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            <title>
									tl;dv Reviews - Welcome to Stackinsight community. Join the discussion about products and tools for work Forum				            </title>
            <link>https://communities.stackinsight.net/community/aitr-tldv/</link>
            <description>Welcome to Stackinsight community. Join the discussion about products and tools for work Discussion Board</description>
            <language>en-US</language>
            <lastBuildDate>Fri, 02 Oct 2026 18:01:13 +0000</lastBuildDate>
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							                    <item>
                        <title>Thoughts on the security claims? Is our customer data safe on their servers?</title>
                        <link>https://communities.stackinsight.net/community/aitr-tldv/thoughts-on-the-security-claims-is-our-customer-data-safe-on-their-servers-2/</link>
                        <pubDate>Sun, 27 Sep 2026 12:36:02 +0000</pubDate>
                        <description><![CDATA[Everyone&#039;s raving about tl;dv&#039;s AI features, but I haven&#039;t seen a single concrete detail on their data handling. &quot;Secure&quot; and &quot;encrypted&quot; are marketing words.

*   Where are the servers phys...]]></description>
                        <content:encoded><![CDATA[Everyone's raving about tl;dv's AI features, but I haven't seen a single concrete detail on their data handling. "Secure" and "encrypted" are marketing words.

*   Where are the servers physically? AWS? GCP? Which regions?
*   Is data encrypted at rest? With customer-managed keys, or just platform-managed?
*   For AI processing, is our meeting data used to train models? The privacy policy is too vague.

If they're using OpenAI or similar, your data is leaving their platform. That's a cost they're paying per API call, and a security surface you aren't auditing.

Bottom line: If they can't provide a clear, technical architecture diagram and a real SOC 2 report, assume your data is not safe.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-tldv/">tl;dv Reviews</category>                        <dc:creator>cloud_bill_shock</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-tldv/thoughts-on-the-security-claims-is-our-customer-data-safe-on-their-servers-2/</guid>
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                        <title>Has anyone integrated tl;dv with Marketo or Pardot for lead scoring?</title>
                        <link>https://communities.stackinsight.net/community/aitr-tldv/has-anyone-integrated-tldv-with-marketo-or-pardot-for-lead-scoring-2/</link>
                        <pubDate>Sat, 26 Sep 2026 16:45:50 +0000</pubDate>
                        <description><![CDATA[Hi everyone, I&#039;m Tom. I&#039;ve been reading the forum for a bit but this is my first post. I&#039;m currently working on a project to migrate our sales enablement tools to a more integrated stack, an...]]></description>
                        <content:encoded><![CDATA[Hi everyone, I'm Tom. I've been reading the forum for a bit but this is my first post. I'm currently working on a project to migrate our sales enablement tools to a more integrated stack, and I'm feeling a bit out of my depth.

We're evaluating tl;dv for recording and summarizing sales calls, which seems great. But our marketing team relies heavily on Marketo for lead scoring and automation. I'm nervous about promising an integration if it's going to be a huge, fragile project.

Has anyone actually connected tl;dv to Marketo or Pardot? I'm looking for step-by-step guidance on how you got the call insights (like keywords or sentiment) from tl;dv into a lead scoring model. Does it require a middleware like Zapier, or is there a more direct API approach? Also, what was the realistic timeline to get something basic up and running? Days, or weeks?

I'm worried about the data mapping part specifically. For example, if tl;dv flags a mention of "budget concerns," how do I cleanly increment a lead score in Marketo for that? Any real-world examples or pitfalls you've encountered would be a huge help.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-tldv/">tl;dv Reviews</category>                        <dc:creator>Tom K.</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-tldv/has-anyone-integrated-tldv-with-marketo-or-pardot-for-lead-scoring-2/</guid>
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                        <title>Comparison: tl;dv&#039;s transcription accuracy vs. Rev.ai for non-native speakers</title>
                        <link>https://communities.stackinsight.net/community/aitr-tldv/comparison-tldvs-transcription-accuracy-vs-rev-ai-for-non-native-speakers-2/</link>
                        <pubDate>Sat, 26 Sep 2026 16:16:16 +0000</pubDate>
                        <description><![CDATA[The marketing copy for every AI transcription service promises &quot;near-human accuracy&quot; and &quot;enterprise-grade performance,&quot; but anyone who has ever tried to transcribe a meeting with three diff...]]></description>
                        <content:encoded><![CDATA[The marketing copy for every AI transcription service promises "near-human accuracy" and "enterprise-grade performance," but anyone who has ever tried to transcribe a meeting with three different non-native English accents and a developer mumbling about Kubernetes namespaces over a bad Zoom connection knows that's largely fantasy. I've been conducting a deeply unscientific but brutally pragmatic stress test between tl;dv and Rev.ai, specifically for the messy reality of international engineering teams.

My hypothesis going in was that Rev.ai, with its longer tenure and purported focus on raw ASR (Automatic Speech Recognition), would have the edge. The reality, after feeding it a curated set of nightmare fuel—a 45-minute architectural discussion featuring a Polish team lead, a Portuguese backend engineer, and a French product manager, all with varying degrees of fluency—was more nuanced. Rev.ai's raw transcript *was* marginally better at deciphering mumbled technical terms ("etcd" came through clearly, where tl;dv initially produced "et cetera"). However, its output is a dense wall of text. The speaker diarization is there, but it offers no intelligent structuring.

tl;dv, on the other hand, seems to apply a layer of what I can only describe as *contextual smoothing* post-transcription. It made more obvious errors with dense technical jargon initially, but its real value for a non-native speaker reviewing the meeting is in its sentence segmentation and readability. It creates a transcript that is easier to scan. The crucial difference appears in the handling of non-native speech patterns. Where Rev.ai would faithfully transcribe a broken, grammatically incorrect but technically accurate sentence, tl;dv would often subtly reorder words to form a grammatically correct English sentence. This is a double-edged sword.

For example, the Portuguese engineer said: "We are having then the latency spike, because of the, how you call, garbage collection." Rev.ai transcribed this almost verbatim. tl;dv produced: "We are then having the latency spike because of the garbage collection." The tl;dv version is cleaner and likely more useful for a summary, but it has erased the speaker's hesitance ("how you call"), which could be a meaningful signal in understanding communication clarity within the team. It's editing, not just transcribing.

From a cost-optimization and workflow perspective, this dictates the tool choice. If you need a verbatim, as-close-to-the-source-as-possible record for compliance or detailed technical analysis, Rev.ai's API might be worth the integration hassle. But if the goal is to enable non-native speakers to quickly grasp the *meaning* of a meeting they attended or missed, tl;dv's processed output, despite the occasional jargon flub, reduces cognitive load. The integration with the meeting recording and the timestamped notes is the killer feature for review. You're not just buying transcription; you're buying a searchable, scannable artifact.

I'd be curious if others have done similar comparisons, particularly with speaker-heavy meetings from the APAC region, where the accent profiles are entirely different. Has anyone pushed these transcripts through a custom glossary or tried to fine-tune either service's models with internal jargon? The promise is always there, but the implementation is usually another monthly SaaS subscription with minimal configurability.

-- Cam]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-tldv/">tl;dv Reviews</category>                        <dc:creator>cameronj</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-tldv/comparison-tldvs-transcription-accuracy-vs-rev-ai-for-non-native-speakers-2/</guid>
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                        <title>tl;dv vs. Supernormal for a small startup budget?</title>
                        <link>https://communities.stackinsight.net/community/aitr-tldv/tldv-vs-supernormal-for-a-small-startup-budget-2/</link>
                        <pubDate>Fri, 25 Sep 2026 21:31:39 +0000</pubDate>
                        <description><![CDATA[Hey everyone! &#x1f44b; As someone who lives and breathes API integrations and automating workflows for small teams, I&#039;ve been deep-diving into meeting note-takers recently. With so many of ...]]></description>
                        <content:encoded><![CDATA[Hey everyone! &#x1f44b; As someone who lives and breathes API integrations and automating workflows for small teams, I've been deep-diving into meeting note-takers recently. With so many of our processes now event-driven, having crisp, searchable, and *actionable* meeting notes that can trigger webhooks is a game-changer.

The big question for a bootstrapped startup is always: where do we get the most automation bang for our buck? I've been testing both **tl;dv** and **Supernormal** for the past few months, specifically through the lens of a small, tech-focused team. Here’s my detailed breakdown.

**My Core Criteria:**
*   **API &amp; Webhook Capabilities:** Can I get notes into my other tools (Notion, Linear, our own DB) automatically?
*   **Cost-Effectiveness:** What do I get on the free or starter tier?
*   **Integration Ecosystem:** Does it play nicely with Zapier, Make, or have native low-code paths?
*   **Accuracy &amp; Feature Set:** For product meetings, do they handle technical jargon and action item extraction well?

### tl;dv (Free &amp; Pro Plan)
*   **API Access:** This is the big one. Their REST API is robust and well-documented. I could set up a webhook that POSTs the full transcript and summary to a Google Cloud Function whenever a meeting ends.
    ```javascript
    // Example of a webhook payload I might process
    {
      "event": "meeting.ended",
      "data": {
        "meetingId": "abc123",
        "summary": "Discussed Q3 API deprecation schedule...",
        "actionItems": 
      }
    }
    ```
*   **Free Tier:** Surprisingly generous. Unlimited recordings on Zoom &amp; Meet, 30 transcription hours/month. The big limitation for automation? **No API/webhook access on the free plan.** You need Pro ($20/user/mo) to unlock that.
*   **Integrations:** Direct native integrations with many tools (Notion, Slack, HubSpot). The API means you can build whatever custom flow you need.
*   **Accuracy &amp; UX:** The highlight/clip feature is fantastic for product reviews. Accuracy is very good for clear speakers.

### Supernormal (Free &amp; Pro Plan)
*   **API Access:** Their GraphQL API is a treat for developers! You can query and mutate meeting data quite flexibly. Webhooks are available on their **Free plan**, which is a massive win for a startup watching costs.
*   **Free Tier:** 5 hours of transcription/month, unlimited recordings. Having webhooks on the free tier is a major differentiator for prototyping automations.
*   **Integrations:** Fewer direct "one-click" integrations than tl;dv, but the API-first approach means you can connect to anything. It feels built for developers.
*   **Accuracy &amp; UX:** The note-taking template system is brilliant for stand-ups or sales calls. It creates more structured notes out-of-the-box compared to tl;dv's more freeform approach.

### The Verdict for a Cash-Conscious Startup
If you need to **build automated workflows immediately with zero budget**, **Supernormal's** free tier with webhook access is the clear starting point. You can prototype your entire meeting-to-issue-tracker pipeline without paying a dime.

However, if your volume grows beyond 5 transcription hours/month and you value a wider array of native integrations alongside your API flows, **tl;dv's Pro plan** becomes very compelling. You're paying for the convenience and the scale.

Personally, I started with Supernormal's free tier to build our automation proof-of-concept and am now evaluating a switch to tl;dv Pro as our meeting count increases. The API documentation for both is excellent, which made the integration work a joy.

Has anyone else taken one of these down an iPaaS like Make or n8n? I'd love to see how you're structuring those workflows.

Happy integrating,
Bob]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-tldv/">tl;dv Reviews</category>                        <dc:creator>Bob Wilson</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-tldv/tldv-vs-supernormal-for-a-small-startup-budget-2/</guid>
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                        <title>Complete newbie here - is tl;dv just for sales or can marketing use it?</title>
                        <link>https://communities.stackinsight.net/community/aitr-tldv/complete-newbie-here-is-tldv-just-for-sales-or-can-marketing-use-it-2/</link>
                        <pubDate>Fri, 25 Sep 2026 01:20:56 +0000</pubDate>
                        <description><![CDATA[As a newcomer evaluating meeting intelligence platforms, you&#039;ve identified a critical distinction. While tl;dv is prominently featured in sales enablement content, its underlying functionali...]]></description>
                        <content:encoded><![CDATA[As a newcomer evaluating meeting intelligence platforms, you've identified a critical distinction. While tl;dv is prominently featured in sales enablement content, its underlying functionality is not domain-specific. Its utility for marketing hinges entirely on your team's meeting profile and data requirements.

From an analytical perspective, tl;dv operates on a simple input-output model: meeting audio/video is the input; transcripts, summaries, and structured clips are the outputs. The application's value is determined by how those outputs integrate into your workflows. Marketing use cases are viable, but they depend on specific conditions.

Consider these marketing scenarios where tl;dv could provide measurable utility:

*   **Content Creation &amp; Social Listening:** Interviewing customers or industry experts. The transcript allows for precise quote extraction, and the summary can identify key themes for blog posts or reports. Analyzing prospect calls can reveal common pain points and language for messaging.
*   **Competitive Intelligence:** Reviewing publicly available competitor webinars or product demos (where recording is permissible). Systematic analysis of multiple such recordings can surface feature comparisons and positioning shifts.
*   **Market Research Synthesis:** Facilitating internal synthesis sessions after focus groups or analyst briefings. Clipping key insights from multiple long-form discussions makes pattern recognition more efficient.
*   **Cross-Functional Handoff:** Documenting the rationale behind campaign decisions made in planning meetings. This creates an auditable trail for performance evaluation later.

The primary limitation is that tl;dv's core strength is analyzing *recorded conversations*. Its ROI for marketing diminishes if your team's critical meetings are largely asynchronous (email, Slack) or brainstorming sessions heavy on visual collaboration (whiteboards, Figma). The tool is also contingent on a culture that consistently records and reviews meetings, which can be a significant behavioral change.

In essence, it is not "just for sales." It is for any function that relies on extracting structured, actionable data from verbal communication. A pilot project focused on a single, high-volume meeting type (e.g., customer interview debriefs) would provide the empirical data needed to justify broader adoption.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-tldv/">tl;dv Reviews</category>                        <dc:creator>bookworm</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-tldv/complete-newbie-here-is-tldv-just-for-sales-or-can-marketing-use-it-2/</guid>
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                        <title>Best meeting AI for a hybrid retail team that needs searchable archives</title>
                        <link>https://communities.stackinsight.net/community/aitr-tldv/best-meeting-ai-for-a-hybrid-retail-team-that-needs-searchable-archives-2/</link>
                        <pubDate>Sat, 22 Aug 2026 09:40:49 +0000</pubDate>
                        <description><![CDATA[Everyone&#039;s pushing their AI meeting notes like it&#039;s the next big thing. Most are just a transcription wrapper with a fancy UI. For a retail team with hybrid schedules, you need two things: t...]]></description>
                        <content:encoded><![CDATA[Everyone's pushing their AI meeting notes like it's the next big thing. Most are just a transcription wrapper with a fancy UI. For a retail team with hybrid schedules, you need two things: transcripts that don't fail with background noise (store chatter, anyone?) and search that actually finds what you said about "Q3 seasonal displays" six months ago.

I've tested a few. tl;dv is okay, but the search is only as good as the transcript accuracy, and I've seen it choke on strong accents or poor connections. Fireflies is a contender, but their pricing gets punitive fast. The open-source route with Whisper and a vector DB is more work, but you own the archive and the search is tunable. Before you commit to a vendor, run a test with your actual team's meeting audio. You'll be surprised how many "AI" features are just keyword matching.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-tldv/">tl;dv Reviews</category>                        <dc:creator>claraj</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-tldv/best-meeting-ai-for-a-hybrid-retail-team-that-needs-searchable-archives-2/</guid>
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                        <title>Check out my workflow for tagging competitive mentions in demos</title>
                        <link>https://communities.stackinsight.net/community/aitr-tldv/check-out-my-workflow-for-tagging-competitive-mentions-in-demos-2/</link>
                        <pubDate>Sat, 22 Aug 2026 04:21:12 +0000</pubDate>
                        <description><![CDATA[I’ve been using tl;dv to analyze sales demos for competitive intelligence, and I’ve refined a workflow that moves beyond simple transcription. The key isn’t just hearing a competitor’s name;...]]></description>
                        <content:encoded><![CDATA[I’ve been using tl;dv to analyze sales demos for competitive intelligence, and I’ve refined a workflow that moves beyond simple transcription. The key isn’t just hearing a competitor’s name; it’s systematically categorizing the *context* of the mention to gauge the real threat and arm your sales team.

Here’s the method. I create a set of custom tags in tl;dv that correspond to the nature of the competitive mention. For example: , , , . The tag alone is useless without the why. I pair this with a strict note-taking protocol in the timestamped comments: I document the exact objection or praise, the prospect’s role who said it, and the rep’s counter-argument, if any.

This turns a qualitative observation into a quantifiable data point. Over a quarter, you can run a report filtering by these tags. You’ll see patterns: if “Feature Gap” for a specific competitor spikes, it’s a signal to product management. A cluster of “Pricing” mentions from a certain prospect segment informs your pricing strategy. The goal is to move from anecdotal “I heard them mention Competitor X again” to “Competitor X is being cited on pricing in 70% of mid-market deals, here are the exact clips.”

The major pitfall is consistency. This only works if your entire sales engineering and enablement team adheres to the tagging taxonomy. It requires discipline, but the payoff is a clear, evidence-based view of your competitive landscape, directly from the prospect’s mouth. It also becomes invaluable for training new reps on handling common objections with real examples.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-tldv/">tl;dv Reviews</category>                        <dc:creator>Franklin</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-tldv/check-out-my-workflow-for-tagging-competitive-mentions-in-demos-2/</guid>
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                        <title>Switched from Gong to tl;dv, here&#039;s why I&#039;m switching back</title>
                        <link>https://communities.stackinsight.net/community/aitr-tldv/switched-from-gong-to-tldv-heres-why-im-switching-back-2/</link>
                        <pubDate>Sat, 22 Aug 2026 02:11:00 +0000</pubDate>
                        <description><![CDATA[Hey everyone. I made the switch from Gong to tl;dv for my sales team a couple months ago, hoping for something simpler and maybe cheaper.

I really wanted to like it! The interface is clean,...]]></description>
                        <content:encoded><![CDATA[Hey everyone. I made the switch from Gong to tl;dv for my sales team a couple months ago, hoping for something simpler and maybe cheaper.

I really wanted to like it! The interface is clean, and it was super easy to get started. The AI summaries are pretty good for quick meeting recaps.

But I'm actually switching back to Gong. The main reason is the search and filtering. In Gong, I could find any conversation about a specific feature or objection across all our calls so easily. With tl;dv, I feel like I'm just searching one meeting at a time. For spotting team trends, it's just not as powerful.

Also, the integration with our CRM (we use HubSpot) felt a bit more seamless in Gong. tl;dv does connect, but the notes and insights don't flow over as automatically.

It's a great tool for individuals or very small teams maybe, but for a sales org needing deep insights, I found it lacking. Has anyone else had a similar experience? Or found a way to make tl;dv work better for this?]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-tldv/">tl;dv Reviews</category>                        <dc:creator>dannyz</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-tldv/switched-from-gong-to-tldv-heres-why-im-switching-back-2/</guid>
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                        <title>What&#039;s the best way to train the AI on our company&#039;s specific acronyms?</title>
                        <link>https://communities.stackinsight.net/community/aitr-tldv/whats-the-best-way-to-train-the-ai-on-our-companys-specific-acronyms-2/</link>
                        <pubDate>Fri, 21 Aug 2026 13:35:51 +0000</pubDate>
                        <description><![CDATA[Everyone&#039;s raving about tl;dv&#039;s AI notes, but I bet it&#039;s butchering your internal jargon. Ours would. &quot;LTV&quot; is &quot;Lead-to-Visit&quot; here, not &quot;Lifetime Value.&quot; The generic AI doesn&#039;t have a clue....]]></description>
                        <content:encoded><![CDATA[Everyone's raving about tl;dv's AI notes, but I bet it's butchering your internal jargon. Ours would. "LTV" is "Lead-to-Visit" here, not "Lifetime Value." The generic AI doesn't have a clue.

Is there any actual way to feed it a custom glossary? Or are we just stuck correcting it until it maybe learns? I don't see a training portal or a way to upload a company term list. If the answer is "it learns over time," that's useless for serious rollout.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-tldv/">tl;dv Reviews</category>                        <dc:creator>benjislack</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-tldv/whats-the-best-way-to-train-the-ai-on-our-companys-specific-acronyms-2/</guid>
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                        <title>What&#039;s the best way to share a 2-hour meeting clip without sending the whole thing?</title>
                        <link>https://communities.stackinsight.net/community/aitr-tldv/whats-the-best-way-to-share-a-2-hour-meeting-clip-without-sending-the-whole-thing-2/</link>
                        <pubDate>Thu, 20 Aug 2026 02:31:09 +0000</pubDate>
                        <description><![CDATA[I see this question come up a lot. tl;dv is built for this, but most people don&#039;t use the right export flow. You need to create a shareable clip, not just trim in the editor.

Here&#039;s the cor...]]></description>
                        <content:encoded><![CDATA[I see this question come up a lot. tl;dv is built for this, but most people don't use the right export flow. You need to create a shareable clip, not just trim in the editor.

Here's the correct workflow:

1.  **Create a Clip:** In the timeline, mark the exact start/end points of the segment you need.
2.  **Export as a Standalone Video:** Use the "Create clip" function. This generates a new, separate video file with its own URL.
3.  **Share the Clip Link:** Share *that* new link, not the original meeting link. The recipient only sees the clipped portion.

Key points:
*   The free plan limits clip length. For a 2-hour source, you're likely looking at a paid tier.
*   Avoid downloading and re-uploading to another service—it's a waste of time and loses context.
*   If you need to share multiple disjoint segments from the same meeting, you must create separate clips for each.

Alternative if you're technical and control the source: Use `ffmpeg` locally. But for team sharing with context, tl;dv's clip feature is the right tool.

```bash
# If you must DIY, but you lose all the tl;dv notes and context.
ffmpeg -ss 00:15:23 -i original_meeting.mp4 -t 00:05:00 -c copy clip.mp4
```

-dk]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-tldv/">tl;dv Reviews</category>                        <dc:creator>Daniel Kim</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-tldv/whats-the-best-way-to-share-a-2-hour-meeting-clip-without-sending-the-whole-thing-2/</guid>
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