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How I Used AI to Turn 2 Days of Conference into Actionable Business Intelligence

• 12 min read • brain-food

Stop taking scattered conference notes. I recorded Biz Dev Camp with Plaud, processed it through Claude AI, and built a system that turned ephemeral experience into documented knowledge assets I use weekly.

I recorded a 20-person peer-learning conference with a Plaud device and Claude AI. Here's exactly how I transformed 10 hours of raw audio into a 16,000-word case study, three derivative documents, and a repeatable methodology for extracting value from conferences.

The Problem: Attending a Conference Without Actually Capturing It

Recently, I attended Biz Dev Camp New Orleans—a small, intimate two-day conference for 20 business development leaders. No slides, no speakers, just peer-to-peer learning across 6 sessions: pipeline building, pricing, team scaling, AI implementation, complex sales cycles, and proposal strategy.

As someone who runs multiple businesses, I knew this was valuable. Founders sharing real numbers. Agencies debating frameworks I hadn't seen elsewhere. The kind of honest, unfiltered conversation that only happens with 20 people in a room, committed to confidentiality.

But here's the problem: I attended to listen, not to transcribe. I had my notebook, sure. But I was capturing fragments while missing the deeper patterns. The conversation moved fast. People referenced previous discussions I wasn't tracking. By day two afternoon, I had spotty notes and a vague sense that I'd missed half of what actually mattered.

Then I realized something: I had recorded the entire conference on my Plaud device.

The Tool Stack: Passive Recording + Intelligent Processing

I don't usually record conferences. But Plaud—a pocket-sized AI recording device—changes the math. It's not about secretly capturing data. It's about creating a reliable reference layer so I can stop trying to be a court stenographer and actually engage with the people in the room.

Here's what I used:

Plaud device — Always on, transcribes in real time to edge AI, exports clean transcripts in minutes. Legal clarity (no hidden recording), zero disruption to the attendee experience.

Claude AI (Claude Sonnet) — The heavy lifting. Processing raw transcripts, extracting patterns, synthesizing across sessions, and generating derivative documents in multiple formats.

Notion & Basecamp — Making the output actually usable for different audiences (team summaries, searchable knowledge bases).

Total time investment: 2 hours of AI processing per 90-minute session, plus ~4 hours of synthesis work across all sessions to identify patterns.

The result: I went from "scattered notes about a good conference" to a comprehensive knowledge asset I can reference for months, share with my team, and build on as those conversations continue to influence my business decisions.

Phase 1: The Capture Phase – What Changed

Before: Conference attendance = scramble to take notes, miss conversations, overload on information, walk away with fragmented insights.

After: Record everything, engage fully, process later, keep forever.

The Plaud NotePin device is deliberately designed to be unobtrusive. It sits in your pocket. It transcribes to the cloud instantly. The venue setting (a small, high-level peer event) meant I could be transparent about recording—confidentiality was already the group norm.

Why this matters: The moment I stopped trying to capture everything in real time, I actually started listening. I asked better questions. I engaged more deeply with attendees. I noticed nuances in the conversation that I would have completely missed if I were scribbling notes.

The recording was happening. I just didn't have to be the one doing it.

Phase 2: The Processing Phase – Raw Transcript to Structured Insight

Here's where most people stop. They have a transcript. That's useful, but it's not knowledge yet. It's data.

I used Claude to transform data into usable frameworks:

Step 1: Session Summaries

Raw transcript → 2,000+ word comprehensive session overview with:

One session transcript (90 minutes, ~12,000 words raw) became a structured document where someone could spend 10 minutes reading the summary or 30 minutes diving into the full analysis. Same information, different access points.

Example output from one session analysis:

Step 2: Pattern Recognition Across Sessions

This is where Claude's strength shows. I uploaded all 6 session summaries and asked it to identify patterns:

Output: A meta-analysis identifying 10 core patterns:

Step 3: Derivative Document Generation

One comprehensive case study, adapted for different audiences:

For me (reference): 16,000-word case study with all sessions, full quotes, attendee quick reference, open questions, frameworks.

For my team (Basecamp): 1,200-word executive summary with top insights, mapped to Rocket Media's business specifically, and actionable next steps we could implement immediately.

For knowledge management (Notion): Hyperlinked overview where someone could skim the summary or dive into any session in full context.

Phase 3: The Output Cascade – Multiple Documents from One Source

This is the insight I didn't expect: once the information is structured, you can generate it in any format for any audience.

From the same conference recording:

Comprehensive case study (16K words) — My reference, full detail, every insight, open questions for future research.

Team communication (1.2K words) — Curated for our specific business, actionable focus, ready to share with the team.

Notion searchable archive — Hyperlinked, navigable, long-term reference for the whole team.

Slack channel analysis — Meta-layer analyzing informal peer conversations during the event.

Extracted frameworks — Pricing models, pipeline stages, compensation structures, and team structures. Ready to implement.

Each format served a different purpose:

Total time to generate all formats: ~4 hours after the initial 2 hours per session. Not "write five separate articles." One comprehensive analysis, transformed into the formats I actually needed.

Phase 4: Methodology Insights – What I Learned About This Approach

Accuracy & Verification

The Plaud transcripts were ~95% accurate. But "accurate transcription" isn't the same as "accurate understanding." Claude helped me catch places where the audio was unclear, context got lost, or my own notes contradicted the recording. This three-layer verification (recording + my notes + AI synthesis) caught nuances I would have gotten wrong relying on notes alone.

Synthesis Matters More Than Transcription

Raw transcripts are useful but overwhelming. One 90-minute session is 12,000+ words of text. That's not knowledge—that's information. The real value came from Claude's ability to:

The Unexpected Byproduct: A Repeatable Methodology

I didn't set out to build a conference documentation methodology. But halfway through, I realized I was creating something replicable. Other conference attendees, event organizers, and researchers could use this exact same approach. That's worth documenting and teaching.

Where This Broke Down

A few limitations emerged:

Hallucination risk: Claude sometimes filled in context that wasn't in the transcript, making confident-sounding recommendations based on pattern-matching rather than actual evidence. I had to be skeptical of every recommendation and cross-check against the original audio. The AI's confidence level sometimes exceeds its actual accuracy—you have to verify everything.

Attendee privacy balance: I captured everything, but I had to be thoughtful about how much specific attribution to include. The Bureau's confidentiality oath meant I needed to de-identify some examples while preserving the insight. You can't just wholesale quote people or connect specific statements to names without their permission.

Time investment: This wasn't "record and instantly have outputs." It was a record, then invest 2 hours of AI processing per 90-minute session, then another 4 hours synthesizing patterns. That's still dramatically faster than traditional research, but it requires upfront work. It's not "free."

Context decay: The later I tried to process sessions, the less fresh the context felt. Processing sessions within 24 hours while they were still top-of-mind created better outputs than processing everything at the end.

Phase 5: The Unexpected Value – What I Actually Used It For

I went into this thinking: "I'll document the conference for future reference."

What actually happened:

Immediate Implementation

Three frameworks from the conference are now active in our business:

We're actively testing these in production, and I have real reference material I can point to when training the team.

Client Consulting

Language and frameworks I extracted became part of the client advisory. Instead of "best practices suggest this," I now say "One attendee shared this structure for thinking about pricing..." References real experiences instead of theory. Clients trust concrete examples over generic frameworks.

Content Generation

Three articles' worth of research came directly from conference insights. Not "here's what a conference said" but "here's a framework that works, tested by 20 agencies, with specific real-world examples." The material is stronger because it's grounded in actual business experience.

Hiring Conversations

When recruiting for our BD team, I now reference frameworks discussed at camp. Candidates who understand the hunter/account split, the 6-month outbound commitment, and the founder extraction problem are fundamentally more aligned with how we think about the business. It becomes a conversation filter.

Personal Strategic Planning

The patterns I extracted shaped how I'm thinking about scaling across my three businesses, which frameworks actually work, what's overrated, and where the blind spots are. I'm making different decisions because I have structured, referenceable knowledge instead of vague impressions.

The conference paid for itself in the first two weeks through decisions I made differently because I had structured knowledge.

Building Your Own Conference Documentation System

Here's what I'd tell someone who wants to replicate this:

You Don't Need to Be Technically Advanced

The Plaud device costs ~$200. Claude costs ~$0.03 per 1,000 words processed. You need a recording device and an AI tool. That's it. The barrier to entry is lower than ever.

You Do Need a Workflow

Record → Transcribe → Upload → Summarize by session → Synthesize patterns → Create derivative formats.

That workflow matters more than the specific tools. You could potentially use a different recording device, a different AI model, or different output formats. The structure is what creates the value.

Transparency Is Non-Negotiable

I was open about recording because I was already in a confidential peer setting. That context matters. Don't secret-record. Build it into the event design from the beginning. Get permission. Be clear about what you're using the recording for.

The Real Value Is in What You Do Afterward

The recording is just the foundation. The synthesis is where the work happens. You could record a hundred conferences and end up with a hundred transcripts that nobody ever looks at. The value comes from processing them intelligently and creating outputs that people actually use.

The Bigger Principle: How Knowledge Work Is Changing

I started this experiment wanting a better way to remember a conference. What I ended up with was a methodology for turning ephemeral experience into documented knowledge assets.

That's bigger than one conference. It's a framework for:

Most knowledge work is still done the old way: attend something, take notes, forget half of it, and wish you had better notes six months later.

What if you approached knowledge capture differently? Recorded strategically, synthesized intelligently, created assets deliberately.

That's where I think this is headed. Not "record everything and relive it," but "record strategically and extract what matters most."

The tools are here now. Plaud handles the recording. Claude handles the synthesis. Notion handles the organization. The missing piece isn't technology—it's methodology.

And methodology is something you can build and refine and teach.

What I'm Testing Next

I'm exploring a few extensions to this approach:

Multi-source synthesis: What happens when I combine conference recordings with Slack channel analysis, attendee emails, and follow-up conversations? Does that create an even richer picture of what actually happened and what people are building on?

Real-time pattern identification: Instead of waiting until the conference is over to find patterns, what if Claude could identify emerging themes during the event and flag them in real time?

Outcome tracking: I have documentation of what people said they were working on. Six months from now, can I follow up and see what actually happened? Does the advice transfer into real implementation?

Methodology productization: This process could become a service. Event organizers, corporate training departments, or consulting firms could use this approach to create documentation assets from their events.

I'll share what happens as I build these out.

The Take-Home: Passive Capture + Intelligent Processing

The conference changed what I'm working on. The documentation system changed how I work.

That's the real value here. Not the recording, not the transcription, but the synthesis—the ability to take raw experience and turn it into structured knowledge you can actually use.

Plaud gave me the recording. Claude gave me the synthesis. The workflow gave me the system.

If you're looking to extract more value from conferences, client work, or team learning, this approach is worth testing. The tools are accessible. The methodology is repeatable. The payoff—for me—was significant enough in the first two weeks that it's now a permanent part of how I operate.

Are you using AI to capture and synthesize knowledge from events or projects? I'd genuinely like to hear how you're approaching this. What works. What breaks. Where I'm missing something obvious.

That's the kind of conversation I'm built for—and exactly what we help clients with at Digital Ignitor.

Book a consultation to explore how to build knowledge systems that actually work for your business →

Created with ❤️ by humans + AI assistance 🤖