We’re excited to launch our new event series: Novus Meetups.

At Novus, we believe that building a strong community is just as essential as building great technology. As one of Turkey’s first AI-native startups, we see community not just as an audience, but as collaborators in shaping the future of AI.

That’s why we’re kicking off a series of gatherings that bring together people from different teams, sectors, and roles. Novus Meetups are designed to create space for real conversations around AI, knowledge-sharing, professional exchange, and meaningful connections.

Last Friday, we officially hosted our first-ever community gathering: “Novus Meetups: AI Teams & Developers Day.”

And what a beginning it was.

We would like to thank the generous support of QNBEYOND for helping us realize this dream and providing us with a great atmosphere.

The day opened with a deep-dive session by our Head of AI, Dr. Halit Örenbaş, where he explored the power of AI agents, orchestration, and the future of intelligent systems. His insights sparked thoughtful dialogue and set the stage for the collaborative spirit that defined the day.

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We ended with a casual networking session, where participants, from developers to AI leads, connected over coffee, shared ideas, and asked honest questions. That final part? It really made everything feel alive.

To everyone who came: thank you.

The energy, the conversations, the shared excitement around AI and startups, it all came together in the best way possible. You made the day what it was.

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We’re also incredibly grateful to our team:

• To our community team, for leading every part of the event with such care and heart,
• To our design team, for shaping the unique visual identity of the Meetups,
• To our sales and product teams, for making sure every guest felt genuinely welcomed during the networking session.

This is just the beginning. And we’re already excited for what’s next. Stay tuned!

Also, to stay updated on our upcoming meetups, feel free to follow us on social media and subscribe to our newsletter:

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Hey there!

Duru here from Novus, and May has been anything but slow in the AI world.

From Google dropping 100 AI announcements at I/O to Baidu working on a system to talk to animals (yes, really), this month’s updates have been equal parts ambitious and bizarre.

Meanwhile at Novus, we’ve been out in the world — from panels to summits — sharing what we’re building with Dot, and connecting with the growing AI community both locally and globally.

If you’re new here, welcome! I round up the biggest AI news, Novus updates, and personal insights in this monthly digest. You can also subscribe to our bi-weekly newsletter if you want the latest updates delivered straight to your inbox.

Let’s get into it.

May 2025 AI News Highlights

Google Goes Full Gemini at I/O 2025

Google I/O 2025 was an AI extravaganza. The company unveiled 100 new features, with Gemini models powering everything from search to smart glasses.

The most ambitious reveal? Gemini 2.5 Pro and Flash, which support 1 million-token context windows and a new “Deep Think” mode for tougher reasoning tasks.

Key Point: Google launched 100 AI features at I/O, showcasing Gemini 2.5 Pro, generative video tools, and a new $250/month AI Ultra plan.

🔗 Further Reading

Baidu Wants to Make Animals Talk

Baidu has filed a patent for a system that would use AI to interpret animal sounds and convert them into readable human language.

It’s early days, but the goal is to decode vocal patterns in pets and potentially understand what they’re trying to communicate.

Key Point: Baidu’s new AI patent aims to translate animal sounds into human language, opening up new frontiers in cross-species communication.

🔗 Further Reading

Duolingo’s AI-First Pivot Sparks Backlash

Duolingo cut 10% of its contractor workforce and announced an AI-first strategy for course content, leading to user complaints about quality.

Longtime learners, especially of Japanese, say cultural nuance is disappearing—and that AI can’t yet match the human touch in language learning.

Key Point: Duolingo’s AI-first shift led to layoffs and user backlash, raising concerns about quality and cultural accuracy in AI-generated education.

🔗 Further Reading

FutureHouse’s AI Tool Tackles Scientific Discovery

FutureHouse, a Google X spinout, launched a new AI platform that helps scientists explore biological research data without writing code.

Its first application in microbiome research allows researchers to model bacteria-human interactions more intuitively.

Key Point: FutureHouse launched an AI tool that turns biological data into interactive knowledge graphs, simplifying scientific exploration.

🔗 Further Reading

Novus Updates

The Novus team has been everywhere this month — from Istanbul to İzmir to Denver — sharing our vision for purposeful AI and showcasing how Dot is making it real.

• At Zorlu Holding’s “Geleceğini Yaz” event, our co-founders Egehan and Vorga spoke about solving real-world challenges with Dot, while Halit, Rehşan, and Doğa gave hands-on demos that sparked great conversations.
• At Webrazzi XYZ 2025, Egehan joined a panel on how AI agents are reshaping post-ChatGPT brand strategy, highlighting what sets Dot apart in this shift.
• During MEXT’s gathering for Denver’s startup ecosystem, Vorga introduced Dot to a global audience, while Duru and Sercan connected with leaders and innovators on the ground.
• At the AI Summit in İzmir, our CRO Vorga Can shared insights on building AI systems with real-world impact, in a session moderated by Ali Rıza Ersoy and hosted by ESİAD and EGİAD.
• Meanwhile, our CEO Rıza Egehan Asad attended the TRAI Mayıs Çalıştayı, exchanging ideas with local AI pioneers in a refreshing and forward-thinking environment.

Each event reminded us why we do what we do: to build technology that doesn’t just impress — but actually empowers.

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Educational Insights from Duru’s AI Learning Journey

Why Telling Chatbots to Keep It Brief Can Backfire

We all want quick answers. But a new study from Paris-based Giskard reveals that asking AI for short replies might actually lead to more hallucinations. The research, tested on GPT-4o, Claude 3.7 Sonnet, and Mistral Large, found that when brevity is prioritized, accuracy suffers.

Why? Short answers leave no room for nuance. The AI might sense something’s off in the question, but without space to explain or clarify, it just blurts out an answer — confidently wrong.

This matches findings from another recent paper, Calibrating Verbal Uncertainty, where researchers reduced hallucinations by 32% simply by adding phrases like “it’s likely” or “this may be.”

So next time you prompt an AI, consider this: giving it space to elaborate might be the smartest move.

🔗 Further Reading

Is the Internet Still Human?

Have you ever read something online and thought, “This feels a little… machine-made”?

You’re not imagining things. According to Amazon Web Services, 57% of all online text is now generated or translated by AI. A separate arXiv study puts it at 30% minimum, and that number is growing fast.

Some analysts believe that by 2026, 90% of web content will be AI-generated (The Living Library).

That’s wild. It raises serious questions: How do we tell what's real? Can we still trust what we read? Is creativity getting flattened into algorithmic sameness?

I use AI in my own work, and I love what it can do — but I also try to stay aware. I still think it matters when something is written with intention, by an actual person. So for now, I’ll keep writing my own words — slowly, but meaningfully.

And I hope we all keep asking: who’s really behind what we’re reading?

Until Next Time, Stay Curious

That wraps up this month’s AI learning journey from my corner of Novus. If you’ve made it this far, I hope it gave you a fresh perspective or at least a few things to think about the next time you prompt your favorite model.

And if you want something a little more fun than a newsletter (yes, I said it), head over to our YouTube channel and check out Açık Kaynak — our podcast where we talk about AI, startups, and the messier side of building in public. It’s honest, unfiltered, and way more casual.

See you next month!

Can artificial intelligence truly act with fairness, or is it driven by the biases embedded in the data it consumes? Do AI-driven loan decisions undermine equal opportunity? And perhaps the most important question of all, who is responsible for solving these problems?

"A robot may not harm a human being, or, through inaction, allow a human being to come to harm. A robot must obey the orders given by humans, except where such orders conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law."

These are Isaac Asimov’s famous Three Laws of Robotics, introduced in the 1940s in his science fiction classic I, Robot. At the time, they belonged to a distant imaginary future. But today, with real-world advancements in AI and robotics, Asimov’s once-speculative ideas are becoming more relevant than ever. As AI becomes integrated into everything from corporate strategy to national security, the question of ethics has become just as critical as technological capability. And while Asimov’s laws seem practical, ethical AI in the real world is a much more tangled challenge.

This article explores key dimensions of AI ethics, algorithmic bias, fairness, automated decision-making, accountability, and privacy. Regulations are also crucial, but I’ve addressed those separately in a previous article.

Algorithmic Bias: The Hidden Trap in AI Systems

When historical injustices are transferred into AI systems via training data, we call it algorithmic bias. One of the most cited examples is Amazon’s hiring algorithm, developed over a decade ago. According to a 2018 report by Reuters, the system favored male candidates over women because it had been trained on data dominated by male applicants. The model internalized the male-dominated hiring patterns of the past and reproduced them. Technically, the system worked as intended, but the result was ethically unacceptable. Amazon scrapped the project, but the lesson remains: AI can mirror and magnify bias.

Since 2018, AI systems have grown more complex, but problems like biased datasets, lack of transparency, and ethical blind spots have persisted.

Facial recognition is another area heavily impacted by algorithmic bias. MIT researcher Joy Buolamwini studied how social bias appears in facial recognition datasets, which she called "coded gaze." Many benchmark datasets, such as IJB-A, contain over 80 percent lighter-skinned faces. This imbalance leads to less accurate recognition for darker-skinned individuals, reflecting broader internet content disparities.

Is AI Opening Its Eyes to Justice?

Ethical concerns around AI are often tied to justice. This is particularly visible in criminal justice systems, where predictive models risk embedding old prejudices as new truths. In the US, predictive policing tools like PredPol direct law enforcement toward areas with historically high crime rates. These areas often overlap with economically marginalized communities. So the bias in the data gets reinforced, and disadvantaged neighborhoods remain under constant scrutiny.

In such cases, the AI system is not necessarily malfunctioning. It processes historical arrest data, demographics, and geography to conduct risk analysis. But the social impact must still be questioned. These systems can create a loop of disadvantage, more surveillance, more arrests, more labeling as "high risk."

Even courtroom decisions have seen AI’s influence. Judges may rely on risk assessment tools when determining bail or detention. But if the training data reflects systemic bias, then certain groups are consistently tagged as high risk. This intersection between algorithms and justice must be examined carefully.

Will AI Approve Your Loan?

AI is rapidly transforming decision-making in sectors like finance and HR. From credit scoring and insurance risk to recruitment and customer support, algorithms are stepping in. These tools bring efficiency, but ethical scrutiny often lags behind.

Take a bank that uses an AI-based scoring system for loan applications. It analyzes payment history, employment status, or even social media behavior to make decisions in minutes. It sounds efficient, but applicants often don’t know why they were rejected.

These systems, sometimes described as "black boxes," do not always explain how decisions are made or what variables were most influential. Technical complexity and business confidentiality often keep the internal mechanics hidden from users.

This opacity creates a trust gap. And when the training data includes biased or flawed inputs, individuals may be unfairly evaluated. A well-designed system can process millions of cases, but without transparency and recourse, the risk of widespread financial harm grows.

Accountability: Who Is Responsible When AI Fails?

When people are harmed by algorithmic decisions, questions about responsibility become difficult. A rejected job candidate or a falsely flagged defendant may not even know who to hold accountable. In human interactions, fault can be traced more easily. But AI systems make the line of responsibility blurry. So if an AI system makes a wrongful decision, who is accountable? The software engineers? The data scientists? The company that deployed the model?

Consider a self-driving car making a fatal error. Should responsibility fall on the passenger, the automaker, the software team, or the regulatory agency?

A well-known example is the 2018 death of Apple engineer Walter Huang. His Tesla’s autopilot system crashed into a highway barrier. While the system failed, Huang was also distracted, reportedly playing a game on his phone. Tesla had already stated the system wasn’t fully autonomous and required driver attention. In the end, fault was shared between the manufacturer, the user, and regulatory gaps. The lawsuit was settled in April with an undisclosed payment.

Cases like this reveal how our legal systems, still designed around human accountability, struggle to adapt to AI’s complexities. AI systems bring unpredictable errors and multilayered responsibility. We urgently need a legal framework that includes an AI-aware perspective.

Privacy: Fragile Boundaries in a Data-Hungry World

AI systems run on data, much of it deeply personal. Smartphones, social media, wearables. All generate enormous volumes of data. For AI, this is gold. For privacy, it’s a growing concern.

Take AI in healthcare. Systems used for diagnosis and treatment may collect complete health histories, genetic information, and lifestyle data. In the right hands, these insights can drive medical breakthroughs. In the wrong hands, they can be misused affecting insurance premiums, hiring decisions, and more.

Despite efforts like the European Union’s GDPR, data privacy laws still vary widely between countries. A universal privacy standard remains elusive. Meanwhile, users continue trading personal data for convenience, and businesses leverage it for competitive edge. In such a context, stopping data collection entirely is unrealistic.

Who Bears the Burden of Responsibility?

Asimov’s robot laws were clean and simple: do no harm, obey, preserve yourself. But today’s AI doesn’t operate in the realm of fiction. It’s embedded in global economies and social systems. We are not talking about positronic brains, but about algorithms influencing billions of lives.

Ethics in AI isn’t just a topic for engineers or technologists. It concerns HR departments, government agencies, app developers, and multinational corporations. Building ethical AI means ensuring transparency, traceability, and accountability at every level. It also requires identifying data-driven bias early, and equipping development teams with a critical lens.

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If your workday starts or ends with updating a report, you are not alone. Weekly metrics, sales summaries, performance dashboards , reporting takes time, attention, and repetition. And for most professionals, it is a task done more for obligation than insight.

Now imagine offloading that task to an assistant who never forgets a metric, formats everything properly, and even highlights what matters most. That is what an ai report generator does.

AI is transforming how teams manage data and communicate results. Instead of filling in spreadsheets or compiling updates manually, companies are turning to intelligent systems that write, visualize, and deliver reports automatically.

In this blog, we explore how an ai report generator works, where it fits best, and what kinds of teams benefit the most whether you're in sales, marketing, HR, finance, or operations.

Why Reporting Still Feels Like a Chore

Let’s face it,  reporting isn’t the problem. It’s how it gets done. Every week or month, someone has to:

• Pull data from multiple sources
• Clean and validate the numbers
• Build charts or slides
• Write commentary
• Share with the team or clients

The task is repetitive, yet high-stakes. A minor error can mislead stakeholders or damage credibility. And even when done right, it pulls talent away from higher-value work.

That’s where the ai report generator changes the game.

What Is an AI Report Generator?

An ai report generator is an intelligent system that automates the creation of written and visual reports. It connects to your data sources, pulls key metrics, interprets patterns, and turns them into readable summaries, dashboards, or documents.

This is not just a dashboard tool. A good ai report generator can:

• Interpret performance trends
• Highlight anomalies
• Generate next-step suggestions
• Explain data shifts in natural language
• Customize output for different audiences

The result is a report that is not just complete but also communicative, saving hours while improving clarity.

Where AI Report Generators Add the Most Value

Different teams rely on different types of reports, but the core frustrations are the same. That’s why ai report generator tools are showing up across departments.

1. Sales Reporting

Weekly revenue performance, lead conversion rates, deal pipeline health, all of it can be tracked and summarized by AI.

A sales ai report generator can:

• Pull CRM data and highlight top-performing reps
• Summarize stuck deals
• Flag missed quotas
• Offer coaching insights based on activity data

You can also explore how reporting fits into the broader sales workflow in AI for Sales Teams: From Lead Scoring to Closing Deals.

2. Marketing Reports

Marketers track dozens of metrics across platforms. Instead of updating decks or dashboards manually, a marketing ai report generator can:

• Pull campaign performance data
• Compare week-over-week or month-over-month growth
• Summarize ad spend and ROI
• Recommend optimizations based on engagement

The AI does the heavy lifting so your team can focus on what’s next, not just what happened.

3. HR and People Analytics

Whether it’s headcount changes, retention trends, or engagement survey results, HR professionals often spend hours pulling people data into readable formats.

With an ai report generator, HR can:

1. Create onboarding or offboarding summaries
2. Track and report DEI metrics
3. Automate pulse survey analysis
4. Generate leadership updates on team health
5. Visualize turnover and hiring trends

Now HR leaders can focus on action, not formatting.

4. Finance and Operational Reporting

Finance teams juggle spreadsheets, forecasts, budgets, and actuals. What if the reporting layer was automated?

An ai report generator can:

• Compare forecast to actual
• Break down expense categories
• Generate department-specific spend summaries
• Alert stakeholders to budget anomalies
• Track KPIs across business units

By eliminating manual consolidation, finance can move faster and with more confidence.

How an AI Report Generator Actually Works

Let’s break down what happens behind the scenes when you use an ai report generator.

Core process:

1. Data integration
   The system connects to tools like CRMs, ERPs, analytics dashboards, spreadsheets, or databases.
2. Data processing
   It cleans, filters, and checks for completeness. No more broken Excel formulas.
3. Analysis and insight generation
   Machine learning models identify trends, detect anomalies, and highlight outliers.
4. Narrative generation
   A large language model translates metrics into plain-language summaries.
5. Formatting and delivery
   The report is exported to a format of your choice , slide deck, PDF, email, or even interactive dashboard.

The result: a professional-grade report generated with consistency and speed.

Top Benefits of Using an AI Report Generator

Here are five concrete reasons teams are adopting ai report generator tools:

1. Save hours every week
   What used to take 3 to 6 hours now takes 3 to 6 minutes.
2. Reduce human error
   Automating analysis removes risks like broken formulas or incorrect filters.
3. Standardize communication
   Every team receives updates in the same structure and format, reducing confusion.
4. Scale reporting without hiring
   More reports do not require more people when AI does the heavy lifting.
5. Improve decision-making
   Clear summaries help stakeholders understand what matters,  not just what changed.

What to Look for in an AI Report Generator

Not all AI tools are created equal. When evaluating options, look for features that ensure reliability and flexibility.

Must-haves include:

• Compatibility with your data sources
• Natural language generation (NLG) capabilities
• Customizable templates
• Role-based access and sharing options
• Explainability and audit logs

A good ai report generator should act like a trusted analyst not just a fancy export button.

Real Examples of AI Report Generators at Work

Let’s look at a few fictionalized but realistic examples of how teams use AI to generate reports in daily operations.

Sales Manager in B2B SaaS

Needs weekly performance updates. Her ai report generator:

• Pulls CRM data
• Tracks deals by rep
• Highlights risks
• Emails the full summary to the VP every Monday morning

HR Director at a Manufacturing Firm

Needs turnover reporting for each facility. His ai report generator:

• Analyzes exit surveys
• Summarizes retention data
• Flags high-turnover locations
• Produces a visual report for HRBPs every month

Marketing Analyst at an E-commerce Brand

Needs campaign recaps. Her ai report generator:

• Pulls ad performance across platforms
• Compares costs vs revenue
• Highlights ROI by channel
• Generates a client-facing PDF automatically

In each case, the report gets done without interrupting strategic work.

The Future of AI Reporting Is Collaborative

As ai report generator tools mature, they’re not just automating reporting. They’re collaborating with humans.

Upcoming features include:

• Real-time Q&A over data
• Voice-based requests for quick updates
• Self-service dashboards powered by AI
• Predictive summaries based on patterns
• Multi-agent workflows that build reports together

AI won’t just report the past. It will help teams understand what’s likely to happen next and what to do about it.

You Can Build Your Own AI Reporter

You don’t need a massive engineering team to implement this. With the right tools, anyone can build a custom AI agent that generates reports based on your business data and workflow.

In fact, platforms like Dot make it possible to create a fully automated AI reporter that:

• Connects to your internal tools like CRM, Sheets, or databases
• Summarizes and interprets metrics on a schedule
• Delivers personalized reports to team members
• Updates dynamically as your data changes

And the best part,  you don’t need to write a single line of code.

If you want to try it out, you can create a free Dot account here and build your first reporting agent in just a few minutes.

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Conclusion: Reporting Is Not Dead, It’s Just Getting Smarter

Reporting will always matter. But doing it manually shouldn’t be the standard.

An ai report generator gives teams a way to stay informed without burning hours. It brings speed, clarity, and structure to a process that often feels scattered.

From marketing to HR to finance to sales, AI is reshaping how information is created and shared. If your team spends more time preparing reports than acting on them, it might be time to delegate that work not to a new hire, but to your AI system.

And if you think AI only works for metrics, think again. It works for momentum too.

Frequently Asked Questions

Can an ai report generator handle sensitive data?
Yes. Most enterprise-grade tools include data access controls, encryption, and user permissions. Always review how your tool manages security.

Is it difficult to train an ai report generator for our specific business?
Not usually. Most systems support template creation, custom prompts, and guided setup. Start with one use case and scale from there.

Does an ai report generator replace data analysts?
No. It removes repetitive reporting tasks so analysts can focus on deeper analysis, forecasting, and business strategy.

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Sales has always been part science, part art. But what happens when the science part gets supercharged by artificial intelligence?

That’s the new reality for modern sales teams. From prioritizing leads to writing emails to forecasting deals, ai for sales is transforming how reps work, how managers coach, and how organizations close.

This blog explores the practical ways AI is being used in sales today  not as a futuristic add-on, but as a day-to-day assistant built right into the sales process.

Whether you're running a small inside sales team or leading a multi-region enterprise org, the message is the same: artificial intelligence is no longer optional. It’s quickly becoming your best teammate.

Why AI Is a Natural Fit for Sales Workflows

Sales teams work with massive amounts of data. Every touchpoint  from a cold call to a signed contract, generates signals. The challenge is not collecting that data. It’s interpreting it, prioritizing it, and acting on it quickly.

That’s where ai for sales excels. It helps teams:

• Focus on the most promising leads
• Customize outreach based on real-time signals
• Automate repetitive tasks
• Deliver insights from CRM data
• Forecast with more accuracy

Rather than replacing sales reps, AI acts like an assistant that ensures no opportunity slips through the cracks.

Five Key Areas Where AI for Sales Makes an Impact

Let’s look at how ai for sales is showing up in the day-to-day work of high-performing teams.

1 . Lead Scoring That Actually Reflects Intent

Most sales teams use lead scoring to prioritize outreach. But static models based on firmographic data often miss what really matters,  behavior.

AI-driven lead scoring looks at:

• Email opens and reply patterns
• Website visit depth and frequency
• Content engagement
• Time spent on pricing or product pages
• Social media interaction

By analyzing intent signals in real time, ai for sales systems surface the leads most likely to convert  even if they don’t match traditional scoring criteria.

2. Outreach Personalization at Scale

AI can write better emails than you think  and faster.

Ai for sales tools can:

• Draft custom email sequences based on role, industry, or activity
• Suggest follow-up timing and subject lines
• Auto-fill product descriptions based on CRM data
• Adjust tone and message length to match contact behavior

This means your team can send more outreach with more relevance  without sounding like robots.

3. Real-Time Call Intelligence

Sales calls are a goldmine of information, but most of it gets lost or misremembered. AI brings structure to these conversations.

Sales teams use AI to:

1. Transcribe and summarize calls
2. Flag competitor mentions or pricing objections
3. Recommend talk tracks in real time
4. Score rep performance across deals
5. Sync insights back to the CRM

The result: better coaching, better messaging, and more consistent execution.

4. Forecasting That Moves With Your Pipeline

Traditional sales forecasting is time-consuming and often inaccurate. AI brings more agility and insight.

Ai for sales forecasting models analyze:

• Historical deal patterns
• Current pipeline velocity
• Win-loss trends by segment
• Team-specific pacing
• External signals like seasonality or macro factors

Instead of static reports, you get adaptive forecasts that respond as your pipeline evolves.

To see how this connects with reporting tools, check out Tired of Reporting? Let an AI Report Generator Handle It where AI automates the reporting side of sales performance too.

5. Coaching and Training Built Into the Workflow

AI is not just for automation. It’s also for enablement.

Smart ai for sales platforms provide:

• Call feedback based on top-performer benchmarks
• Micro-learning content triggered by rep activity
• Automated reminders to follow up on stale deals
• Real-time nudges during negotiations or demos
• Playbooks that adapt to deal type or stage

This transforms sales enablement from a once-a-quarter training to an always-on support layer.

What AI for Sales Looks Like in Action

Let’s walk through a typical day for a rep using AI:

1. Logs in and sees prioritized leads with intent signals and suggested first lines
2. Uses AI to generate a custom intro email
3. Gets a real-time prompt to follow up on a deal that just opened a proposal
4. Takes a call, AI transcribes, highlights objections, and syncs action items
5. Reviews updated forecast insights and watches a coaching clip tied to their last demo

This is not a future scenario. This is how modern ai for sales platforms operate right now.

1. Reviews updated forecast insights and watches a coaching clip tied to their last demo

This is not a future scenario. This is how modern ai for sales platforms operate right now.

Benefits of AI for Sales Teams

The value of ai for sales is not just speed,  it’s better results, better conversations, and better planning.

For reps:

• Less time doing admin
• Better-targeted outreach
• Instant content support
• Personalized call coaching

For managers:

• Consistent reporting
• Predictable forecasting
• More effective training
• Visibility into team performance

For organizations:

• Shorter sales cycles
• Higher win rates
• More scalable processes
• Tighter alignment with marketing

When used correctly, AI makes every part of the sales funnel smoother and smarter.

If you’re looking for a structured solution built around these outcomes, Dot Sales offers AI agents and workflows designed specifically for sales teams from lead qualification to proposal generation and everything in between.

When used correctly, AI makes every part of the sales funnel smoother and smarter.

How to Start Using AI for Sales in Your Team

If you’re new to ai for sales, start small and grow with confidence.

Here’s a simple roadmap:

1. Begin with one use case: lead scoring, email writing, or reporting
2. Use your existing CRM and connect an AI plugin or agent
3. Test outputs before scaling, keep a human in the loop
4. Track adoption and performance (response rates, forecast accuracy, etc.)
5. Expand to new workflows once value is proven

It’s better to go deep on one flow than to spread too thin across many. AI success in sales is built on focus and iteration.

How to Start Using AI for Sales in Your Team

If you’re new to ai for sales, start small and grow with confidence.

Here’s a simple roadmap:

1. Begin with one use case: lead scoring, email writing, or reporting
2. Use your existing CRM and connect an AI plugin or agent
3. Test outputs before scaling, keep a human in the loop
4. Track adoption and performance (response rates, forecast accuracy, etc.)
5. Expand to new workflows once value is proven

It’s better to go deep on one flow than to spread too thin across many. AI success in sales is built on focus and iteration.

Common Concerns About AI in Sales

AI adoption in sales is rising, but some teams still have questions. Here are the most common ones:

• Will AI replace my job?
  No. It replaces repetitive tasks, not relationships, trust, or creativity.
• Is AI accurate?
  With the right inputs and guardrails, yes. Keep humans involved where stakes are high.
• Will reps trust it?
  If it saves them time or helps them win, they will. Start with low-friction features and build from there.
• Does it require technical skills?
  Most modern tools are low-code or no-code. Sales ops can own setup, and reps just use the interface.

Conclusion: AI Is the New Sales Assistant

Selling has always been about listening, solving, and closing. Now, it also includes collaborating  with AI.

From surfacing the right lead to writing the right message to running the right forecast, ai for sales helps teams perform at their best with less guesswork.

The most successful reps of the next few years won’t just be great at closing. They’ll be great at working with AI. Because sales is no longer just about hustle. It’s about intelligence — human and artificial — working together.

Frequently Asked Questions

How does ai for sales improve lead conversion?
By identifying buyer intent signals, personalizing outreach, and guiding follow-up timing, AI helps reps focus on the most ready-to-convert leads.

Is ai for sales useful for small teams too?
Yes. Even small teams benefit from automated emails, better forecasting, and lead prioritization all without needing a dedicated analyst.

What’s the first thing I should automate with ai for sales?
Start with lead scoring or email writing. These are low-risk, high-reward workflows where AI delivers fast results.

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It’s always a joy for us to be part of La French Tech Istanbul’s events, they’ve carved out a special place in our journey.

This time, we joined their Monthly Breakfast gathering with our CRO & Co-Founder, Vorga Can, and our Community Manager, Doğa Su Korkut, representing Novus in the warm and inspiring setting of ArkHaus Community.

The panel, titled “Finding the Perfect Fit: Startups Seeking Capital, Investors Seeking Potential,” brought together sharp minds and open conversations. Moderated by Mete Bayrak, the session featured thoughtful insights from Vorga Can, Doğukan Çetin, Enis Hulli and Çağlar Yalı.. From investor dynamics to the startup-founder mindset, the discussion was as honest as it was energizing.

We’re always up for real talk, fresh perspectives, and a good dose of fun.

Huge thanks to La French Tech Istanbul and Dara Hizveren for the kind invitation. We truly value every opportunity to collaborate with this amazing community.

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AI adoption is no longer about whether to use it. It is about choosing the right structure to unlock its value. Claude and Dot represent two different visions: one focused on the raw intelligence of a single model, and the other on structured, flexible AI workflows built across multiple models.

In this post, we compare Claude and Dot not just by what they are but by what they enable. If your goal is not just intelligent answers but intelligent operations, this side-by-side breakdown is for you.

If you missed our last comparison, Dot vs Gemini offers another perspective on what real-world AI platforms can deliver.

Let us start with the part that turns AI into real results: automation.

AI Workflow: One Mind vs. Many Moving Parts

Claude is powerful. It processes long context, handles reasoning, and delivers thoughtful responses. But it is a single agent built to answer, not act.

Dot, by contrast, is designed as an AI workflow platform. That means:

• You can create multiple AI agents, each with a specific role
• These agents can work together: pulling data, analyzing results, triggering actions
• Agents can run in sequence or parallel across your tools
• No-code workflows let non-technical teams automate daily operations

With Dot, you're not using one AI to think, you're using many AIs to get things done.

Learn how Dot handles complex workflows in this detailed post

Hosting & Data Control: Flexibility vs. Dependency

Control over infrastructure and data location matters deeply for companies in regulated industries.

• Claude runs only on Anthropic’s infrastructure, hosted in the cloud.
  While it follows strong internal security standards, businesses cannot choose where or how their data is stored.
• Dot offers full hosting flexibility:
  
  • Cloud deployment through Novus
  • On-premise installation on company servers
  • Hybrid models for balancing compliance and convenience

Dot is also GDPR compliant, and gives businesses the ability to define their own security, encryption, and audit policies.

For companies where sovereignty and control are non-negotiable, Dot’s deployment options provide a real advantage.

Integrations: Working Inside Your Existing Stack

Enterprise AI cannot live in a silo. It must operate inside the tools your teams already use.

• Claude provides a great API, but has no native app integrations. Connecting it to tools like Salesforce or Slack requires developer effort.
• Dot comes ready with native integrations for:
  
  • Salesforce
  • Slack
  • HubSpot
  • Zendesk
  • Notion, Google Drive, and more

More importantly, Dot agents can take action inside those apps and not just send data, but update records, create tasks, or notify the right person.

That’s not just AI capability. That’s real business productivity.

See Dot’s full list of integrations here

Customization: For Business Users and Builders Alike

One of the biggest challenges in AI adoption is the gap between what teams need and what developers have time to build. Customization should not depend entirely on technical resources. It should be accessible to everyone.

Claude is a powerful model, but it is accessible mainly through its API.
That means:

• You need developers to set it up
• You need infrastructure to maintain it
• You need time to adapt it to your internal tools

There is no visual interface or built-in workflow builder for non-technical users. For most teams, this becomes a blocker.

Dot takes a different approach. It is built as an AI framework.

What does that mean?

An AI framework provides the tools and structure to design, build, and operate AI workflows across your organization. Instead of offering a fixed model or a narrow set of features, a framework gives teams the flexibility to build their own solutions using shared components.

Dot is a framework because:

• Non-technical users can create agents and workflows using a visual no-code interface
• Teams can define when agents run, what they do, and which apps they work inside
• Developers can extend these agents with private APIs, internal logic, and company-specific tools

For example:

• A sales team can build an agent that updates CRM records based on call summaries
• A legal team can create a workflow that reviews NDAs and flags compliance risks
• A developer can build an onboarding agent that connects HR tools with internal systems

With Dot, technical and non-technical users can build together.
You are not limited to using one AI tool. You are building your own internal AI capability.

That is what a real AI framework offers: structure, adaptability, and control across the entire organization.

Model Capabilities: Intelligence, Context, and Choice

There is no question that Claude is one of the most advanced language models available today.

Its strengths include:

• Handling extremely long inputs and conversations
• Providing consistent and well-reasoned answers
• Excelling at summarization, analysis, and structured thinking

If your goal is to use a single model to handle a wide range of complex tasks, Claude is a great choice.
However, it comes with one limitation: it is the only model you can use inside its own platform.

Dot, by contrast, is not tied to one model.
Dot is model-flexible, meaning it gives you access to multiple models in one place:

• Claude for long context and reasoning
• Mistral for lightweight, fast interactions
• Cohere for low-latency use cases
• Gemini for general-purpose tasks
• Novus original models for company-specific or regulated workflows

Inside Dot, you can assign different models to different agents depending on what you need. You can change models at any time, without rebuilding your workflows.

This means your AI workflows can adapt as models evolve. If a new model launches next quarter with better performance for your use case, you are ready to switch.

Claude is a powerful individual performer.
Dot gives you a team of AI specialists working together inside a structure that fits your business.

Pricing Flexibility: Scale with Usage, Not Limits

When evaluating AI platforms, pricing often becomes a deciding factor. Not just the overall cost, but how that cost grows with your usage and business needs.

Claude’s pricing offers several plan tiers designed for different user types:

• A Free Plan for individuals getting started
• A Pro Plan with extended usage and multi-model access
• A Max Plan for higher volume and research needs
• A Team Plan for companies needing centralized billing and admin tools
• An Enterprise Plan with custom pricing, extended context windows, SSO, and audit logs

For developers and product teams, Claude also provides usage-based API access. While flexible, the token-based billing model can become unpredictable at scale and may require close monitoring as usage grows.

Dot starts similarly — you can open an account for free, and usage is billed through a pay-as-you-go model. But Dot is designed with growth in mind, offering a structured path from experimentation to enterprise deployment.

• Start small
  For individuals or small teams:
  
  • No registration cost
  • Single-user access
  • Use AI agents and build no-code workflows
  • Collaborate using tool integrations and cloud deployment
• Scale with your team
  For growing businesses:
  
  • $250 per month
  • Up to 3 users
  • Integration with management systems
  • Access to Dot Solutions (Sales, Finance, Content, Operations)
  • Ability to run your own language model in Dot
  • Dedicated support
• Customize at scale
  For large enterprises:
  
  • Unlimited users
  • Integrate with any app
  • Tailor-made AI solutions
  • Hybrid and on-premise deployment options
  • Analytics, reporting, and real-time support
  • Custom pricing through consultation

While Claude offers flexibility for both individuals and teams, Dot provides a clearer upgrade path — designed to support long-term, company-wide AI workflow adoption.

Side-by-Side: Claude vs. Dot

Here’s a quick breakdown to close the comparison:

Conclusion: Raw Intelligence or Operational Power?

Claude is one of the strongest standalone models on the market today and for developers building apps that depend on reasoning and long context, it is a fantastic choice.

Dot takes a different path. It is not about being the smartest model. It is about giving teams the tools to automate work, move faster, and build processes powered by multiple AIs working together.

For businesses that care about AI workflow, team-level automation, and ownership of data and deployment, Dot is not just an alternative, it is an architecture.

Open your free Dot account today and start building smarter AI workflows with your team.

Frequently Asked Questions

What is the difference between Dot and Claude?

Dot is an AI framework built for workflow automation and multi-agent orchestration, while Claude is a single-model assistant focused on reasoning.

Which is better for building AI workflows?

Dot is better for AI workflow creation, offering visual tools, multi-agent systems, and native integrations for automating business processes.

Can Claude be used inside Dot?

Yes, Dot supports Claude models along with others like Mistral, Cohere, and Gemini, allowing you to assign the right model to each agent.

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The financial world has always moved fast. But the speed and scale brought on by artificial intelligence have completely reshaped what’s possible. From fraud prevention to real-time underwriting, ai in fintech is no longer a promise. It’s active infrastructure.

Today, artificial intelligence supports decision-making, improves customer experiences, reduces risk, and handles tasks that once required large teams. Financial institutions are deploying AI not as a back-office tool but as a core capability that influences how services are delivered, managed, and evolved.

In this blog, we will explore what ai in fintech actually looks like in practice. Not abstract predictions. Real tools. Real workflows. Real business value.

What Does AI in Fintech Actually Do

AI in fintech refers to the application of machine learning, language models, predictive analytics, and autonomous agents within financial products and services. While the term covers a broad field, its core impact comes from doing what traditional systems cannot.

Here is what ai in fintech helps accomplish:

• Analyze massive volumes of data in seconds
• Identify patterns humans might miss
• Automate complex decision flows
• Personalize offerings for each customer
• Reduce human error in time-sensitive operations

At its core, ai in fintech improves three pillars of the industry: accuracy, efficiency, and personalization. And the most advanced companies are seeing those pillars turn into real advantages.

Five Real Use Cases That Show AI in Fintech at Work

To understand how this intelligence is being applied, let’s walk through five real scenarios where ai in fintech is making a measurable difference.

1. AI in Fintech for Credit Decisions

Traditional credit scoring relies on fixed inputs: credit history, income, and sometimes collateral. AI looks beyond.

AI systems analyze:

• Cash flow and spending behavior
• Past repayment trends from non-traditional loans
• Signals from open banking APIs
• Social or regional data patterns

This allows fintechs to offer loans to people without perfect credit histories. It also reduces rejection rates for people who would be deemed risky by old standards but are actually reliable.

2. Fraud Prevention Through AI in Fintech

Fraud is always evolving. So are AI systems. In fact, one of the most mature applications of ai in fintech is real-time fraud detection.

AI monitors patterns across millions of transactions to detect:

• Anomalous behavior (such as new login devices or transfer patterns)
• Risky geolocation signals
• Unusual account-to-account activity
• Subtle network-based fraud attempts

What makes AI better than rules? It adapts. It learns from new behaviors. And it can reduce false positives, so real customers don’t get blocked unnecessarily.

3. Personalized Financial Tools Powered by AI in Fintech

Generic dashboards are being replaced by smart assistants. Now, when you open a financial app, it might tell you:

• You’re about to overspend based on your history
• A better investment option is available
• Your credit utilization is trending too high
• You’re eligible for a higher interest account

These experiences are built using AI models that learn from your actions  and from people like you, to deliver advice that feels personal. That is what ai in fintech looks like on the customer side.

4. AI in Fintech for Internal Workflow Automation

Behind the scenes, many teams use ai in fintech to automate repetitive but critical internal tasks.

Examples include:

1. Reading and extracting data from financial documents
2. Routing customer tickets to the right department
3. Creating compliance summaries after a transaction
4. Drafting internal risk reports
5. Populating CRM fields based on call transcripts

In many cases, these AI-powered agents act as team mates not just bots. They handle tasks that used to slow down human teams and make everything run more smoothly.

5. Customer Support with AI Agents in Fintech Platforms

AI chat is now expected. But ai in fintech is going a step further.

Instead of just answering FAQs, AI agents now:

• Understand the user’s financial products
• Pull real account data for reference
• Help users change payment schedules or settings
• Escalate complex issues to the right person
• Proactively send updates based on behavior or deadlines

This turns support from a reactive function into a proactive customer experience layer.

How Fintech Companies Adopt AI Step by Step

Most fintech teams do not roll out AI across everything at once. They take small, high-impact steps that build trust and show results.

A typical adoption pattern:

1. Identify one workflow that is repetitive and data-rich
2. Introduce AI to assist human teams (not replace them yet)
3. Measure time saved and quality improvements
4. Expand AI to handle entire workflows
5. Build orchestration between multiple agents or models

Once AI has proven value in one place, the appetite to scale it grows fast, especially when leadership sees results.

What Powers AI in Fintech Behind the Scenes

The technology stack behind ai in fintech is now more accessible than ever. You do not need to build a model from scratch. Many teams use a mix of:

• Pre-trained LLMs for communication and classification
• Prediction models for fraud or credit scoring
• APIs for language and vision processing
• Vector databases for memory and retrieval
• Agent frameworks for multi-step decision-making

You can combine open-source tools, private models, or enterprise APIs depending on your privacy and performance needs. This is explored further in our guide to Real Applications of AI in Healthcare, Finance & More.

How AI in Fintech Is Reshaping Customer Relationships

The greatest shift caused by ai in fintech may not be technical at all. It is emotional.

AI systems are:

• Answering faster than humans
• Explaining decisions more clearly
• Delivering personalized advice without judgment
• Making onboarding feel intuitive
• Offering help before it is even asked for

This changes the relationship between fintech platforms and customers. It feels less like software, more like service.

Where AI in Fintech Is Going Next

Based on how things are evolving, here is what you can expect in the near future:

1. Real-time decision-making across every user action
2. Multi-agent coordination handling entire loan cycles or onboarding flows
3. Regulatory-safe AI that can explain and log every decision made
4. Hyper-personalized interfaces that adjust based on intent
5. More open-source deployments for internal compliance and privacy

As models improve and platforms mature, ai in fintech will shift from individual tools to deeply integrated systems across every department.

Conclusion: AI in Fintech Has Moved from Concept to Core

There was a time when teams talked about AI as a future differentiator. That time is over. AI in fintech is now embedded in everyday processes and the teams not using it are already falling behind.

From automating internal workflows to creating richer customer experiences, AI is not just making things faster. It is changing how financial products are built, delivered, and improved.

If your team is still thinking of AI as an innovation project, it might be time to treat it like infrastructure. Because the most competitive fintech companies already do.

Frequently Asked Questions

How is ai in fintech different from traditional automation?
AI in fintech brings context, prediction, and reasoning to workflows. It goes beyond rule-based automation and adapts to real-time data and intent.

Does ai in fintech require deep technical knowledge to implement?
No. Many tools are no-code or low-code. Technical teams can set up systems, but product and operations teams often run them with minimal training.

Is ai in fintech safe for regulated environments?
Yes, if implemented responsibly. Most AI platforms include audit trails, access controls, and logging to ensure compliance with financial regulations.

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Many businesses are eager to bring AI into their operations. But most AI tools come with a hidden limitation: you do not control where they run, how they store data, or how deeply they integrate with your workflows. This limitation might not seem critical at first, but it quickly becomes a challenge as organizations scale, handle sensitive data, or require compliance with internal governance policies.

In this blog, we compare Dot with some of the most widely used cloud-only AI tools including ChatGPT, Gemini, Claude, Microsoft Copilot, Perplexity AI, and Sana AI. We explore what they offer, where they fall short, and why full control, compliance, and customization make a real difference when AI becomes a core part of your business stack.

This post focuses on what most comparisons miss: how much control your business actually has over the AI tools you rely on every day.

The Cloud-Only AI Tools Landscape

Let’s start by understanding what “cloud-only” means and why it matters.

Cloud-only AI tools are platforms that:

• Operate exclusively on the vendor’s infrastructure
• Do not offer on-premise or hybrid deployment models
• Depend on the provider’s predefined storage, security, and data handling policies
• Offer limited options for infrastructure-level customization

These tools are easy to access, quick to implement, and ideal for initial testing or non-sensitive use cases. However, for companies in regulated sectors or those handling confidential data, cloud-only solutions may introduce security and operational limitations.

Here’s a more detailed look at some of the most common cloud-only AI tools:

ChatGPT (OpenAI)

• Fully hosted on OpenAI infrastructure
• Great for personal productivity and developer experimentation
• Offers limited data control or integration depth
  → Read our full comparison: Dot vs. ChatGPT

Gemini (Google)

• Embedded within Google Workspace apps
• Powerful model for summarization, search, and writing tasks
• Bound to Google Cloud, with little room for hosting flexibility
  → Read our full comparison: Dot vs. Gemini

Claude (Anthropic)

• Known for long context and thoughtful responses
• Hosted fully on Anthropic’s platform
• Lacks deployment flexibility and direct data management
  → Read our full comparison: Claude vs. Dot

Microsoft Copilot

• Integrates deeply with Microsoft 365 apps
• A strong choice for teams already using Office tools
• No options for on-premise deployment or workflow customization

Perplexity AI

• Designed for rapid search and factual responses
• Consumer-friendly and web-based
• Not designed for enterprise data integration or compliance

Sana AI

• Focused on internal knowledge delivery and corporate learning
• Entirely hosted on Sana’s infrastructure
• Lacks private deployment or deep customization capabilities

What Dot Does Differently

Dot was designed from the beginning to support long-term, enterprise-grade AI use. It is not just another chatbot; it is a flexible, adaptable AI framework built for companies with real operational needs.

With Dot, teams can:

• Choose their hosting method including cloud, hybrid, and fully on-premise options
• Create complex workflows using orchestration of multiple AI agents
• Use and switch between multiple models (Claude, Mistral, Gemini, Cohere, and more)
• Build workflows using no-code tools while also enabling developer-level extensions
• Manage their own data storage, access controls, and compliance structure

This setup allows organizations to use AI not just as a tool but as part of their infrastructure. Unlike most cloud-only tools, Dot adapts to your operations instead of forcing your operations to adapt to the tool.

Why Hosting Options Matter

In industries like finance, healthcare, government, and legal services, data location and infrastructure control are non-negotiable. Where and how your AI operates can determine whether you meet industry regulations, protect intellectual property, or maintain customer trust.

Dot offers:

• Cloud hosting for organizations needing speed and convenience
• Hybrid deployment to separate sensitive and non-sensitive workloads
• On-premise deployment for full data sovereignty and internal infrastructure use

In contrast, cloud-only tools centralize everything in the vendor's infrastructure which may conflict with internal IT policies or regional compliance laws. The ability to choose your hosting method is often the line between experimentation and real implementation.

Compliance You Can Define

AI tools process sensitive data. That means your organization is responsible for how it is handled, secured, and stored.

Dot helps you meet your own standards by offering:

• GDPR compliance and full alignment with regional data laws
• Alignment with common governance needs in areas like healthcare, consumer privacy, and information security
• Customizable encryption, logging, retention, and access control options

Cloud-only tools may comply with general standards, but often restrict custom configurations. For organizations in regulated sectors or those building proprietary AI operations, that lack of flexibility can become a long-term risk.

Customization Beyond the Surface

AI should feel like part of your team, not a disconnected app with a pretty interface.

Dot enables:

• Visual, no-code workflow creation so business teams can build independently
• Multi-agent orchestration, allowing agents to take on different roles across workflows
• Developer access for custom logic, internal tool integration, or advanced model usage
• Use of internal data, documents, APIs, and decision layers

Cloud-only tools may allow prompt customization, but they rarely offer the ability to build autonomous, multi-step workflows that reflect your internal processes.

What About Open-Source Models?

The open-source model space is growing fast. Tools like LLaMA, Mistral, DeepSeek and Falcon provide competitive capabilities, especially for companies looking to avoid vendor lock-in.

But here’s the catch: most cloud-only tools do not support open models.

Dot does.

You can run Mistral and other open models inside Dot, fully integrated with your workflows, agents, and infrastructure preferences. This means you get the flexibility of open-source with the structure and compliance of an enterprise-grade platform.

So, what exactly is an open-source model?

Open-source AI models are publicly released by developers or research labs with permission to inspect, modify, and build upon the model architecture and weights. Unlike proprietary models (like GPT-4 or Claude) that are locked inside private infrastructure, open-source models allow you to:

• Host the model locally or in your own cloud
• Fine-tune or extend it for your own use cases
• Audit the code and training data (where available)
• Integrate into private systems without external dependencies

This makes them a powerful foundation for organizations that want to control how their AI evolves. However, open-source models require infrastructure, engineering resources, and orchestration support, all of which Dot provides.

This hybrid approach ensures that AI development is not restricted by model availability or deployment structure. You get the freedom of open tools, backed by the reliability of a business-ready platform.

Conclusion: AI Tools Are Everywhere. Control Isn’t.

Cloud-only AI tools have pushed generative AI into the mainstream. They are fast, accessible, and ideal for simple tasks or personal productivity. But they are not enough when AI becomes a critical part of how your company works.

Dot was built for those moments when AI stops being a test and becomes a requirement. It gives you full control over infrastructure, compliance, and workflow design.

Whether you’re building internal copilots, AI agents for finance, automated support flows, or custom AI integrations across teams, Dot helps you do it securely, flexibly, and at scale.

Get in touch with us to discuss how Dot can support your enterprise AI needs with on-premise or hybrid deployment.

Frequently Asked Questions

What is the difference between Dot and cloud-only AI tools?
Cloud-only AI tools run entirely on vendor infrastructure, while Dot offers full control with cloud, hybrid, or on-premise deployment.

Why does hosting flexibility matter for AI tools?
It impacts compliance, data privacy, and scalability — especially for regulated industries or companies with internal infrastructure.

Can Dot run open-source models like Mistral or DeepSeek?
Yes, Dot supports open-source models and allows them to be used in secure, customized enterprise environments.

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