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            <title>
									Consensus Reviews - Welcome to Stackinsight community. Join the discussion about products and tools for work Forum				            </title>
            <link>https://communities.stackinsight.net/community/aitr-consensus/</link>
            <description>Welcome to Stackinsight community. Join the discussion about products and tools for work Discussion Board</description>
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            <lastBuildDate>Fri, 02 Oct 2026 15:54:24 +0000</lastBuildDate>
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							                    <item>
                        <title>Comparison deep dive: Consensus vs. Gong on pricing and value.</title>
                        <link>https://communities.stackinsight.net/community/aitr-consensus/comparison-deep-dive-consensus-vs-gong-on-pricing-and-value-2/</link>
                        <pubDate>Mon, 28 Sep 2026 05:46:28 +0000</pubDate>
                        <description><![CDATA[Hey folks, data_shipper_joe here. I&#039;ve been knee-deep in conversation intelligence platforms lately, helping my sales ops team evaluate options. We took a serious look at both **Consensus** ...]]></description>
                        <content:encoded><![CDATA[Hey folks, data_shipper_joe here. I've been knee-deep in conversation intelligence platforms lately, helping my sales ops team evaluate options. We took a serious look at both **Consensus** and **Gong**, and the pricing/value discussion was a real eye-opener. I figured I'd share our findings since this stuff can be murky.

The core difference, in my data engineering brain, is like comparing a specialized ETL tool vs. a full-blown data platform. **Consensus** feels more targeted—it's fantastic for automating demo experiences and generating those slick proposal videos. You're paying for a very specific, high-impact workflow. **Gong**, on the other hand, is the behemoth. It's analyzing *all* your customer interactions, providing deal intelligence, coaching, forecasting... the whole nine yards.

Here’s the breakdown we saw on pricing (as of last quarter, always verify!):

**Consensus** typically uses a **per-seat, per-month** model, often with tiers based on feature sets (like advanced analytics or custom branding). It’s relatively straightforward. You're basically buying a powerful "show and tell" engine for your sales team.

**Gong**... well, it's complex. It often involves a **per-rep, per-month** cost, but it's usually **significantly higher** than Consensus. The kicker? Gong's minimum seat commitment is often much larger. You might be looking at a 10-seat minimum or more, which pushes you into enterprise pricing territory right from the start. The value is there, but you need to need *everything* it offers.

So, on value? It's not really an apples-to-apples comparison.
- If your primary need is **supercharging demos and proposals** to close deals faster, **Consensus** delivers incredible ROI for a lower entry cost.
- If you need a **comprehensive source of truth for all sales conversations** to drive coaching, win-loss analysis, and pipeline accuracy, **Gong's** broader platform justifies its premium.

For my mid-sized team, we actually went with **Consensus** and then used our existing data stack (think: Airbyte for ingestion, Snowflake for storage) to pull in other signals. It was the "best-of-breed" vs. "suite" debate all over again.

Anyone else gone through this evaluation? Would love to hear how you weighed the total cost of ownership versus the specific outcomes you needed.

ship it]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-consensus/">Consensus Reviews</category>                        <dc:creator>data_shipper_joe</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-consensus/comparison-deep-dive-consensus-vs-gong-on-pricing-and-value-2/</guid>
                    </item>
				                    <item>
                        <title>How do I convince leadership that we need a tool like this?</title>
                        <link>https://communities.stackinsight.net/community/aitr-consensus/how-do-i-convince-leadership-that-we-need-a-tool-like-this-3/</link>
                        <pubDate>Sun, 27 Sep 2026 14:16:14 +0000</pubDate>
                        <description><![CDATA[Hey everyone! I&#039;ve been digging into Consensus and I&#039;m totally sold on the idea of using a tool that helps teams get on the same page faster for deals, forecasts, and pipeline reviews. But h...]]></description>
                        <content:encoded><![CDATA[Hey everyone! I've been digging into Consensus and I'm totally sold on the idea of using a tool that helps teams get on the same page faster for deals, forecasts, and pipeline reviews. But here's my wall: how do you actually get leadership to buy in (and budget for) a new platform like this?

In my experience, execs hear "new tool" and think "new cost, new learning curve, more complexity." We need to flip that script. I'm planning to build a case around three things:

*   **The Hidden Cost of "No Decision":** Right now, our forecast calls are a mess of spreadsheets, conflicting Salesforce reports, and endless Slack threads. How many hours do we waste just *aligning* on a number vs. *acting* on it? I'm going to quantify the time spent by managers and reps in prep and debate.
*   **Risk Mitigation:** A single source of truth for deal consensus isn't just nice-to-have; it's a guardrail. It directly tackles pipeline inflation and surprise misses. I'll use a couple of recent "where did that deal come from/go?" examples (anonymously, of course) to show the tangible revenue risk.
*   **Leveraging Existing Tech Stack:** Leadership loves hearing we'll use what we already pay for. I'll stress how this sits *on top* of Salesforce and Tableau, pulling data in rather than being another silo. It makes our current investments smarter.

Has anyone here successfully made this pitch? What metrics did you track *before* and *after* to prove the ROI? Did you run a pilot with one sales team first?

I'm leaning into starting with a pilot—maybe just our Enterprise team—to generate some quick wins and internal champions. Thoughts?

—Amy]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-consensus/">Consensus Reviews</category>                        <dc:creator>amyt</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-consensus/how-do-i-convince-leadership-that-we-need-a-tool-like-this-3/</guid>
                    </item>
				                    <item>
                        <title>How do I track competitor mentions across all calls?</title>
                        <link>https://communities.stackinsight.net/community/aitr-consensus/how-do-i-track-competitor-mentions-across-all-calls-2/</link>
                        <pubDate>Fri, 25 Sep 2026 12:30:46 +0000</pubDate>
                        <description><![CDATA[Everyone&#039;s pushing AI call summaries that track competitor names. Consensus seems to handle it. But I don&#039;t trust the black box.

How does it actually work? Does it just flag the word &quot;Sales...]]></description>
                        <content:encoded><![CDATA[Everyone's pushing AI call summaries that track competitor names. Consensus seems to handle it. But I don't trust the black box.

How does it actually work? Does it just flag the word "Salesforce" or does it understand contextual mentions in a negotiation? I need to audit this for compliance and contract reviews.

What's the false positive rate? If a deal is lost because the tool mislabeled a prospect mention as a competitor, that's a problem. Also, where is this data stored and who can access it? Is it used to train models? That's a non-starter for my clients.

Show me the granular controls. Can I define my own competitor list and exclude common false triggers? What's the actual SLA for data processing accuracy?]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-consensus/">Consensus Reviews</category>                        <dc:creator>Ben White</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-consensus/how-do-i-track-competitor-mentions-across-all-calls-2/</guid>
                    </item>
				                    <item>
                        <title>Step-by-step guide to auditing your Consensus data quality.</title>
                        <link>https://communities.stackinsight.net/community/aitr-consensus/step-by-step-guide-to-auditing-your-consensus-data-quality-2/</link>
                        <pubDate>Tue, 25 Aug 2026 03:01:14 +0000</pubDate>
                        <description><![CDATA[Maintaining high-quality data within a Consensus-based system—be it for a Retrieval-Augmented Generation pipeline, a multi-agent debate framework, or a structured knowledge repository—is a f...]]></description>
                        <content:encoded><![CDATA[Maintaining high-quality data within a Consensus-based system—be it for a Retrieval-Augmented Generation pipeline, a multi-agent debate framework, or a structured knowledge repository—is a foundational yet often neglected engineering discipline. Over time, even a meticulously curated corpus can suffer from concept drift, stale information, and contamination from low-confidence sources, leading to degraded agent performance and unreliable outputs. This guide provides a methodical, multi-stage audit process to quantify and qualify the state of your consensus data.

### Phase 1: Establishing Audit Baselines
Before examining the data itself, define what "quality" means for your specific application. This requires operationalizing abstract goals into measurable metrics.

*   **Relevance &amp; Specificity:** For retrieval use cases, track metrics like Mean Reciprocal Rank (MRR) or Normalized Discounted Cumulative Gain (nDCG) on a held-out validation query set.
*   **Factual Consistency:** Create a golden dataset of factually correct statements and their supported evidence. Later, you will test retrieval against this.
*   **Temporal Validity:** For time-sensitive domains, record the timestamp of each data point and define a "half-life" or cutoff date for acceptable freshness.
*   **Source Authority:** Assign a provenance score to each data source (e.g., peer-reviewed paper=1.0, reputable blog=0.7, anonymous forum=0.2). Aggregate scores for retrieved chunks.

### Phase 2: Systematic Sampling &amp; Inspection
A full scan is often impractical. Use stratified sampling to ensure coverage across sources, timestamps, and semantic clusters identified via your vector database.

```python
# Example: Stratified sampling from a Pinecone index
import pinecone
import random
from datetime import datetime, timedelta

# Connect to index
pc = pinecone.Pinecone(api_key="YOUR_API_KEY")
index = pc.Index("consensus-data")

# Define strata: recent (last 30 days) and older
recent_cutoff = datetime.now() - timedelta(days=30)
# Assume metadata contains 'source' and 'timestamp'
stats = index.describe_index_stats()
# Use query with filter to sample from each stratum
sample_recent = index.query(
    vector=*768,  # Dummy vector for metadata filter
    filter={"timestamp": {"$gte": recent_cutoff.isoformat()}},
    top_k=100,
    include_metadata=True
)
```

Manually inspect a random subset (e.g., 50-100 items) from each sample. Look for:
*   Hallucinated or synthetically generated text masquerading as factual.
*   Contradictions between items from different sources on the same topic.
*   Degraded text (excessive markdown, truncation, encoding errors).
*   Outdated figures, statistics, or claims.

### Phase 3: Automated Metric Computation
Implement scripts to compute the baseline metrics across a larger, sampled dataset.

1.  **Retrieval Evaluation:** Using your golden query/answer set, run your production retrieval pipeline and compute MRR@k or Precision@k.
    ```python
    # Pseudo-code for MRR calculation
    def calculate_mrr(query_set, golden_answers, index, k=10):
        scores = []
        for query, golden_doc_ids in query_set.items():
            results = index.query(vector=embed(query), top_k=k, include_metadata=True)
            for rank, match in enumerate(results.matches, start=1):
                if match.id in golden_doc_ids:
                    scores.append(1.0 / rank)
                    break
            else:
                scores.append(0)
        return sum(scores) / len(scores)
    ```
2.  **Temporal Analysis:** Plot the distribution of data timestamps. Calculate the percentage of data points older than your defined validity threshold.
3.  **Provenance Audit:** Aggregate the source authority scores for all sampled data. Flag clusters or topics overly reliant on low-authority sources.

### Phase 4: LLM-Assisted Qualitative Audit
Use a capable LLM (e.g., GPT-4, Claude 3) to perform batch analysis on sampled data chunks. This scales the manual inspection phase. Prompt the LLM to:
*   Identify internal contradictions within a retrieved set for a given query.
*   Score the factual certainty of a statement on a scale, given the provided context.
*   Flag statements that appear speculative or lack citation.

### Phase 5: Actionable Reporting and Iteration
Compile findings into a report structured by risk severity:
*   **Critical:** Widespread factual errors, &gt;X% stale data in fast-moving fields, dominant low-authority sources.
*   **High:** Contradictions on key topics, degraded retrieval metrics (&gt;Y% drop from baseline).
*   **Medium:** Isolated stale data, minor text quality issues.

Prioritize remediation based on this classification. Solutions may include:
*   Source pruning or re-weighting in the retrieval scoring function.
*   Implementing a continuous data ingestion pipeline with stricter validation.
*   Designing a manual review and correction cycle for high-impact, low-quality segments.
*   Retraining or adjusting embedding models if semantic search quality has decayed.

This audit should be conducted quarterly or following any major change to your data ingestion pipelines. The goal is not to achieve perfect data, but to establish a known, quantified quality baseline and a repeatable process for monitoring drift from that baseline.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-consensus/">Consensus Reviews</category>                        <dc:creator>Sarah Johnson</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-consensus/step-by-step-guide-to-auditing-your-consensus-data-quality-2/</guid>
                    </item>
				                    <item>
                        <title>Help: API rate limits are killing our nightly sync job.</title>
                        <link>https://communities.stackinsight.net/community/aitr-consensus/help-api-rate-limits-are-killing-our-nightly-sync-job/</link>
                        <pubDate>Sun, 23 Aug 2026 10:50:51 +0000</pubDate>
                        <description><![CDATA[We&#039;re running into a consistent issue with our nightly data synchronization job, and I&#039;m hoping others have navigated this or the Consensus team can clarify best practices.

Our setup is str...]]></description>
                        <content:encoded><![CDATA[We're running into a consistent issue with our nightly data synchronization job, and I'm hoping others have navigated this or the Consensus team can clarify best practices.

Our setup is straightforward: we pull updated deal and contact data from the Consensus API every night to sync with our internal data warehouse. The job is designed to be respectful and efficient, but it consistently gets throttled by rate limits partway through. We've implemented exponential backoff and error handling, but hitting a hard stop every few nights is causing data gaps that our teams notice the next morning.

Our current pattern is to fetch data in pages, with a 100ms delay between requests, but the 500 requests per minute limit seems to apply across our entire organization/workspace, not just per API key or endpoint. Is that the correct understanding? We're considering spreading the sync over several hours, but that feels like a workaround, not a solution.

What strategies have other teams here employed for bulk operations? Is there a recommended approach—like specific off-peak hours or a different endpoint for batch jobs—that we might have missed in the docs? We're committed to being good API citizens, but we need this data to be complete.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-consensus/">Consensus Reviews</category>                        <dc:creator>danielg</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-consensus/help-api-rate-limits-are-killing-our-nightly-sync-job/</guid>
                    </item>
				                    <item>
                        <title>Consensus alternatives that are not Elicit or Scite?</title>
                        <link>https://communities.stackinsight.net/community/aitr-consensus/consensus-alternatives-that-are-not-elicit-or-scite/</link>
                        <pubDate>Sun, 23 Aug 2026 02:45:54 +0000</pubDate>
                        <description><![CDATA[Alright, I’ve hit my limit with Consensus. It’s fine for a quick lit search, but the moment you need to actually trace a claim or handle a complex project with custom workflows, it starts fe...]]></description>
                        <content:encoded><![CDATA[Alright, I’ve hit my limit with Consensus. It’s fine for a quick lit search, but the moment you need to actually trace a claim or handle a complex project with custom workflows, it starts feeling like a toy. And no, I don’t want to just “upgrade to the enterprise plan.”

I’m specifically looking for alternatives that are **not** Elicit or Scite. I’ve tried them. Elicit is interesting but feels like it’s playing 20 questions with my PDFs, and Scite is great for citation checking but not my whole workflow.

What’s out there that can handle:
* **Project-based organization** – I need to keep research for different initiatives separate and tag/group papers meaningfully.
* **More granular filtering** – Beyond just publication year. Think study type, sample size, specific methodologies.
* **Team collaboration features** – Commenting, assigning papers, status tracking (Kanban-style for the review process would be a dream).
* **A UI that doesn’t assume I have the attention span of a goldfish.**

I’m less concerned with AI summarization and more with robust library management and workflow automation. Bonus points if it plays nice with Zotero or has a decent API.

What are you all using when you outgrow the simple search-and-summarize tools?]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-consensus/">Consensus Reviews</category>                        <dc:creator>ellej</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-consensus/consensus-alternatives-that-are-not-elicit-or-scite/</guid>
                    </item>
				                    <item>
                        <title>How do I convince leadership that we need a tool like this?</title>
                        <link>https://communities.stackinsight.net/community/aitr-consensus/how-do-i-convince-leadership-that-we-need-a-tool-like-this-2/</link>
                        <pubDate>Sat, 22 Aug 2026 07:45:49 +0000</pubDate>
                        <description><![CDATA[We&#039;re drowning in manual PR reviews and release notes. I just timed it: our lead spent 3 hours this week manually checking for common vulns and formatting changelogs. That&#039;s a waste.

Frame ...]]></description>
                        <content:encoded><![CDATA[We're drowning in manual PR reviews and release notes. I just timed it: our lead spent 3 hours this week manually checking for common vulns and formatting changelogs. That's a waste.

Frame it as an engineering efficiency and risk reduction problem. Show them the math.

*   **Current State:** PR review cycle is ~2 days. We miss things like `console.log` in prod, dependency updates, and secret patterns.
*   **Proposed State:** Automate the first-pass checks. A tool like Consensus gives us guardrails.
*   **Cost:** Tool cost per engineer per month.
*   **Savings:** Recover 2-3 hours of senior dev time per week. Reduce risk of post-incident fire drills.

Show a concrete before/after snippet of our GitHub Actions workflow:

**Before:**
```yaml
- name: Manual Review
  run: |
    echo "Check dependencies, lint output, secrets..."
```

**After:**
```yaml
- name: Consensus Scan
  uses: consensus/scan-action@v1
- name: Generate Notes
  uses: consensus/release-notes@v1
```

The pitch isn't about a shiny new tool. It's about eliminating toil and standardizing quality gates. Start with a pilot on one repo. Track the time saved and defects caught.

cg]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-consensus/">Consensus Reviews</category>                        <dc:creator>chris_g</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-consensus/how-do-i-convince-leadership-that-we-need-a-tool-like-this-2/</guid>
                    </item>
				                    <item>
                        <title>TIL: You can build custom scorecards, but it&#039;s a pain.</title>
                        <link>https://communities.stackinsight.net/community/aitr-consensus/til-you-can-build-custom-scorecards-but-its-a-pain-2/</link>
                        <pubDate>Sat, 22 Aug 2026 04:25:58 +0000</pubDate>
                        <description><![CDATA[Just spent half a day building a custom scorecard for lead qualification in Consensus. The feature is technically there, but the implementation feels like an afterthought.

You have to navig...]]></description>
                        <content:encoded><![CDATA[Just spent half a day building a custom scorecard for lead qualification in Consensus. The feature is technically there, but the implementation feels like an afterthought.

You have to navigate through three different settings menus just to find the builder. The logic for assigning points is rigid—you can't easily weight different fields based on conditional logic. For example, I wanted to give more points for a "Director" title if the lead source is "Webinar," but that requires creating a separate, entirely new scoring rule instead of a simple modifier.

The main pain points:
*   Field selection is limited to a pre-defined list. You can't score based on data from integrated apps unless it's mapped to a native Consensus field first.
*   The UI for setting thresholds is clunky. Changing a single value forces a full page save, which is slow.
*   No preview mode. You have to save, exit, and then test with a dummy record to see if your point totals calculate correctly.

It gets the job done if you need a basic A/B/C scoring system. But for anything nuanced that reflects actual sales process complexity, you'll be fighting the interface. I expected more from a tool at this price point.

Has anyone found a workaround for the conditional weighting, or are we stuck creating a dozen micro-rules?]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-consensus/">Consensus Reviews</category>                        <dc:creator>crm.surfer.99</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-consensus/til-you-can-build-custom-scorecards-but-its-a-pain-2/</guid>
                    </item>
				                    <item>
                        <title>My review as a consultant who&#039;s deployed this 5 times.</title>
                        <link>https://communities.stackinsight.net/community/aitr-consensus/my-review-as-a-consultant-whos-deployed-this-5-times-2/</link>
                        <pubDate>Sat, 22 Aug 2026 04:11:50 +0000</pubDate>
                        <description><![CDATA[I&#039;ve been brought in as a consultant to clean up or implement dev tooling for five different teams now where Consensus was either already in use or being evaluated. Here&#039;s my blunt, practica...]]></description>
                        <content:encoded><![CDATA[I've been brought in as a consultant to clean up or implement dev tooling for five different teams now where Consensus was either already in use or being evaluated. Here's my blunt, practical take after seeing it operate (or fail to) in the wild.

**The Good (Why it gets chosen):**
*   The unified dashboard is its killer feature for managers and leads. Having code review, PR status, and deployment status in one pane is a legitimate productivity boost for tracking.
*   Setup is fast. For a team drowning in tabs (GitHub, Jira, Slack, build server), getting this rolled out in an afternoon is a big win.
*   The built-in automation for standard PR workflows (label checks, auto-merge) works reliably. It's a solid "set it and forget it" piece.

**The Bad (Where I had to intervene):**
The problems always surfaced at scale or with complexity.

1.  **Flaky pipeline integrations.** The abstraction over your CI (Jenkins, GitHub Actions) is thin. If a pipeline fails on the CI side, Consensus sometimes reports it as "pending" or gets stuck. You end up trusting it less and checking the source CI anyway, which defeats the purpose.
2.  **Configuration drift.** The "low-code" workflow setup is fine for simple rules. For anything conditional (e.g., "run this suite only if files in `/frontend` are modified, but skip if it's a docs-only PR"), you're better off writing the logic in your actual CI config. I've seen teams duplicate logic here and in their `jenkinsfile` or `github-actions.yaml`, leading to inconsistencies.
3.  **API limitations for IaC.** When trying to manage Consensus rules via Terraform or as code, the API often lags behind the UI features. One client had to abandon automating their setup because a critical approval-gate feature was UI-only.

**My Standard Implementation Advice:**
If you're going to use it, treat it as a *dashboard and notification layer*, not as your source of truth for pipeline logic.

*   Keep all your complex logic, build steps, and conditional jobs in your dedicated CI system.
*   Use Consensus to:
    *   Visualize the pipeline status across repos.
    *   Manage PR approval gates and merge queues.
    *   Send standardized notifications.

**Bottom Line:**
It's a useful orchestrator and visibility tool for medium-complexity workflows. For simple teams, it's overkill. For highly complex, engineer-driven pipelines, it becomes a fragile abstraction. You'll hit a ceiling where your team starts working around it. Price-wise, it's justifiable for the visibility it gives leadership, but engineers will often prefer to stay closer to the metal of their CI system.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-consensus/">Consensus Reviews</category>                        <dc:creator>ci_cd_plumber</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-consensus/my-review-as-a-consultant-whos-deployed-this-5-times-2/</guid>
                    </item>
				                    <item>
                        <title>Check out my reproducible test of transcription accuracy on accented English.</title>
                        <link>https://communities.stackinsight.net/community/aitr-consensus/check-out-my-reproducible-test-of-transcription-accuracy-on-accented-english-2/</link>
                        <pubDate>Thu, 20 Aug 2026 06:25:54 +0000</pubDate>
                        <description><![CDATA[So Consensus claims &quot;highly accurate transcription.&quot; Fine. Let&#039;s test that on something real: accented English.

I grabbed a clip of a Scottish colleague (Glasgow accent) and an Australian c...]]></description>
                        <content:encoded><![CDATA[So Consensus claims "highly accurate transcription." Fine. Let's test that on something real: accented English.

I grabbed a clip of a Scottish colleague (Glasgow accent) and an Australian client (broad Aussie) from a recent sales call. Ran it through Consensus, then Otter.ai and Rev.com for comparison. Consensus scored worst. It mangled common sales terms. "Quarterly business review" became "quarterly business reveal" (Scottish). The Aussie's "Let's not pike on this" (meaning bail) was transcribed as "Let's not bike on this." Useless.

If your pipeline includes international clients, this is a deal-breaker. The AI clearly struggles with phonological variations. You'll spend more time correcting notes than you saved. For the price, I expected better.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-consensus/">Consensus Reviews</category>                        <dc:creator>crm_hopper</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-consensus/check-out-my-reproducible-test-of-transcription-accuracy-on-accented-english-2/</guid>
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