User-Controlled AI for Text Analysis
The integration of generative AI into text analysis represents a significant opportunity for researchers, analysts, and organizations. Yet, many implementations lack transparency and flexibility. Provalis Research offers a different path—one that puts users in control while delivering powerful AI capabilities.
The Challenge of AI Integration in Research Tools
The release of ChatGPT in late 2022 sparked widespread interest in generative AI applications for text analysis. However, many early tools introduced serious concerns. They function as “black boxes,” concealing models, prompts, and decision processes—making it difficult to assess, reproduce, or validate results.
In addition to this opacity, expensive subscription models often force organizations into rigid, vendor-locked ecosystems. This creates tension between AI’s promise and professional needs for transparency, reproducibility, and control. Many tools prioritize marketability over the rigorous standards required for policy research, business intelligence, or academic study.
Provalis’ Transparent, Flexible GenAI Approach
Provalis Research addresses these challenges by designing AI integrations that enhance human capabilities without compromising methodological integrity. Our solution rests on three foundational principles:
1. Engine Agnosticism and Model Selection
WordStat and QDA Miner support integration with multiple AI engines—including OpenAI, Claude, Gemini, Mistral, DeepSeek, and local models via Ollama. This allows users to choose the best models for their needs, balance cost and performance, and avoid vendor dependency. It also makes it easier to adopt emerging models without waiting for vendor updates.
2. Economic Transparency and Efficiency
We use a pay-as-you-go model that leverages personal API keys. This is a dramatic shift from high-cost subscriptions. Users only pay for what they use and can estimate and track costs in real time. With Ollama, users can even run models locally at no cost—ensuring sensitive data stays on-device.
3. Methodological Transparency and Customization
WordStat and QDA Miner make most AI prompts visible and editable. Analysts can see precisely how their data is being processed, adapt prompts to their own methods, and document their workflows for audit or peer review. This level of transparency enables rigorous, replicable AI-assisted analysis.
Application Across Analytical Methods
Provalis tools support both quantitative and qualitative methodologies, recognizing that modern analysis often blends approaches. With AI integration, new capabilities enhance traditional workflows:
- Sentiment analysis with high domain and language adaptability
- Improved named entity recognition through contextual understanding
- Next-generation topic modeling with AI-generated labels and themes
- AI-assisted summarization and key information extraction
- Scripts for pros/cons extraction,
- Readability scoring
- Spell-checking & Translation
- Segmentation for Chinese, Japanese, and Thai texts
- Grouping of Vietnamese monosyllabic tokens
- Automatic coding of open-ended response (QDA Miner)
- Case-based and aspect-based sentiment analysis (QDA Miner)
Users can also create and refine their own AI prompts, tailoring analyses to specific use cases—from business dashboards to government policy evaluations.
QDA Miner for Qualitative Research
QDA Miner enhances qualitative work through natural language querying, allowing intuitive exploration of interviews, focus groups, or survey responses. AI-assisted coding accelerates the categorization of large datasets while preserving human oversight, making it ideal for social media data, open-ended survey responses, and more.
Quality Assurance and Implementation
AI models are powerful, but they are not infallible. QDA Miner includes validation tools such as inter-model agreement and intra-model consistency checks. While WordStat does not include built-in validation, its flexible design enables comparable workflows using different models and prompt versions.
Human-in-the-loop practices are central to our approach. Automated tools identify areas that need further review, but final decisions always remain with the analyst—ensuring analytical quality is never compromised.
Conclusion and Future Directions
The integration of generative AI into research workflows offers immense potential—but also requires responsibility. At Provalis, we are committed to tools that empower users rather than replace them. Transparency, adaptability, and methodological rigor are at the heart of our design philosophy.
As AI technology advances, we will continue to expand our features with guided procedures, better validation tools, and support for new engines—always with user control in mind.
Get Started
Want to experience transparent, user-controlled AI in your text analysis workflow? Explore WordStat and QDA Miner or contact us today to request a demo or start a free trial.