DEV Community: Rıdvan Tülünay (TulunaY)

12 AI Models Tested: Which One Generates the Best Business Charts?

Rıdvan Tülünay (TulunaY) — Fri, 15 May 2026 20:56:13 +0000

When you type "show me monthly revenue by region" into an AI dashboard tool, something complex happens behind the scenes.

The AI model must understand your business intent, identify the correct data columns, select an appropriate chart type, and generate valid configuration.

Different models handle these tasks very differently. Some get the chart type right but use wrong columns. Others understand the intent but produce invalid output.

We wanted real answers. So we tested 12 popular models across 32 real-world business scenarios.

What We Tested

We designed 32 test scenarios covering common business analytics requests:

Basic charts: Bar, line, pie, area for standard KPIs
Multi-dimensional analysis: Grouped comparisons, stacked charts
Time-series analysis: Trends over time, period comparisons
Conditional formatting: Thresholds, color rules
Multilingual prompts: Requests in Turkish and English
Complex queries: Multi-step analysis with follow-up questions

Each model was scored on correctness (right chart type and data mapping), completeness (all requested elements included), speed (response time), and stability (consistent results across similar prompts).

Speed Rankings

Response time matters for interactive use. If users wait more than 10 seconds, engagement drops.

Model	CPU (no GPU)	GPU / Apple Silicon
Gemma 4 E2B	~5s	~1.5s
Mistral 7B	~6s	~2s
Qwen 2.5 7B	~7s	~2s
Llama 3.1 8B	~8s	~2s
Qwen 3 8B	~10s	~3s

Gemma 4 E2B is clearly the fastest — ideal for interactive chart creation where speed matters more than the last few percent of accuracy.

Chart Accuracy (32 Scenarios)

Model	Correct	Wrong	Partial
Llama 3.1 8B	28/32	2	2
Qwen 2.5 7B	27/32	3	2
Qwen 3 8B	26/32	3	3
Gemma 4 E2B	25/32	4	3
Mistral 7B	24/32	5	3

Llama 3.1 8B leads in accuracy. But the gap between top models is narrowing with each release.

Multilingual Performance

For teams working in multiple languages, this matters a lot.

Model	Turkish Prompts	English Prompts	Overall
Qwen 2.5 7B	26/32	27/32	Best multilingual
Qwen 3 8B	25/32	26/32	Strong multilingual
Llama 3.1 8B	22/32	28/32	English-first
Gemma 4 E2B	20/32	25/32	Weaker Turkish
Mistral 7B	19/32	24/32	Weaker Turkish

Qwen models clearly dominate multilingual scenarios. If your team works in Turkish, Arabic, or other non-English languages, Qwen should be your first choice.

What We Learned About Chart Generation

Intent Detection Is Critical

"Show me revenue by region" could be a bar chart, pie chart, or treemap. The best models infer the most appropriate visualization type from context.

Structured Output Matters

Some models generate conversational text when chart configuration is expected. Others produce partial configurations that break rendering. This is the most common failure mode.

Edge Cases Cause Failures

Common patterns:

Misidentifying date columns as categorical data
Generating chart types that don't match data structure
Failing to handle empty or null values
Confusing similar column names

Better models handle these edge cases gracefully.

Consistency Across Sessions

Some models produce different charts for identical prompts across sessions. For production dashboard generation, consistency is essential.

Recommended Models by Use Case

Use Case	Recommended Model	Why
Turkish/multilingual dashboards	Qwen 2.5 7B	Best multilingual support
Fast interactive use	Gemma 4 E2B	Quickest response time
Maximum accuracy	Llama 3.1 8B	Highest chart correctness
Balanced performance	Qwen 3 8B	Good accuracy + multilingual
Lightweight deployment	Mistral 7B	Low hardware requirements

The Gap Is Narrowing

Models that struggled with chart generation six months ago now produce acceptable results. Within the next year, most mainstream models will handle business chart generation competently.

For now, selecting the right model for your specific use case still matters.

We built LivChart to make local AI dashboards accessible. Try different models and see which works best for your data.

Set Up Local AI Dashboards With Ollama in Under 5 Minutes

Rıdvan Tülünay (TulunaY) — Fri, 15 May 2026 20:56:07 +0000

Running AI models locally used to be complex. With Ollama, it's a few terminal commands.

In this guide, I'll walk through installing Ollama, downloading a business-capable AI model, and connecting it to LivChart for AI-powered dashboard generation — all running locally, no cloud dependency.

Step 1: Install Ollama

macOS:

brew install ollama

Linux:

curl -fsSL https://ollama.com/install.sh | sh

Windows: Download from ollama.com

After installation, start the Ollama server:

ollama serve

Step 2: Download a Model

For business analytics, I recommend starting with Qwen2.5 7B — it handles multilingual prompts well (including Turkish) and performs reliably for chart generation.

ollama pull qwen2.5:7b

This downloads approximately 4.7 GB. Other good options:

Model	Size	Best For	RAM Required
Qwen2.5 7B	4.7 GB	Multilingual analytics	8 GB
Llama 3.1 8B	4.9 GB	High-accuracy charts	16 GB
Gemma 4 E2B	1.6 GB	Fast interactive use	8 GB
Mistral 7B	4.1 GB	Lightweight deployment	8 GB

Step 3: Test Your Model

ollama run qwen2.5:7b

Try a business prompt:

Show me a bar chart of monthly revenue by region for Q1 2026

If the model responds with structured output, it's working.

Step 4: Connect to LivChart

Open LivChart and go to Settings → AI Configuration
Set the AI provider to Ollama
Set the endpoint: http://localhost:11434 (default Ollama port)
Select your model: qwen2.5:7b
Click Test Connection

If the connection succeeds, you're ready to use AI-powered chart generation.

Step 5: Create Your First AI Dashboard

Import your data (Excel, CSV, or SQL database)
Open the AI Chart Wizard
Describe what you want: "Show me quarterly revenue comparison by product category"
The AI generates the chart
Refine if needed, then add to your dashboard

Common Problems and Solutions

"Connection refused" — Make sure Ollama is running (ollama serve). Check that port 11434 is not blocked by firewall.

"Model not found" — Run ollama list to see downloaded models. If empty, pull a model first.

"Slow responses" — Try a smaller model (Gemma 4 E2B) or enable GPU acceleration. On Apple Silicon, Ollama uses Metal by default.

"Incorrect charts" — Try rephrasing your prompt with more specific terms. Include chart type explicitly: "Create a line chart showing..."

Hardware Recommendations

Setup	RAM	GPU	Models
Entry-level	16 GB	Not required	Gemma 4 E2B, Mistral 7B
Mid-range	32 GB	Recommended	Qwen2.5 7B, Llama 3.1 8B
Enterprise	64 GB+	Multi-GPU	Large models, concurrent users

Why Local AI for Dashboards?

Privacy: Business data never leaves your machine
Speed: No network latency, models respond locally
Compliance: KVKK and GDPR requirements are simpler when data stays internal
Cost: No per-request API charges after initial hardware investment
Offline: Works without internet connection

The setup takes under 5 minutes. The benefits last much longer.

Try it yourself — download LivChart and connect Ollama for local AI-powered analytics.

The Case for Keeping Your Analytics Data Off the Cloud

Rıdvan Tülünay (TulunaY) — Fri, 15 May 2026 20:56:01 +0000

Most companies using AI for analytics don't realize what happens after they type a prompt.

Their business data — sales metrics, ERP records, financial reports, supplier information — leaves their infrastructure, gets processed on external servers, and comes back as a chart or summary.

For many organizations, this creates real problems. Not just theoretical privacy concerns, but actual compliance risks under KVKK, GDPR, and internal data policies.

The Hidden Problem with Cloud AI Analytics

Cloud AI platforms are convenient. You connect a service, send prompts, and receive answers instantly.

But operationally, the model is simple:

Data leaves your infrastructure
AI processes it externally
Results return to your system

Even simple prompts may include customer information, pricing data, sales performance, production metrics, or financial trends. Organizations increasingly want more visibility into how AI systems process that information.

AI Analytics Is Becoming Infrastructure

This is not only a privacy discussion. AI is rapidly becoming part of core operational workflows:

Reporting
Forecasting
Dashboard generation
Operational monitoring
ERP analysis
Executive decision-making

As AI becomes infrastructure, deployment architecture matters much more. Companies now evaluate where models run, how data flows, and who controls the infrastructure.

What Local AI Changes

Local AI changes the architecture completely. Instead of sending prompts to external APIs, organizations run models directly inside their own infrastructure.

The workflow becomes:

Data remains internal
AI runs locally
Analysis happens inside company infrastructure
Results never require external processing

Modern tools like Ollama and LM Studio have made deployment dramatically easier. Organizations can now run advanced AI models locally without deep ML expertise.

Real Business Use Cases

Manufacturing — Teams analyze production downtime, machine efficiency, and warehouse performance without exporting operational metrics externally.

ERP Reporting — Organizations use AI-assisted analytics for inventory analysis, purchasing trends, and financial workflows inside internal infrastructure.

Financial Analysis — Teams investigate unusual transactions, margin changes, and cash flow trends using locally deployed models.

Executive Reporting — Executives get faster answers without depending on dashboard rebuild cycles.

Why Companies Are Moving Toward Hybrid AI

The future is unlikely to be fully cloud-only or fully local-only. Most businesses adopt hybrid AI environments:

Cloud AI for public workflows and non-sensitive data
Local AI for operational analytics and privacy-sensitive data
Self-hosted reporting for compliance-critical workflows

Organizations want flexibility rather than dependency on a single deployment model.

Privacy Is Important, But Speed Matters Too

Traditional BI workflows often require analysts, SQL queries, dashboard updates, and report maintenance.

AI-assisted analytics reduces friction between question and insight. When users can ask "Which supplier delays affected production output this week?" and receive analysis instantly, operational workflows become dramatically faster.

Challenges of Local AI

Local AI infrastructure also introduces new responsibilities:

Hardware Requirements — Larger models may require GPUs and higher RAM
Model Selection — Different models behave differently depending on language, analytical tasks, and hardware
Infrastructure Management — Organizations must manage deployment, updates, monitoring, and performance optimization

Local AI provides flexibility, but also requires operational ownership.

The Strategic Shift

The most important change is strategic. Businesses are beginning to treat AI as part of operational infrastructure instead of treating it as an external utility.

AI is moving closer to core business systems. Where it runs, and who controls it, matters more than ever.

If you're interested in local AI analytics, check out LivChart — a privacy-first analytics platform that runs AI models locally alongside your dashboards.

DAX and ETL Without the Cloud: A Local-First Approach to Business Intelligence

Rıdvan Tülünay (TulunaY) — Sat, 25 Apr 2026 07:57:31 +0000

DAX and ETL Without the Cloud: A Local-First Approach to Business Intelligence

If you have worked with Power BI, you know DAX (Data Analysis Expressions) and ETL (Extract, Transform, Load) are essential for business intelligence. But every time you use Power BI, your data travels to Microsoft servers.

What if you could use DAX and ETL — without sending your data anywhere?

DAX in a Local-First World

DAX is the formula language behind Power BI and Excel Power Pivot. It lets you:

Create calculated columns and measures
Build time intelligence (YTD, MTD, YoY comparisons)
Define KPIs and business logic
Aggregate and filter data dynamically

In LivChart, we built a local DAX engine that runs entirely on your device. Same formulas, same results — zero cloud dependency.

Example: Running DAX Locally

Total Sales YTD = TOTALYTD(
    SUM(Sales[Amount]),
    Sales[Date]
)

This measure works exactly the same in LivChart as in Power BI. The difference? LivChart computes it on your machine, not on Microsoft servers.

ETL Without Cloud Transfer

ETL is how you prepare data for analysis:

Extract — Pull data from Excel, CSV, or SQL databases
Transform — Clean, filter, pivot, and reshape
Load — Push into your dashboard or report

In cloud BI tools, all three steps happen on remote servers. LivChart runs ETL locally:

Connect directly to your database (no data export)
Transform with a visual pipeline builder or DAX
Load into dashboards on your device

Why This Matters

For Financial Teams

Financial data (revenue, costs, forecasts) is highly sensitive. Local processing means:

No risk of data breaches through cloud transfer
Compliance with financial regulations
Faster processing — no upload/download latency

For HR Departments

Employee data (salaries, performance, personal info) is protected by privacy laws. Local-first means:

KVKK compliance by default
No cross-border data transfer
Employee data never leaves company infrastructure

for Healthcare Organizations

Patient records require strict privacy (HIPAA, KVKK). Local processing:

Zero risk of PHI exposure
Audit trail stays on-premise
No third-party data access

Try Local DAX and ETL

Download LivChart and connect your first data source. Your data stays on your machine — always.

Built with local-first principles by Liv Yazılım

Excel Verilerinizi Dashboarda Dönüştürün – Veri Buluta Çıkmadan

Rıdvan Tülünay (TulunaY) — Sat, 25 Apr 2026 07:56:48 +0000

Excel Verilerinizi Dashboarda Dönüştürün – Veri Buluta Çıkmadan

Her ay yüzlerce Excel raporuyla boğuşuyorsanız, bu yazı tam size göre.

Sorun: Veriniz Nereye Gidiyor?

Birçok şirket, Excel raporlarını bulut tabanlı BI araçlarına yüklerken şu riskleri gözden kaçırıyor:

Veri güvenliği – Finansal raporlar, müşteri verileri, stratejik planlar üçüncü taraf sunucularına gönderiliyor
KVKK uyumluluğu – Kişisel verilerin yurt dışına aktarımı yasal risk oluşturuyor
Maliyet – Kurumsal BI lisansları aylık binlerce lira
Bağımlılık – Verendor değiştirmek tüm veriyi taşımak demek

Çözüm: Yerel AI ile Veri Analizi

LivChart, tüm veri işleme işlemlerini kendi cihazınızda yapar. Veriniz asla buluta çıkmaz.

Adım 1: Excel Dosyanızı Yükleyin

LivChart, .xlsx ve .csv dosyalarını doğrudan okur. Herhangi bir dönüştürme gerekmez.

Adım 2: AI Dashboard Oluştursun

Yerel AI modeli:

Veri türlerini ve ilişkileri otomatik algılar
Her veri seti için en uygun grafik türünü önerir
Trendleri ve desenleri yorumlar

Adım 3: Özelleştirin

Sürükle-bırak ile düzenleme:

Grafik türleri (çubuk, çizgi, pasta, ısı haritası...)
Renkler ve temalar
Filtreler ve detaylandırma

Adım 4: PDF Rapor Oluşturun

Tek tıkla profesyonel PDF raporlar:

Sunuma hazır formatlama
AI tarafından oluşturulan yorumlar
Kurumsal markalama seçenekleri

Karşılaştırma

Özellik	LivChart	Power BI	Tableau
Veri cihazda kalır	✅	❌	❌
Yerel AI	✅	❌	❌
İnternetsiz çalışır	✅	❌	❌
DAX formülleri	✅	✅	❌
ETL dahil	✅	✅	Sınırlı
KVKK uyumlu	✅	Kısmen	Kısmen

Ücretsiz Deneyin

👉 livchart.com adresinden indirin ve ilk dashboardınızı saniyeler içinde oluşturun.

Verileriniz sizin cihazınızda kalır. Her zaman.

Bu yazı Liv Yazılım tarafından hazırlanmıştır.

How to Build Dashboards Without Uploading Data to the Cloud

Rıdvan Tülünay (TulunaY) — Sat, 25 Apr 2026 07:50:10 +0000

How to Build Dashboards Without Uploading Data to the Cloud

Most BI tools require you to upload your data to their servers. But what if your data is too sensitive? Financial reports, HR data, strategic plans — you should not have to trust a third party with this information.

Here is how to build production-ready dashboards while keeping your data 100% on your machine.

The Problem with Cloud BI

When you use Power BI, Tableau, or similar tools:

Your data leaves your device — uploaded to Microsoft, Salesforce, or AWS servers
Compliance risk — GDPR, KVKK, and industry regulations restrict data transfers
Vendor lock-in — switching tools means migrating everything
Cost — enterprise licenses can cost thousands per month

The Local-First Alternative

LivChart takes a different approach: all data processing happens on your device. Nothing is uploaded. Ever.

Step 1: Connect Your Data Source

LivChart supports multiple data sources:

Excel files (.xlsx, .xls)
CSV files
SQL databases (MySQL, PostgreSQL, SQL Server, and more)
Direct database connection — no export needed

Step 2: AI Analyzes Your Data

Once you connect your data, the built-in local AI:

Detects data types and relationships
Suggests optimal chart types for each dataset
Creates an initial dashboard layout
Interprets trends and patterns

All of this happens on your machine — no API calls, no cloud processing.

Step 3: Customize Your Dashboard

Drag and drop to arrange your charts. The AI suggestions are just a starting point — you have full control over:

Chart types (bar, line, pie, scatter, heatmap, etc.)
Colors and themes
Layout and sizing
Filters and drill-downs

Step 4: Export Reports

One click to generate professional PDF reports:

Presentation-ready formatting
AI-generated interpretations
Custom branding options

Built for Data Privacy

Feature	LivChart	Power BI	Tableau
Data on device	✅	❌	❌
Local AI	✅	❌	❌
No internet required	✅	❌	❌
DAX formulas	✅	✅	❌
ETL built-in	✅	✅	Limited
GDPR by default	✅	Partial	Partial

Try It Yourself

Download LivChart from livchart.com
Upload an Excel file or connect a database
Let AI build your first dashboard in seconds

Your data stays on your machine. Always.

This post was written using LivChart — local-first AI data visualization.

LivChart: Local-First AI Data Visualization – Your Data Never Leaves Your Device

Rıdvan Tülünay (TulunaY) — Sat, 25 Apr 2026 07:24:31 +0000

LivChart: Local-First AI Data Visualization

Your data never leaves your device.

The Problem

Every time you upload sensitive business data to a cloud BI tool, you risk exposure. Traditional dashboard tools require you to trust third-party servers with your financial reports, customer data, and strategic insights.

With increasing data regulations like GDPR and KVKK, and rising cyber threats, cloud-based BI is becoming a liability.

The Solution

LivChart is a local-first data visualization and dashboard builder powered by AI. It runs on your machine – your data never gets uploaded to the cloud.

Key Features

📊 AI-Powered Charts: Upload Excel, CSV, or connect your database. AI builds the optimal visualization in seconds.
🔒 Privacy-First: All processing happens locally. Zero data leaves your device.
📄 PDF Reports: Generate professional, presentation-ready reports instantly.
🧮 DAX & ETL Support: Built-in data transformation and DAX formula engine.
🗄️ Database Independent: Works with Excel, CSV, SQL databases, and more.
🤖 Local AI: Uses on-device AI models for data analysis and interpretation.

How It Works

Upload your data (Excel, CSV, database connection)
AI analyzes your data structure and suggests optimal visualizations
Customize your dashboard with drag-and-drop
Export as PDF report or share internally

Why Local-First?

In an era of increasing data regulations and cyber threats, keeping data on-premise is not just a preference – it is a necessity.

Comparison with Cloud BI Tools

Feature	LivChart	Power BI	Tableau
Data stays on device	✅	❌	❌
Local AI analysis	✅	❌	❌
No cloud dependency	✅	❌	❌
DAX support	✅	✅	❌
ETL built-in	✅	✅	Limited
PDF report generation	✅	Limited	Limited
GDPR/KVKK compliant	✅	Partial	Partial

Get Started

🌐 Visit livchart.com
📥 Download the desktop app
🔑 Get a license key
📖 Read the blog for tutorials

Built with ❤️ by Liv Yazılım