You've Interviewed Hundreds of Experts.
That's a Database Nobody Else Has.
You've spent years collecting perspectives from founders, executives, scientists, policymakers. Those insights are scattered across hundreds of episodes—unsearchable, unsynthesized, inaccessible. We structure your interview archive into something you can actually query, build on, and use.
You Can't Access What You've Collected
You've interviewed 200, 300, maybe 400 people. Each conversation added something—a perspective, a prediction, a framework, a disagreement with someone you'd talked to before.
That's years of accumulated intelligence. And you can't do anything with it.
- Before your next interview, you want to know what you've already covered on this topic
- Your audience asks 'What have your guests said about X?' and you can't answer without hours of work
- You've noticed patterns—themes that emerge, questions that get interesting answers—but they're trapped in your head
- The synthesis you've done across hundreds of conversations isn't written down anywhere
You're sitting on a proprietary database of expert perspectives. Hundreds of hours of substantive conversation with people who know things. It's more valuable than most market research firms could compile.
And you can't search it.
Why Transcripts Don't Solve This
Transcripts make your episodes text-searchable. That helps with finding specific episodes. It doesn't help with the actual problem: synthesis.
What you want to know: "What have tech CEOs said about AI regulation across all my interviews?"
Transcript Search Gives You
A list of episodes where someone said 'AI' and 'regulation.' Fragments. No synthesis.
Knowledge Graph Gives You
Synthesis across 40 guests: three main camps, notable outliers, positions that changed over time.
What you want to know: "Where do my guests disagree on this topic?"
Transcript Search Gives You
Nothing. It finds mentions, not contradictions.
Knowledge Graph Gives You
Explicit mapping of agreements and contradictions between Guest A in episode 45 and Guest B in episode 112.
What you want to know: "How has the consensus shifted over time?"
Transcript Search Gives You
Nothing. It treats 2020 and 2024 equally.
Knowledge Graph Gives You
Temporal tracking showing how your guests' views evolved—like the shift on remote work after 2021.
Your archive needs structure, not search.
We Turn Your Interview Archive Into Structured Intelligence
Through analysis of your full archive plus focused conversations with you, we build a knowledge graph—a structured map of everything your guests have said, how it connects, and where it contradicts.
Guest Perspectives
Every substantive position your guests have taken on every topic. Not just that they mentioned 'AI regulation'—but what their actual position was, what they predicted, what they recommended.
Topic Clusters
The themes that run through your archive. Every guest perspective tagged to the topics it addresses—AI, regulation, leadership, creator economy, whatever your territory covers.
Agreements and Contradictions
Where guests align. Where they disagree. Where Guest A directly contradicted what Guest B said two years earlier. These relationships are explicit, not buried.
Temporal Evolution
Guests who appeared multiple times—how their positions changed. Topics where the collective view shifted. The graph knows 2020 consensus isn't 2024 consensus.
Your Editorial Framework
The implicit methodology you may have developed. How you frame questions. What you push back on. The themes you return to. If you have an editorial point of view, we surface it.
Cross-Guest Connections
When Guest A's expertise connects to Guest B's—even if they never spoke to each other. The synthesis that exists in your archive but was never made explicit.
What Your Archive Becomes
The knowledge graph is infrastructure. Here's what it enables:
Research & Prep
Prep for interviews in minutes, not hours
Before interviewing someone about antitrust, query your archive: "What have I covered on antitrust? What did my guests say? Where are the gaps?"
Instead of scanning transcripts or relying on memory, you get synthesized answers with citations. "You've covered antitrust in 23 episodes. The main positions were X, Y, and Z. Guest A and Guest B disagreed on this point. You haven't covered the international angle at all."
Walk into every interview knowing exactly what you've already established and where to push.
Synthesis On Demand
Intelligence at your fingertips
Get answers that would take days to compile manually. 'What's the consensus on the future of social media?' 'Where do founders and VCs consistently disagree?' 'What predictions didn't pan out?'
Content From Your Archive
New content from old interviews
Contradiction episodes: 'Five tech CEOs gave me five different answers.' Synthesis posts from 30 founders on hiring. The Year in Review: What did your guests collectively predict?
Your Implicit Framework
If it exists
Some interview hosts have a strong editorial methodology—a real point of view that shapes every conversation. If you have one, the extraction surfaces it. You see your own patterns.
Audience-Facing Products
Optional monetization
Let your audience query your guests' collective intelligence. Expert perspective reports on specific topics. Premium features or standalone products—you have optionality.
Why This Isn't Just Transcript Search
You've tried uploading transcripts to ChatGPT. It can find things. It can summarize fragments. It can't synthesize across guests or track positions over time.
Scenario: You want to know what your guests have said about 'regulation of AI.'
Transcript Search / ChatGPT
Finds episodes where guests said those words. Returns fragments from different contexts. Can't synthesize across 40 different guests. Treats every mention equally.
Knowledge Graph
Maps every guest's actual position. Knows 'mentioned in passing' vs 'argued extensively.' Synthesizes: 'Three camps. Camp A (15 guests), Camp B (12 guests), Camp C (8 guests). Notable outliers include Guest M, who changed positions.'
Scenario: You want to know where your guests disagree.
Transcript Search / ChatGPT
Can't do this. It finds mentions, not contradictions. Has no concept of conflicting positions.
Knowledge Graph
Explicitly maps agreements and contradictions. 'Guests A, B, C agree. Guests D, E contradict them. Guest F's 2023 position contradicts their own 2020 position.'
The core distinction: Transcripts give you searchable content. A knowledge graph gives you structured intelligence—positions, relationships, evolution, synthesis. That's the difference between finding fragments and actually understanding what 400 interviews have collectively taught you.
How It Works
Your archive does most of the heavy lifting. You've already recorded the conversations. We structure them.
Archive Analysis + Focused Interviews
Objective: Process your entire archive and clarify your editorial lens.
- Process all transcripts—identifying guests, positions, topic clusters, themes
- Surface contradictions and evolution over time
- Conduct focused interviews with you (not to extract expertise—your guests did the talking)
- Clarify: How do you categorize topics? What's your editorial framework?
Structure Design
Objective: Design the knowledge graph architecture for your specific archive.
- How should guest perspectives be categorized?
- What topic clusters matter for your domain?
- How do we represent agreement, contradiction, evolution?
Graph Construction
Objective: Map every guest perspective, relationship, and evolution.
- Build connections across hundreds of hours
- Make synthesis explicit—connections never visible in any single episode
- Link temporal evolution and position changes
Calibration + First Application
Objective: Tune the graph for your specific use case.
- Research tool? Synthesis engine? Content generation?
- You test it—query your own archive
- Validate synthesis matches your understanding
Optimization
Real usage reveals what needs refinement. We optimize based on how you actually use it.
Total time commitment: 3-4 hours of interviews, plus review and testing.
Your guests already did the talking. We structure what they said.
The Technology Is Proven
We'll be direct: we haven't done this with an interview podcast archive yet. You'd be among the first.
But the synthesis technology is proven. We've extracted and structured 40 years of content across multiple formats—identifying how concepts connect, how positions evolve, where ideas contradict or reinforce each other.
Loading Dr. Joe Vitale's Master Graph...
408 nodes • 545 relationships
The same technology that synthesizes one person's evolving methodology synthesizes hundreds of guests' perspectives. The graph structure handles both. What's new is the application—interview archives with hundreds of distinct voices instead of one expert's body of work.
"This captures how I think—not just what I've said."
Dr. Joe Vitale
For an interview archive, the value is similar: capturing what your guests have collectively said—synthesized, connected, and queryable for the first time.
Is This Right for You?
This makes sense if:
You have a substantial interview archive
100+ episodes with substantive expert conversations. Not casual chats—real interviews where guests shared genuine expertise and positions.
You want to build on what you've collected
Whether that's better prep, synthesis content, understanding your own patterns, or potentially audience products.
You're frustrated by inaccessibility
You've wanted to reference 'what my guests have said about X' and realized there's no way to answer without hours of manual work.
You're in it for the long haul
If you're doing this for another 5-10 years, the archive keeps growing. The graph keeps compounding.
This probably isn't right if:
Your interviews are surface-level
Promotional conversations without substantive positions have less to extract. Quality matters.
You don't have enough archive yet
Under 50-75 substantive interviews, there might not be enough to synthesize meaningfully.
You just want better transcripts
If keyword search would solve your problem, use a transcription service. This is for synthesis, not search.
You're primarily a solo host teaching your own methodology
Different page for that. This is specifically for interview archives where the value is in what your guests said.
Let's Talk About Your Archive
If you've built something valuable across years of interviews and you're ready to actually use it, let's talk about whether extraction makes sense for your situation.
Or email directly: mitch@invisibleminds.ai
Common Questions
4 weeks for the core extraction, plus 90 days of optimization support.
3-4 hours of focused interviews, plus review and testing. Less than methodology extraction because your guests already did the talking—we're structuring what they said, not excavating your implicit frameworks.
Yes. If your interviews are substantive—guests sharing real positions, expertise, predictions—there's a lot to extract. If they're surface-level promotional conversations, there's less. Garbage in, garbage out.
Yes. The graph handles temporal evolution. If you've interviewed someone three times over five years, we track how their positions changed. That's often the most interesting synthesis.
We extract that too. Your framing, your pushback, your recurring themes. But for interview shows, that's secondary to guest synthesis. If you're more of a neutral moderator, that layer might be thinner—and that's fine.
Not yet—you'd be among the first. The synthesis technology is proven on large content archives. The interview podcast application is new. We're being direct about that.
That's fine. Most interview hosts we've talked to care more about their own access to the archive than monetization. The research/prep tool might be the whole use case. We build what you need.
On a call, I can walk through the Vitale knowledge graph and show you how synthesis works—then discuss how that would translate to an interview archive with multiple guests instead of one expert's methodology.
Invisible Minds — Expertise Extraction for Interview Podcasters