Meetric AI

Product•2026

Every team has the same problem: meetings happen, things get discussed, decisions get made and then nothing moves. Someone writes notes, another person writes different notes, and by the next day half the action items have evaporated into the ether. I've watched this play out in startups, remote teams, and growing SMEs across Africa. The cost isn't just lost time it's lost trust. Clients wait. Deadlines slip. People blame each other because no one really knows who committed to what. The frustration is universal, but the tools to solve it have always been either too expensive, too complex, or built for enterprise teams with dedicated ops staff. I wanted to build something that works for a two-person startup just as well as a twenty-person agency something that turns the messiest meeting transcript into a clean, actionable task board in seconds. That's what became Meetric.

Challenge

Prompt reliability at scale

Getting Qwen to return consistent, parseable JSON across wildly different transcript formats formal board meetings, casual Slack huddles, voice-note ramblings required significant prompt iteration. The breakthrough was a strict system prompt with explicit JSON schema, temperature set to 0.1, and a fallback parser that strips markdown code fences before parsing.

Implicit vs explicit action items

Most meeting action items are implied, not stated outright "let's revisit this next week" is an action item, and "John mentioned the pricing issue again" might be one too. Training the agent to catch these without over-extracting noise was a careful balancing act in the system prompt design.

Alibaba Cloud deployment

Setting up Function Compute with the correct environment variables, CORS configuration, and cold-start handling for a Node.js Express app added unexpected complexity. The solution was packaging the API as a custom runtime with a warm-up ping route.

Real-time UX during extraction

Users shouldn't stare at a spinner for 8 seconds. Implementing streaming from the Qwen API through a Next.js route handler to the client while parsing incomplete JSON chunks safely — required building a custom streaming state machine.