ContextCatcher

Inspiration

ContextCatcher transitioned into a Meeting Reconstruction Assistant because our team wanted to solve that terrible, universal feeling of looking at a scratchpad two days after a major meeting and realizing your notes look like a completely different language. We’ve all written down cryptic, fragmented shorthand like “fix that alignment issue soon” or “update the group on the pitch,” only to realize later that the context is totally gone.

Standard note-taking apps are just passive folders; they don't help you remember what you were actually thinking when you wrote those messy notes. Most AI tools just make wild guesses, hallucinating tasks that nobody ever agreed to. We wanted to build a conversational partner that steps in to rescue your lost thoughts. By anchoring your messy notes to your meeting’s overarching topic and agenda, our assistant methodically decodes the shorthand, acts as an operational gatekeeper, and restores the clarity needed to turn a forgotten meeting into real-world momentum.

What it does

When a user interacts with the assistant, it guides them through an intuitive onboarding flow, asking for three anchors: the Meeting Topic, the Agenda, and their Cryptic Notes. Instead of generating generic task lists, the assistant pairs the high-level agenda with the user's chaotic shorthand. It then reconstructs the full conversational context, flags ambiguities, isolates clear takeaways, and builds an actionable execution map so teams can confidently take their first real steps forward without second-guessing their notes.

How we built it

We built the conversational dashboard using Voiceflow and deployed the live interactive web app on Netlify.

The entire system works through a structured data-matching process. When a user interacts with the chatbot, the interface captures their inputs and maps them into three distinct variables: Topic, Agenda, and Cryptic Notes.

Instead of letting the AI guess blindly, we use the user's Agenda as a strict structural guardrail. We programmed the prompt logic inside the Voiceflow AI block to cross-reference the messy, shorthand notes against the official agenda guidelines. This forces the AI to filter out random conversational noise and only extract action items that actually belong to the scope of that specific meeting. The final output is then cleanly formatted into a readable checklist directly inside the chat window.

Challenges we ran into

Our biggest hurdle was preventing the LLM from over-inferring when user notes were completely empty or irrelevant to the agenda. In early testing, if a user typed unhelpful text, the AI would desperately try to fabricate realistic tasks just to satisfy the user, violating our core rule against presenting false certainty. We overcame this by writing rigorous, example-driven prompt boundaries inside the Voiceflow AI step, instructing the model to reject notes that fail to mathematically map back to the agenda, forcing the conversation to loop back and ask the user for clarification instead.

Accomplishments that we're proud of

We are incredibly proud of building a solution that actively encourages better human thinking rather than trying to fully automate a human process. Turning a cold, static text application into a warm, guided, conversational assistant that feels like a real operational partner is a massive win for our team. We successfully navigated this major technical pivot right before the deadline.

What we learned

We learned that conversational AI design is heavily dependent on variable state management. Simply asking an LLM to "summarize notes" results in low-quality text generation. However, breaking down user inputs into isolated anchors—like a topic, an agenda, and notes—allows you to structure the AI’s reasoning matrix, yielding deterministic, high-value decision inputs.

What's next for ContextCatcher

We want to integrate voice-to-text API nodes directly into the Voiceflow intake loop. This will allow users to simply record a chaotic voice note straight out of a meeting room, letting the assistant transcribe, filter, and reconstruct the execution framework instantly.

Built With

• voiceflow