Conversational Knowledge App
An AI chatbot experience with contextual responses and rich interactions that doubled engagement and improved user trust.
Impact
Results that mattered
Overview
The opportunity
The founders had a clear idea: honest, useful answers in a mobile experience people actually enjoy using. We turned that into TruthGPT, an AI app with conversational UX, semantic search, and retention-focused interaction design.
The challenge
- Generic chatbot UX that felt cold and untrustworthy
- Need for contextual, source-aware responses
- Engagement drop-off after first session
- Performance and animation quality expectations on mobile
Solution
What we built
We built a conversation-first product with strong onboarding, prompt patterns, semantic search, and delightful motion. The AI layer was integrated carefully so answers felt useful, not gimmicky.
Contextual chat
Conversation memory and clearer follow-up handling.
Semantic search
Better retrieval for higher-quality responses.
Insight cards
Structured answer modules users can scan quickly.
Engagement loops
Daily prompts and saved threads to drive return usage.
Polished motion
Flutter-powered interactions that feel premium.
Trust cues
Clearer framing around confidence and sources.
Product UI
App screens we delivered
A closer look at the core mobile experiences, from primary workflows to supporting views shipped in the final product.
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TruthGPT
Ask anything
Hi, I can help with clear, reliable answers. What do you need?
Summarize today’s market movers
Here are 3 movers with context and sources…
Semantic search · On
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Insights
+40% interactions
3D replies
3X
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Library
Saved Threads
Market movers brief
Updated 12 min ago
Product research
8 messages
Daily prompt
New
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Source View
Answer grounded in 4 sources with confidence cues.
Source A
High
Source B
Med
TrustClear framing
Delivery
How the engagement unfolded
01
Concept
Defined answer quality standards and conversation tone.
02
Prototype
Validated chat UX and retention moments.
03
AI integration
Connected models, retrieval, and evaluation loops.
04
Launch
Soft-launched with analytics-led iteration.
Stack
Technologies used
Flutter
Python
Firebase
TensorFlowFlutterPythonFirebaseTensorFlow
Outcomes
Business impact
- Session depth and return visits improved rapidly after launch
- Users spent more time exploring follow-up questions
- The product narrative became clearer for investors and early adopters
More Work
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