Look, I’m going to be straight with you. The traditional article is powerful. But it’s only one way to present your reporting.

You spend weeks on an investigation. You publish 3,000 sharp words. But what happens to the data behind it? The full timeline? The quotes that didn’t fit?

What if you could turn that investigation into an interactive experience, complete with clickable timelines, hover-activated charts and tagged insights, in about 20 minutes?

With vibe coding, it’s possible.

What is vibe coding?

The term vibe coding came out of developer culture, but it is no longer just for developers. It’s for anyone who wants to tell a story that harnesses the power of coding.

When you vibe code, you’re building an application with the help of AI by focusing on what you want it to do. Rather than coding with HTML, JavaScript or other technical languages, a builder describes the user experience in plain language to a Large Language Model (or LLM).

You might type a prompt like, “Build me an interactive timeline showing [x, y, z] events.”

Here’s what worked for the journalists I’ve seen succeed with vibe coding:

• Step 1: Pick something simple. Don’t try to rebuild your entire investigation. Start with one article, dataset or interview.
• Step 2: Use a basic prompt structure, like: “Build an interactive [website/dashboard/story] that shows [your content] in [style you want]. Focus on [what matters most].”
• Step 3: After you have something simple, iterate 3-5 times. First pass: structure. Second: visual style. Third: functionality. Fourth: polish.
• Step 4: Share your creation with a colleague. Don’t talk, just watch. See if they click around. If they get stuck, that means you built it for yourself, not your audience. Time to iterate again or start over.

Why vibe coding and journalism make sense

When I taught vibe coding through the Google News Initiative AI Lab, I watched journalists with zero coding experience build interactive financial dashboards, data visualizations and branded microsites, all in about 90 minutes.

“This would have taken our dev team a month,” one person told me. “I did it during our session.”

While you can move quickly to an initial application with vibe coding, you still want to get your product or development support staff on board before launching. The real benefit is that vibe coding lets you prototype faster to see if your idea works before needing to commit resources.

This matters because most newsrooms don’t have a developer on speed dial. At the Adirondack Explorer, a small regional outlet covering New York’s Adirondack Park, journalists are building a civic information product that aggregates town meeting recordings, transcripts and minutes across dozens of municipalities. That kind of project would normally require hiring contractors or a dedicated dev team. Instead, their reporters are building it themselves.

When I worked with VTDigger through the Google News Initiative, they automated campaign emails across four audience segments, work that directly generated $40,000 in donations that likely wouldn’t have happened with manual effort alone. Vibe coding turns “we can’t afford to build that” into “let me show you what I made this morning.”

Think of vibe coding as a creative prototyping partner. Get to 80% quickly. Then decide if you need developer support to get to 100%.

Tips for those who are new to vibe coding

I’m repeating this because it’s important: Start simple. Pick one piece of your big investigation. It could be a dense PDF that needs to be more accessible. It could be data sitting in a spreadsheet. It could even be an interview transcript with great quotes that couldn’t all make it into the article.

When describing what I want to the LLM, I get experimental with my prompts. I’ll type things like, “use the most modern UI and UX interactions and animations to make my charts and graphs more interesting and allow me to parse through the data visually.” Or, “build this in the style of a high-end investigative journalism piece meets Wired magazine’s data viz.”

Then I iterate with edits like, “change the color scheme to match our brand,” or, “pull out more quotes from the sources,” or even, “make [x, y, z] section more prominent.”

Three high-level no-no’s when vibe coding

1. Never use it for final production without verification.
2. Don't use it for anything requiring real-time data without a proper backend.
3. Never publish AI-generated content without independent verification. In high-stakes areas like health, legal, financial or public safety, errors can cause real harm.

A Google AI Overview recently told pancreatic cancer patients to avoid high-fat foods, which is the exact opposite of what oncologists recommend and could jeopardize a patient’s ability to tolerate chemotherapy.

AI can generate beautiful visualizations, but it can also confidently present wrong numbers. For anything where errors could harm your readers, verify everything against primary sources.

Vibe coding tools to try

The main platform I use for vibe coding is Lovable.dev. For a simple interactive graphic such as a timeline, searchable transcript or basic data visualization, you can expect to use roughly 3-8 credits to produce a solid prototype.

More complex builds with multiple views, filtering or light database features can take 15-30 credits depending on how much you iterate. The free tier is typically enough to experiment with small projects, while paid plans make sense if you’re building regularly or refining more advanced applications.

Bolt.new is another tool worth knowing. For a simple interactive project, such as a timeline or basic data visualization, you might use roughly 20,000 to 60,000 tokens depending on how much you iterate. More complex builds with custom logic, multiple components or repeated revisions can exceed 100,000 tokens. The free tier is generally sufficient for small experiments, while larger or ongoing projects may require a paid plan.

Bolt tends to give you more control over the code and works well if you want to edit things directly. Lovable is more beginner-friendly with a cleaner interface for non-technical users.

Both tools let you attach content like article text, CSV files or transcripts, describe what you want in plain language, and get a working prototype you can publish immediately.

You might wonder why you need these tools when you already have ChatGPT or Claude. The difference is output.

When you ask ChatGPT to build you a dashboard, it gives you code snippets you’d need to assemble yourself, often requiring a developer to make sense of it. When you ask Lovable the same thing, you get a working application with a live preview, hosting, and a chat interface to iterate on it.

Lovable is actually powered by Claude, but it wraps the AI in a full-stack builder that handles deployment, databases and design. For journalists without coding experience, that’s the difference between “here’s some code” and “here’s how it looks.”

Go build something new

The article format has served us well. It’s not dead, but it is not the only option.

When thousands of people fly to a conference, share incredible insights and then go home, that knowledge evaporates unless it’s transformed into something people can continue to engage with.

Vibe coding lets us do that better than text-only articles can. Not just for conferences, but for city council meetings, investigative data, community journalism and breaking news.

My hope is you’ll take these vibe coding tips and run. You’ll build interactive story formats your newsroom has never seen. You’ll prototype tools that solve real problems. You’ll make journalism more engaging, more accessible and more honest about its data and sources.

Journalists who can write, build, prototype, ship and transform their own work into new formats will define what news looks like in five years. So, go vibe code something. I can’t wait to see what you build.

This post first appeared in News Media Help Desk.

The post Vibe coding for journalists: Build interactive stories without writing a single line of code appeared first on The Media Copilot.

Zetland faced a productivity crisis hiding in plain sight. The Danish digital outlet publishes primarily audio-based journalism, which meant 35 reporters each conducted multiple hours of interviews weekly. Manual transcription consumed five to seven hours per journalist every week—time CEO Tav Klitgaard described as journalists “basically being robots.” Many skipped transcription entirely because the work was so tedious, weakening their reporting by forcing reliance on notes rather than recorded quotes.

When OpenAI released its Whisper speech recognition model in September 2022, Zetland developer Jakob Steinn built an overnight test later called Good Tape after a senior editor complained about transcription burden over lunch. The next morning, a journalist ran into Klitgaard’s office demanding he allocate all resources to the project because “it’s magic.” Zetland spun off Good Tape as a separate company in 2023, and the tool now serves 2.5 million users globally.

This quick reference covers Zetland‘s implementation approach, the measurable time savings achieved, and what other newsrooms should consider before adopting transcription automation.

The gist

Zetland‘s in-house development team solved a transcription crisis by building Good Tape when existing tools failed to handle Danish language audio, then:

• Saved three to six hours per journalist weekly
• Eliminated tedious manual transcription work entirely
• Scaled to 2.5 million global users within 18 months

How they did it

Zetland moved quickly from identifying the problem to building and deploying a solution that transformed newsroom operations:

• Recognized the productivity drain: Leadership calculated that 35 journalists spending five to seven hours weekly on manual transcription represented enormous wasted capacity for actual journalism work.
• Tested the alpha internally first: After Steinn built the first version, Zetland journalists tested it despite slow speed and imperfect accuracy—it was still better than manual transcription.
• Released public beta to Danish journalism community: In January 2023, Zetland asked Danish journalists to test the tool, receiving unanimous enthusiasm and requests to purchase immediately.
• Launched paid version: March 2023 launch of Good Tape Pro proved willingness to pay, with thousands signing up within minutes of the paid tier becoming available.
• Spun off as separate company: Zetland established Good Tape as independent entity to serve journalists worldwide, not just internal needs.

Key numbers

• 200+ hours saved weekly: With 35 journalists each saving three to six hours per week, Zetland reclaimed substantial reporting capacity
• 2.5 million users: Growth from internal tool to global platform within 18 months of public launch
• 90-95 percent accuracy: Typical transcription accuracy requiring minimal correction of names and technical terms
• $17/month: Pricing significantly below competitors charging $24-52 monthly

What to watch for

Implementation challenges emerged despite strong results:

• Feature limitations: Good Tape initially lacked integration with common newsroom tools like Slack and Google Drive, requiring standalone workflow
• Mobile gap: No mobile app available at launch, limiting field recording workflows (mobile app expected in fall)
• Speed vs. quality tradeoff: Early alpha version was slow; balancing transcription speed with accuracy required significant development work

Good Tape offers free testing with no commitment. Newsrooms can evaluate transcription accuracy, interface usability, and workflow fit before purchasing subscriptions. Teams of five or more qualify for custom pricing that scales with organizational size.

Frequently Asked Questions

The post How <em>Zetland</em> reclaimed 200+ journalist-hours weekly with Good Tape appeared first on The Media Copilot.

Interview transcription consumes hours that journalists don’t have. A single hour-long interview can demand three hours of manual transcription work—tedious, repetitive labor that keeps reporters from what they’re trained to do. For newsrooms conducting multiple interviews weekly, the time cost compounds. Many journalists skip transcription entirely, relying on notes and memory, risking missed quotes and weakened reporting.

Good Tape emerged from this frustration at Danish outlet Zetland, where reporters spent five to seven hours weekly transcribing audio—time they described as “being robots.” When OpenAI released its Whisper speech recognition model in September 2022, Zetland developer Jakob Steinn built a test version overnight. The next morning, a journalist ran into CEO Tav Klitgaard’s office demanding he “stop everything” and allocate resources to the project. The tool has since grown to 2.5 million users, with journalists worldwide adopting transcription that finally works for their specific needs.

This article examines why journalists choose Good Tape, drawing from user experiences and documentation that reveal what matters most: not just transcription speed, but the combination of affordability, security standards, and journalism-focused design that distinguishes tools built by newsrooms from generic business software.

1. Price point that matches newsroom budgets

Good Tape costs $17 monthly or $190 annually. That subscription includes 20 hours of transcription, unlimited file uploads, no file size restrictions, AI summaries, and speaker labels. For comparison, Otter charges similar monthly costs but caps users at 10 files. Descript charges $24 monthly for just 10 hours of transcription. Trint costs $52 monthly for only seven files.

The cost difference compounds across newsrooms. Zetland estimated saving three to six hours per journalist weekly with Good Tape—time that previously went to manual transcription. Jacob Granger, senior reporter at journalism.co.uk, used Good Tape through a five-day journalism festival with rapid-fire story production. “It just really gave me a leg up,” he said. The tool delivered what he needed without the pricing structure that forces budget-conscious outlets to ration transcription among staff.

For freelancers and small outlets where every monthly expense matters, Good Tape’s straightforward pricing removes barriers. No tiered plans requiring cost-benefit calculations. No per-file charges that penalize thorough reporting. The model assumes journalists need reliable transcription frequently, not occasionally, and prices accordingly.

2. Data security designed for source protection

Good Tape hosts its AI model on EU-based servers under European data privacy regulations, which exceed U.S. standards. The company encrypts data using AES-256, the same standard the U.S. government uses for classified information. Most critically, Good Tape never trains its AI models on user data—a commitment that distinguishes it from competitors that use de-identified recordings for model improvement.

For journalists handling confidential sources, leaked recordings can destroy careers, endanger sources, and compromise investigations. “It cannot leak and you cannot train any models on it,” Klitgaard explained. “It might be an interview with Snowden.”

Granger, who has covered how tech companies consume copyrighted material for AI training, emphasized this distinction. “I don’t think you can underestimate the value of Good Tape being very data and security conscious,” he said. When journalists give recordings to services that train AI models, “we’re giving away a very valuable part of our work” to benefit those companies. Users working under EU data privacy regulations or handling sensitive sources cannot compromise on these protections. Good Tape provides an additional security option: users can uncheck a box during upload to prevent the audio file from being saved on servers, ensuring only the transcript remains.

3. Journalism workflows inform every feature

Good Tape originated when a Zetland senior editor complained about transcription burden over lunch with developer Steinn in late 2022. The first version was slow, but Zetland journalists immediately recognized it would transform their work. When the company released the alpha version publicly in November 2022 (one day before ChatGPT launched), Danish journalists tested it and responded uniformly: “Oh my God, what’s going on?”

That newsroom origin shows in the interface design. Transcripts appear with automatic time codes approximately every 11 seconds—ideal for podcasters and broadcasters who need precise navigation. Users click any word to jump to that audio moment, essential for fact-checking quotes or finding specific soundbites. Speaker identification works throughout, though it can lag behind speaker changes by a few words. Files appear in the left sidebar, newest first, with collections for organizing related transcripts.

The AI summary feature includes time codes indicating when each topic appears in the recording, letting reporters jump directly to relevant sections. An AI chat feature (currently in beta) allows users to ask questions about transcript content or search across all stored transcripts—useful when reporters need to find themes across multiple interviews or remember what a source said weeks earlier. Granger praised Good Tape for “not fabricating information, which has happened a number of times on other platforms I’ve used. Those services do really reach for connecting dots where there aren’t dots to be connected.”

4. Multilingual transcription that actually works

English benefits from heavy AI investment because of its global reach. Smaller languages struggle with transcription accuracy. Good Tape performs well with Danish, Estonian, Finnish, Croatian, Taiwanese Mandarin, Azerbaijani, Hebrew, and other languages that major competitors ignore or handle poorly. Zetland launched publicly in Denmark first, then watched journalists worldwide test the tool in their languages and report consistently strong results.

“If you take a language like Danish or Estonian or Finnish or Croatian or this type of Mandarin that they speak in Taiwan or Azerbaijan or whatever, then you should probably look to Good Tape,” Klitgaard said. For newsrooms operating in non-English markets, this capability removes a fundamental barrier that previously made accurate transcription inaccessible or prohibitively expensive.

The tool auto-detects language, though users can manually select if needed. It handles most accents and audio quality well, though recording in quiet environments improves accuracy. The system typically achieves 90 to 95 percent accuracy, with users correcting names or technical terms during review.

5. Speed that reclaims journalist time

Good Tape transcribes in seconds for typical interview lengths. Reporters upload files by dragging them onto the web interface, and transcripts appear immediately with time codes and speaker labels. That speed matters because transcription delay creates workflow bottlenecks. Journalists conducting multiple interviews for deadline stories cannot wait hours for transcripts.

Zetland estimated each journalist saved three to six hours weekly—time previously spent on manual transcription. “You’re doing more of the journalism and less of the tedium,” Granger explained. Klitgaard described the transformation: journalists “might be spending five, six, seven hours per week basically being robots, and they hated it.” Good Tape gave them those hours back to “call two sources more or do three interviews more or just write your article through twice again.”

The time savings compound because reporters transcribe more interviews when transcription stops being prohibitively slow. Zetland noticed journalists transcribing substantially more audio after adopting Good Tape—work they previously skipped because manual transcription consumed too much time. More transcription means better sourcing, more accurate quotes, and stronger reporting. The productivity gain isn’t just about speed; it’s about enabling the thorough journalism that manual transcription makes impractical.

How Good Tape compares to major alternatives

Otter focuses on business users, trains AI models on de-identified recordings, and costs roughly the same as Good Tape but caps users at 10 files monthly. Alice offers more integration features but lacks certified data security and charges by the hour of transcription. Descript provides extensive video and audio editing features with AI training by opt-in only, but costs more. Trint includes video and audio editing plus story-building tools but runs substantially more expensive than Good Tape.

Good Tape trades integrations for simplicity and security. It doesn’t connect with Slack, Google Drive, or Microsoft Office, and currently lacks a mobile app. For newsrooms requiring advanced video editing or real-time collaboration features, alternatives may fit better. For journalists prioritizing accurate, secure, affordable transcription without feature bloat, Good Tape delivers exactly what matters.

Who should consider Good Tape

Good Tape works best for journalists who conduct multiple interviews requiring transcription, work with sensitive sources, operate under European data privacy regulations, need multilingual support, or want reliable tools without excessive cost. It excels for reporters working on long-form stories where interview material must be carefully reviewed and quoted accurately. Teams sharing interview material among multiple journalists benefit from organizational features and transcript accessibility.

The tool serves freelancers and small outlets especially well because pricing doesn’t penalize frequent use. Larger newsrooms with reporters conducting regular interviews save substantial time across staff. The tool works less well for breaking news services needing live transcription of press conferences or organizations requiring extensive video editing and real-time collaboration features. Good Tape functions as standalone software, not integrated with common newsroom tools, though that simplicity eliminates setup complexity.

Good Tape offers free testing with no strings attached. Journalists can upload recordings, review transcript quality, and assess whether the interface matches their workflow before committing to paid plans. Teams of five or more can request custom pricing that scales with organizational needs.

Frequently Asked Questions

The post Why journalists choose Good Tape for interview transcription appeared first on The Media Copilot.

Journalists conducting multiple interviews face a straightforward problem: manual transcription consumes hours that should go toward reporting, writing, or conducting additional interviews. Automated transcription tools promise to solve this, but choosing between similar-seeming services requires understanding differences that matter for journalism workflows. Two factors complicate the decision: not all transcription tools handle confidential sources appropriately, and pricing structures can penalize thorough reporting by limiting files rather than transcription hours.

Good Tape originated in Danish outlet Zetland‘s newsroom when reporters spent five to seven hours weekly on manual transcription. Developer Jakob Steinn built the first version overnight in September 2022 after OpenAI released its Whisper speech recognition model. Zetland spun off Good Tape as a separate company in 2023, and it now serves 2.5 million users globally. The tool emphasizes data security, multilingual support, and journalism-specific features like time-coded navigation optimized for quote verification.

Otter operates in the business transcription market, serving corporate meetings, interviews, and collaboration workflows. The service uses AI transcription with features designed for business users including meeting summaries, action item extraction, and team collaboration tools. Otter markets broadly to professionals who need transcription across various contexts, not specifically to journalists.

This comparison analyzes where each tool has documented advantages, what user types they serve best, and what key differences emerge from available documentation about pricing, security, and workflow design.

Where Good Tape has advantages

Good Tape’s newsroom origins translate to specific design decisions that serve journalism workflows. The tool provides unlimited file uploads with a monthly transcription hour limit (20 hours for $17 monthly or $190 annually), a structure that doesn’t penalize reporters conducting many short interviews. Otter charges similar monthly costs but caps users at 10 files, forcing journalists to choose which interviews to transcribe when covering stories that require numerous sources.

Data security represents Good Tape’s most significant documented advantage. The company hosts its AI model on EU-based servers under European data privacy regulations, encrypts data using AES-256 (the standard the U.S. government uses for classified information), and critically, never trains its AI models on user data. Users can also uncheck a box during upload to prevent audio files from being saved on servers, ensuring only transcripts remain. CEO Tav Klitgaard explained the journalism imperative: “It cannot leak and you cannot ever train on this material because it might be super sensitive. It might be an interview with Snowden.”

Good Tape performs well with languages beyond English—Danish, Estonian, Finnish, Croatian, Taiwanese Mandarin, Azerbaijani, Hebrew, and others that major competitors often handle poorly. This multilingual capability removes barriers for newsrooms operating in non-English markets where transcription tools traditionally underperformed. The tool also emphasizes simplicity, focusing on core transcription needs rather than expanding to collaboration features or video editing suites.

Where Otter has advantages

Otter’s business focus yields integration capabilities that Good Tape currently lacks. Good Tape doesn’t integrate with Slack, Google Drive, or Microsoft Office while Otter offers business collaboration workflows.

The tool’s business user base and longer market presence suggest more mature collaboration features for team environments where multiple users need to access, comment on, and share transcripts within existing workflow tools. 

Otter’s market positioning for general business transcription means it serves users beyond journalism, potentially offering features relevant to corporate meetings, presentations, and business collaboration contexts that fall outside Good Tape’s journalism-specific design priorities.

Who should consider each tool

Good Tape documentation indicates the tool works best for journalists who conduct multiple interviews requiring transcription, work with sensitive sources demanding strict data security, operate under European data privacy regulations, need multilingual transcription support, or prioritize reliable tools without excessive cost. The unlimited file structure particularly benefits reporters conducting numerous short interviews rather than occasional long recordings.

The tool serves freelancers and small outlets well because pricing doesn’t penalize frequent use. Larger newsrooms with reporters conducting regular interviews benefit from time savings that compound across staff. Jacob Granger, senior reporter at journalism.co.uk, emphasized the trust factor: “When you’ve got software that has been built by people in your profession, rather than just an abstract tech company, I think that gives you a bit more faith in the values of how they’re handling the data.”

Otter may fit better for professionals who prioritize integration with existing business tools over journalism-specific features, work in contexts where data used for AI training (even if de-identified) doesn’t pose source protection concerns, or conduct fewer than 10 transcription sessions monthly so file limits don’t constrain workflows. 

Key technical or operational differences

The pricing structures reveal different assumptions about user needs. Good Tape’s $17 monthly subscription provides 20 hours of transcription with unlimited files, assuming journalists need frequent transcription sessions. Otter charges similar monthly costs but limits users to 10 files, a structure better suited to occasional transcription needs or longer recordings.

Data handling practices differ fundamentally. Good Tape never trains AI models on user data and provides EU-based server hosting with strict European privacy compliance. Otter trains AI models on de-identified user recordings according to Good Tape’s documentation about competitors. For journalists handling confidential sources, this difference determines whether a tool can be used for sensitive interviews.

Good Tape currently lacks mobile app capabilities (expected in fall) and doesn’t integrate with common newsroom tools like Slack or Google Drive. This standalone approach prioritizes simplicity and security over ecosystem integration. 

Accuracy metrics available from Good Tape documentation show 90-95 percent typical transcription accuracy requiring minimal correction of names and technical terms. 

What the comparison doesn’t cover

This comparison relies primarily on Good Tape documentation with references to Otter’s general positioning. Questions that remain unanswered include: What specific collaboration features does Otter provide? How do the tools compare on transcription speed for equivalent audio lengths? What are Otter’s documented accuracy rates across different languages and audio quality conditions? How do the platforms handle speaker identification in multi-person interviews? What are the specific data retention policies for each service?

Organizations should review Otter’s detailed security documentation, data handling policies, and pricing tiers independently. The comparison focuses on dimensions documented in Good Tape materials, which naturally emphasize areas where Good Tape differentiates itself. A complete evaluation would require direct testing of both platforms with representative audio samples and workflow scenarios specific to each organization’s needs.Organizations evaluating transcription tools should test Good Tape free at goodtape.io and review Otter’s offerings at otter.ai. Both services offer trial periods that allow direct comparison with actual interview recordings. For newsrooms handling confidential sources, consulting IT security teams about data handling policies remains essential before committing to either platform.

Frequently Asked Questions

The post Good Tape vs Otter: Comparing transcription workflows appeared first on The Media Copilot.

When Danish outlet Zetland spun off Good Tape as a standalone transcription service in 2023, it made a bold claim: this would be transcription software that journalists could actually trust with their sources. That’s no small promise in an era where tech companies routinely harvest user data for AI training, potentially exposing confidential sources or handing competitive advantages to rivals. The question isn’t whether Good Tape delivers accurate transcription—it does—but whether newsrooms can trust it with their most sensitive material.

The stakes are particularly high for journalists. A leaked recording can destroy careers, endanger sources, and compromise months of investigative work. Meanwhile, transcription services that train their AI models on user content essentially turn journalists into unpaid data providers, feeding proprietary interviews into systems that competitors might later access. Jacob Granger, senior reporter at journalism.co.uk, put it bluntly: “If we’re feeding our transcripts into untold other generative AIs and they use that to train their model, we’re giving away a very valuable part of our work to benefit them.”

Good Tape’s approach to these concerns reflects its newsroom origins. The company hosts its infrastructure on EU-based servers, subjecting itself to GDPR and other European privacy regulations that impose stricter controls than U.S. data protection laws. But server location is just the beginning of the security equation. What matters more is how the company handles the data once it arrives, who has access to it, and what happens to it after transcription is complete.

Risks identified in Good Tape’s security posture

The primary risk with any transcription service centers on data exposure—whether through breaches, employee access, or AI training practices. Good Tape addresses the AI training concern directly: the company states it never uses customer transcripts to train its models. This differs markedly from competitors like Otter.ai, which has acknowledged using de-identified user recordings for model improvement, raising questions about whether true de-identification is even possible with voice data.

Another risk involves data retention and deletion. While Good Tape allows users to delete their transcripts from the platform, the documentation doesn’t specify retention periods for audio files or whether deletion is immediate and permanent across all backup systems. For journalists working on time-sensitive investigations or with whistleblowers, understanding exactly when and how completely their data disappears matters. The platform does offer an option to process audio without saving it—users can uncheck a box during upload to prevent audio storage—but this security-conscious feature isn’t prominently advertised.

Security controls Good Tape has implemented

Good Tape’s security framework centers on AES-256 encryption, the same standard used by the U.S. government for classified information. This encryption applies to data both in transit and at rest, meaning files are protected during upload and while stored on servers. The EU server location adds a regulatory layer of protection—European privacy laws require explicit consent for data processing and impose substantial fines for violations, creating financial incentives for compliance that don’t exist in less regulated jurisdictions.

The company’s most significant security feature may be its business model. Unlike free or ad-supported transcription services that must monetize user data somehow, Good Tape operates on straightforward subscription pricing: $17 monthly or $190 annually for 20 hours of transcription. This removes the financial pressure to extract value from user content through AI training or data brokering. CEO Tav Klitgaard emphasized this philosophy when discussing confidential sources: “It cannot leak and you cannot train any models on it.”

The platform’s authentication and access controls remain less documented. While the service requires user accounts and passwords, the available documentation doesn’t specify whether it supports two-factor authentication, single sign-on for enterprise customers, or role-based access controls for newsroom teams. These features become critical when multiple journalists share an organizational account or when newsrooms need to comply with their own security policies. The platform does maintain file organization through a sidebar system that could theoretically support user permissions, but current documentation doesn’t confirm this capability.

Security checklist for Good Tape users

Before trusting Good Tape with your newsroom’s sensitive transcripts, verify the following:

• Does your organization require SOC 2 Type II compliance?
• Do you handle data subject to GDPR/CCPA?
• Do you need data residency in specific geographic regions?
• Are you subject to industry-specific regulations (HIPAA, FERPA, etc.)?
• Do you require custom data processing agreements?
• Do you need detailed audit logs of all data access?
• Does your IT department require SSO integration?

For journalists handling particularly sensitive material, Good Tape’s option to process audio without storage provides an extra security layer—though users must remember to actively select this option with each upload.

Newsrooms should evaluate Good Tape against their specific threat models and compliance requirements. For many journalists, the combination of encryption, EU hosting, and no AI training on user data will meet their security needs. Others may require additional documentation about audit logs, incident response procedures, or enterprise security features before committing sensitive interviews to any third-party platform.

Frequently Asked Questions

The post Can you trust Good Tape with your newsroom interview transcripts? appeared first on The Media Copilot.