering global trends, startup life, and the stories that don’t usually make it to stage.

If you enjoy our newsletter, you’ll probably enjoy Açık Kaynak too.

Key Point: Açık Kaynak is Novus’ new AI-focused videocast series, bringing open, personal, and global conversations to the forefront.

🔗 Watch on YouTube

Italian Newspaper Hands Over the Pen to AI

In a bold experiment, Il Foglio handed its entire Friday edition over to GPT-4, complete with witty headlines, fake interviews, and unexpected irony. Readers loved it, and the issue sold 20 percent more than usual.

It’s a glimpse into what editorial workflows might look like in an AI-powered media world.

Key Point: Italy’s Il Foglio let GPT-4 write an entire issue, boosting sales and offering a provocative glimpse at AI in journalism.

🔗 Further Reading

Novus Updates

It’s been another busy and inspiring couple of weeks for the Novus team.

Novus Team at Darüşşafaka Eğitim Kurumları for INNOMAX'25

INNOMAX '25 Young Entrepreneurs Talent Workshop

We spent a day with the brilliant students of Darüşşafaka Eğitim Kurumları, where our co-founder Vorga gave a keynote on entrepreneurship. Afterwards, we mentored student groups as they developed startup ideas at the intersection of AI and sustainability.

Watching these young minds in action was truly energizing, and Vorga also had the honor of serving on the jury to select the top idea. The future is in good hands.

BAU Future AI Summit

We had an amazing two days at the Future AI Summit, hosted by BAU Hub and BAU Future Campus. From students to investors to leaders from top companies, it was an incredible opportunity to introduce Dot to such a wide audience.

On the second day, we also shared the Novus story during AI Startup Demo Day, thanks to a kind invitation from Lima Ventures.

Events like these remind us why we’re here: not just to build technology, but to shape a future grounded in knowledge-sharing, collaboration, and curiosity.

Educational Insights from Duru’s AI Learning Journey

Why Smarter AI Isn’t Just About the Tech

When people talk about improving AI results, the conversation usually jumps to the model.

Which one are you using? Is it faster? Cheaper? Did you try the new release yet?

But the more time I spend with AI tools, the clearer it becomes:

Better results don’t just come from better technology. They come from better communication.

Enter prompt engineering.

It’s not about coding, it’s about crafting the right instruction to get the AI to respond more accurately, creatively, or reliably. You’re steering the model with your words, not changing its architecture.

This became especially clear when OpenAI’s GPT-3 paper Language Models are Few-Shot Learners showed how small tweaks to input transformed model behavior. Then came Chain of Thought prompting from Google, proving that simply asking a model to “think step-by-step” could significantly improve reasoning.

Today, prompt engineering is a real skill set.

A few things I’ve learned that really help:

  • Be specific. Tone, format, goal—spell it out.
  • Give examples. Show what you want. It helps guide the model.
  • Break it down. Use steps or structure to guide longer tasks.
  • Experiment. Rewording a prompt can change everything.

Bottom line:

In a world where everyone has access to the same models, your advantage comes from how you use them.

And that begins with the questions you ask.

Looking Ahead

As AI evolves, so should the way we work with it. Whether it’s building smarter workflows or asking better questions, there’s always something new to learn.

If you’d like to keep following along—and maybe get a few ideas for your own AI journey—make sure to subscribe to our newsletter. You’ll get updates, insights, and behind-the-scenes stories from our team, straight to your inbox.

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Newsroom

Dot in Action at Zorlu Holding’s AI Leaders Event

We joined Zorlu Holding’s AI Leaders event to present Dot, share real-world use cases, and connect with industry leaders.

April 29, 2025
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We had the pleasure of attending Zorlu Holding’s special gathering, “Geleceğini Yaz: Yapay Zeka Liderler Buluşması,”an event that brought together forward-thinking professionals to explore how artificial intelligence is driving meaningful change across industries.

It was an honor to be part of a program where AI was discussed not as a trend, but as a transformative force. The conversations throughout the day were focused, thoughtful, and grounded in real-world impact just the kind of energy we love to be around.

During our presentation, our CEO Rıza Egehan Asad offered a brief perspective on AI’s evolving role in business, followed by our CRO Vorga Can, who shared real-life use cases showing how Dot is being used to streamline operations and empower teams through intelligent workflows.

Our CEO Rıza Egehan Asad and our CRO Vorga Can talk about AI and Dot.

After the session, our Head of AI, Halit Örenbaş, and Senior Machine Learning Engineer, Rehşan Yılmaz, welcomed guests to the Dot experience area, guiding attendees as they explored the product hands-on. Meanwhile, Doğa Su Korkut, our Community Manager, ensured the flow of communication and engagement throughout the day.

Team experiences like this always leave a lasting impression and this one was no exception. A heartfelt thank you to Zorlu Holding, and especially to Ayşe Nisa Akgün, for the invitation and warm welcome!

Our CEO Rıza Egehan Asad and our CRO Vorga Can continue their presentations
Our CEO Rıza Egehan Asad and our CRO Vorga Can continue their presentations.

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