When deploying AI internally, the questions that matter go beyond accuracy. People want to know: Where did this answer come from? Can I control how the model behaves? Is this response traceable and secure?
These aren't just technical concerns—they're fundamental to building trust in AI systems that handle sensitive organizational knowledge. QAnswer's latest update tackles these challenges head-on with four features designed to bring transparency and control to enterprise AI.
The Power of Choice: Multiple LLM Options
Not all AI models are created equal. Some are optimized for speed, others for precision. Some are verbose, others more concise. The reality is that different use cases demand different approaches, and one-size-fits-all rarely works in enterprise environments.
QAnswer now gives users full control over which model they use—without compromising data privacy. Users can choose between several EU-hosted LLMs, including:
- QAnswer's in-house model
- Claude 3.7 Sonnet (via AWS Bedrock)
- Azure GPT-4o and GPT-4o Mini
- Mistral Medium
All of these are hosted within the European Union, which is critical for public institutions, regulated industries, or any company taking data governance seriously.
This flexibility means users can run sensitive queries on internal documents without leaving EU borders, choose a lighter or faster model for high-volume workloads, or experiment with different LLMs to see which one fits their workflow best. Whether integrating AI into support, legal, compliance, or R&D, organizations now have the flexibility to adapt QAnswer to their technical and organizational needs.
Opening the Black Box: Prompt Transparency
In AI, context is everything—and now, users can see exactly how QAnswer builds that context.
Every time a response is generated, users can view the underlying prompt configuration, including the system instructions (like "You are a helpful assistant. Don't make up answers."), the documents and sources retrieved, and the final user-visible output.
This makes QAnswer fully auditable, especially for teams working in regulated or high-responsibility environments. Legal teams can validate the output trail, AI engineers can debug assistant behavior, and analysts can review query accuracy and consistency.
It's a simple feature with huge impact: users can now understand why the assistant said what it did, not just what it said. This level of transparency transforms AI from a mysterious oracle into a tool organizations can actually trust and verify.
Precision in Context: Smarter Answer Highlighting
One of the most frustrating aspects of AI-generated answers is the difficulty in tracing them back to their sources. QAnswer has significantly improved how it highlights content inside long documents.
Instead of only summarizing the answer, QAnswer now highlights the exact sentence fragments that support the answer, links them to source documents with clear numbering, and displays extracted snippets in the results panel so you don't need to open the full file.
This is especially valuable when working with large technical PDFs, procedural manuals, or archived documents. You get instant traceability from answer to source, with minimal friction.
For non-technical users, this builds trust. For technical users, it reduces time spent verifying outputs. It's the kind of feature that seems obvious once you have it, but represents a significant leap forward in AI usability.
The Best of Both Worlds: Faceted Search
Natural language search is powerful—but sometimes, you need to go deeper. Enterprise users often need to combine the flexibility of conversational AI with the precision of traditional search filters.
The new faceted search system allows you to filter results dynamically based on metadata. The filters adapt to the dataset and might include file type, topic or label, author or source system, custom tags, and date range.
These filters are automatically inferred from the indexed data, and adjust based on what's available in your environment—whether that's SharePoint, OneDrive, internal servers, or uploaded documents.
This approach lets you quickly narrow down results in large datasets, combine broad semantic questions with precise filters, and give expert users more control without sacrificing usability. It's a huge step toward blending classic enterprise search with the flexibility of conversational AI.
Trust Through Transparency
These updates represent something important: the evolution of QAnswer from a simple chatbot into a reliable, transparent, and customizable tool for navigating internal knowledge.
It's not about replacing people. It's about making sure that when someone asks a question, they get an accurate answer from a trusted source, with visible reasoning behind it, on infrastructure that respects their compliance needs.
Whether you're a business lead evaluating AI vendors, or a technical lead deploying in production, these tools are designed to meet you where you are—and grow with you. In an era where AI trust is paramount, QAnswer is betting on transparency as the foundation for lasting enterprise adoption.
