Tellox

Inspiration

The inspiration for Tellox came from recognizing a fundamental disconnect between customers and small businesses. Businesses receive vast amounts of public feedback through online reviews, social media, and forums. However, this feedback is often scattered, unstructured, and reduced to simple star ratings. Small business owners typically lack the time and technical expertise to sift through this data and extract meaningful, actionable insights. The core problem is not a lack of information, but the inability to effectively interpret it. Tellox aims to close this gap, transforming fragmented public commentary into clear, decision-driving research.

What it does

Tellox is a research and analysis tool designed to help small businesses learn from public feedback. It functions as an interpretive layer on top of existing platforms, not as a replacement for them.

Aggregation and Structuring: It collects unstructured public feedback related to a business from multiple sources and processes it into structured data.

Insight Generation: It uses advanced analysis techniques to identify overall sentiment, recurring topics, and trends over time. Instead of just a score, it provides explanation and context.

Conversational Research: It includes a unique chat interface that allows users to ask specific, natural language questions about their feedback, enabling flexible exploration beyond predefined metrics.

How we built it

Tellox is conceptualized as a web-based application built on principles of feasibility and transparency.

Data Processing: Public feedback is first collected and cleaned to ensure quality.

Hybrid Analysis: We propose a hybrid approach to sentiment analysis, combining fast, explainable rule-based methods (like VADER) with deep semantic understanding provided by transformer-based language models. This balances speed, accuracy, and interpretability.

Topic Modeling: Natural language processing is used to identify recurring themes and group feedback into interpretable categories.

Interface Design: The system emphasizes a clear dashboard for structured insights and a conversational interface for targeted data retrieval, focusing on accessibility for non-technical users.

Challenges we ran into

The primary conceptual challenges centered on balancing analytical depth with explainability.

Sentiment Misclassification: Unstructured text is difficult to analyze. We need to mitigate the risk of incorrectly classifying sarcasm or nuanced opinions, which is why a hybrid modeling approach is essential.

Overgeneralization: Presenting aggregated insights carries the risk of overgeneralizing the findings. The solution must ensure users can always inspect representative examples of the underlying feedback to maintain context and transparency.

Scope Management: The system must avoid generating unsupported conclusions. The conversational interface is strictly designed to retrieve and summarize insights grounded in existing, analyzed feedback, rather than inventing new information.

Accomplishments that we're proud of

We are most proud of designing a system that focuses on interpretation rather than just collection. Tellox addresses the systemic failure in the feedback loop by making existing data useful. We believe the hybrid analytical approach and the novel conversational research interface significantly reduce the friction experienced by small business owners. This design turns a frustrating pile of data into a clear research tool, which is a meaningful accomplishment.

What we learned

We learned that for small businesses, the bottleneck is rarely the volume of feedback, but the velocity of insight. Simply having the data is not enough; the data must be immediately understandable and actionable. We confirmed that a solution must prioritize a user experience tailored to non-technical users, emphasizing explanation and context over opaque, complex metrics.

What's next for Tellox

The path forward involves a staged approach to development:

Stage One MVP: Focus on a minimal viable product where users upload structured feedback files. Implement basic text cleaning, rule-based sentiment analysis, and simple keyword extraction.

Stage Two Expansion: Integrate transformer-based models for deeper thematic analysis and contextual sentiment grouping. Introduce time-based trend comparisons.

Stage Three Launch: Develop the conversational research interface, allowing natural language queries to retrieve and summarize insights from the analyzed dataset.

The ultimate goal is to build a fully working web application that provides multi-level analysis and supports limited natural language querying for all small business owners.

Built With

• huggingface
• python