Why not just use ChatGPT for meeting summaries?

"Can't I just paste a transcript into GPT and ask it to summarize?"

This is the most common question we get from people discovering Briefly for the first time. And yes, you absolutely can use ChatGPT, Claude, or Gemini to summarize text. So why do professionals choose Briefly?

The Limitations of Generic LLMs

When you give GPT or Claude a meeting transcript and say "summarize this," you'll get a decent result. But when you try to use it in actual work, several problems emerge.

1. Inconsistent Structure

Summarize the same transcript multiple times, and you get different formats each time. Sometimes bullet points, sometimes paragraphs, sometimes tables. It's hard for a team to share information in a consistent format.

2. No Criteria for "What's Important"

LLMs don't know what matters. They can't distinguish between 5 minutes of small talk and 30 seconds of critical decision-making. They tend to flag "frequently mentioned" topics as important.

3. No Path to Action

You get a summary, but "so what do I do next?" is missing. In consulting, summaries themselves aren't the goal — the actions and insights derived from them are.

Briefly's Approach: Knowledge Aggregator Framework

Briefly doesn't simply ask an LLM to "summarize." It generates structured insights based on the Knowledge Aggregator Framework (KAF).

What is KAF?

The Knowledge Aggregator Framework is a summarization methodology developed from years of hands-on experience at top-tier strategy consulting firms. The core principle is Answer First — start with the conclusion.

"A good summary lets the reader grasp the key points within the first 30 seconds."

This methodology follows a clear structure:

Key Takeaways: First, what you need to know from this meeting/interview
Supporting Evidence: Why we reached these conclusions
Next Steps: Concrete actions to take

What Does "Scientific Summarization" Mean?

Briefly's summaries are "scientific." What does that actually mean?

Reproducible Results

Same input with same settings produces consistently structured output. Summaries from Team Member A follow the same format as those from Team Member B.

Category-Optimized Templates

Optimized summarization templates are applied for each type: expert interviews, client meetings, internal discussions.

Expert Interviews: Focus on insights and implications
Client Meetings: Focus on requirements and feedback
Task-based Meetings: Focus on action items and owners

Validated Prompt Engineering

Not a simple "summarize this" command, but prompts validated against hundreds of real meeting transcripts.

Real Comparison: GPT vs Briefly

What happens when you feed the same expert interview transcript to GPT versus Briefly?

GPT's Summary

This interview discussed enterprise AI adoption.
The expert mentioned that most companies are in the PoC stage,
and explained that security and cost are major considerations.
They also suggested that a hybrid model approach is effective...
(continues)

Briefly's Summary

[Key Takeaways]
1. Enterprise AI adoption proceeds through accumulated BU-level PoCs, not "big bang"
2. Actual budgets concentrate in low-risk areas: contact centers, search, internal summaries
3. Commercial/open models + domain tuning + RAG is more realistic than building in-house LLMs

[Implications]
- Prioritize Quick Win areas over large-scale investments
- Guardrails are more effective than blanket bans

[Next Steps]
□ Map low-risk use cases by client industry
□ Draft vendor comparison table

See the difference?

Why This Matters

Consultant time is expensive. You shouldn't spend another hour analyzing a 1-hour interview.

With Briefly:

Get actionable summaries within 5 minutes after meetings
Communicate in consistent formats across your entire team
Capture every insight with structured documentation

Generic LLMs are great tools. But professional work requires tools designed for consultants.

The Bottom Line

The answer to "Why not just use GPT?" is this:

You can. But you can do better.

Briefly isn't just a summarization tool. It's the automation of methodologies validated through years of consulting practice.

Instead of spending time formatting meeting notes, focus on what you're actually supposed to do — solving your client's problems.