Adding Context to Answers with GenAI-Powered Analytics

Generative AI (GenAI) has been arcing north ever since it gained momentum with ChatGPT. The platform definitely opened a new chapter in human-machine conversations. Data analytics is no stranger to this phenomenon. With conversational analytics now being a ‘real thing’, GenAI’s impact on the way enterprises use data can be much more remarkable.

As this technology finds its legs in the analytics landscape, the concept of talking to data must evolve and sync with context-aware interfaces. It should go beyond simply answering a series of related questions with disjointed responses to ensure complete coherence and a proper flow of conversation. But there’s a catch: most foundational models used for GenAI-based platforms do not possess contextual awareness. Their outputs can be robotic and hallucinated, especially when they use billions of parameters to build the underlying large language models (LLMs), and inputs are unclear or incomplete.

Kyvos Copilot is the secret sauce that redefines a much-needed shift toward humanized analytics—moving from complexity to utmost simplicity. After all, there’s a difference between ‘conversing with data’ and ‘having complete trust in answers’.

How Kyvos Copilot Enriches Conversational Analytics

GenAI-powered analytics can bring more realism, correctness and perspective to answers sought by business users in their day-to-day work. Here’s how:

Intelligent Selection of Underlying Data Models

Much like a bank teller who can answer any account-related questions without the account holders being bothered by the sources used, GenAI can do the same when coupled with a smart semantic layer.

Every user query needs to be parsed through the most relevant data models to answer it. Even when using traditional self-serve analytics tools, business users need to possess a certain degree of technical acumen, knowledge about query languages and understanding of the underlying data models. But if we combine GenAI with an advanced semantic layer like Kyvos, it can automatically choose the right models using intelligent algorithms.

The answers become faster, more accurate and highly relevant, when backed by standardized business logic—all without users having to learn anything about the process. They can just ask a question and get its answer. Simple as that!

Context-Aware, Guided Interactions Lead the Way

Talking to data without any proper context to the questions or flow in which they are asked can yield faulty answers—it’s the simple principle of ‘Garbage-In-Garbage-Out’. However, human-like interactions with data should not come with this challenge, and they must pick up relevant context based on a prior query.

Let’s understand with an example: Ben wants to dig out the previous year’s sales numbers for all beverages sold in Los Angeles. He asks the platform’s copilot to show the relevant figures. Next, he tries to find only the number of sparkling water bottles sold in the same location. In this case, the tool should have the intelligence to show the data from the previous year since that was his first query. Even advanced GenAI-driven BI platforms can’t do that with their natural language processing (NLP) interfaces.

Kyvos Copilot can. With a chat-like interface, it helps Ben ask ‘n’ questions about past year’s sales and churn out contextually relevant answers every time. No more circling back to the first query and adding the year to each question posed.

Easy Data Exploration Is the Key

Simplifying analytics requires more than answering queries and running a ‘Q&A’ session with a BI tool. Sentiments and semantics are equally important. As technology grows, GenAI models are predicted to become more iterative and intelligent by recognizing the right context and language attributes. The result is easier sailing through data to find relevant trends and insights that can lead conversations in the right direction with a proper flow.

Kyvos does all this while also saving previous interactions in a repository. This helps personalize subsequent responses, speed them up and show the most relevant results for repeated queries. So, moving forward with the same example, Ben can use contextual prompts or cues to choose the query closely related to the one asked before. It can be questions like:

• How were the sales of sparkling water in the last quarter?
• What were the sales of sparkling water vs. mineral water?
• How many bottles were sold in New Jersey?

This keeps the conversations on the right track. Users can keep refining their questions as they follow this trail until they get all the answers they need. Kyvos understands the user intent behind each question to avoid vague or disconnected responses—all while using plain business language.

Missed Something? Let’s Backtrack Every Step

Users may also want to backtrack to a previous chat, try to reframe the questions or open a parallel track to talk more about the subject. All these need multi-turn conversations within the copilot, picking up loose ends and tying them up with better questions—much like the way human discussions are.

Kyvos helps comb through the full length and breadth of data—until all t’s are crossed and i’s are dotted. It allows the flexibility to pick up conversations from any point and restart the exploration.

Responses Are On-Point Always!

The black-box approach of GenAI models dampens their trustworthiness. Without insights into what data and reasoning are used, they don’t earn many trust points with users. Explainability can just be the cure.

Kyvos Copilot helps users get details about the sources, logic and facts used to generate a response. These can be the columns of data or fields used by the models—as they reduce hallucinations or biases with continuous model evaluation.

Looking Ahead

Kyvos Copilot brings radical change to conversational analytics with a seamless, guided exploration of enterprise data. We are at the forefront of innovations in AI-powered analytics, helping organizations convert their raw data into actionable metrics with minimal effort and resource consumption. Enabling conversational interactions with data at all levels, the platform strengthens self-serve analytics and establishes a universal, trusted source of truth on billion-scale data.

Moreover, Kyvos is the only semantic layer in the market with LangChain connectivity. It works as a trusted data source for LLMs and downstream AI applications, offering standardized versions of enterprise data definitions and metrics.

Talk to our experts for more juicy details.

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