The company develops and sells diagnostic imaging systems to hospitals and imaging centers. Their product line is complex: multiple systems with different capabilities, software versions that update quarterly, compatibility matrices that shift with each release, and regulatory clearances that vary by market and application.<\/p>\n\n\n\n
The sales team of 65 reps handles complex B2B sales cycles. The average deal involves multiple stakeholders \u2014 radiologists, IT directors, procurement, and hospital administration. Every conversation includes product claims \u2014 specs, compatibility, regulatory status, support commitments.<\/p>\n\n\n\n
The head of sales enablement had invested in a call analysis platform that could transcribe every practice session and search for keywords. She assumed this was sufficient to catch errors. She was wrong.<\/p>\n\n\n\n
The test: finding what keyword search misses<\/strong><\/h2>\n\n\n\nShe ran a simple test. She listened to five recent practice calls, transcribed by the platform, and manually extracted every factual claim a rep made about the company’s products. Then she compared her manual extraction to what the platform’s keyword search would have caught.<\/p>\n\n\n\n
\n- Call 1:<\/strong> A rep said “it’s basically compatible with all the major EMRs.” The platform’s search for “fully compatible” found nothing. One of the major EMRs the rep had implied was supported actually wasn’t.<\/li>\n\n\n\n
- Call 2:<\/strong> A rep said “you’d get a clinical applications specialist who’s always available to help.” The platform’s search for “24\/7 support” found nothing. The actual support was business hours only, with a 24-hour response SLA.<\/li>\n\n\n\n
- Call 3:<\/strong> A rep said “it’s cleared in the US and most of Europe.” The platform’s search found the phrase “FDA cleared,” but couldn’t evaluate whether “most of Europe” was accurate \u2014 several key European markets actually required additional clearances the rep didn’t mention.<\/li>\n\n\n\n
- Call 4:<\/strong> A rep said “software updates are usually included.” Keyword search found “software updates” but couldn’t tell that “usually” was doing a lot of work \u2014 some updates were included, major version upgrades were not.<\/li>\n\n\n\n
- Call 5:<\/strong> A rep said “it works with any of the major contrast agents.” Keyword search found “contrast agents” but had no way of knowing whether “any” was accurate. Several contrast agents the rep had implied were compatible actually weren’t.<\/li>\n<\/ul>\n\n\n\n
Five calls. Five missed claims. Zero flags from the platform.<\/p>\n\n\n\n
The cost of missed claims<\/strong><\/h2>\n\n\n\nThe head of sales enablement shared her findings with the VP of Sales. He wasn’t surprised. He had been tracking post-sale issues for two quarters. In that time:<\/p>\n\n\n\n
\n- 12%<\/strong> of post-installation support tickets involved customers who received inaccurate product information during the sales process<\/li>\n\n\n\n
- 9%<\/strong> of delayed installations were caused by compatibility assumptions that turned out to be wrong<\/li>\n\n\n\n
- 7%<\/strong> of customers who churned within the first year cited “expectations set during sales didn’t match reality”<\/li>\n<\/ul>\n\n\n\n
The keyword search platform was generating reports. It wasn’t preventing errors.<\/p>\n\n\n\n
“We could search for every word we thought might be a problem. But we couldn’t search for what we didn’t know to look for. And that’s where the real mistakes were happening.”<\/p>\n\n\n\n
The intervention: replacing keyword search with claim extraction<\/strong><\/h2>\n\n\n\nThe company replaced their keyword-search platform with EOS (<\/strong>akaeos.com<\/strong><\/a>)<\/strong> \u2014 one that didn’t rely on predefined terms.<\/p>\n\n\n\nInstead of searching for specific words, EOS extracted every factual claim from each practice session and checked each one against the company’s actual product documentation:<\/p>\n\n\n\n
\n- It extracted claims, not keywords.<\/strong> The platform didn’t look for “fully compatible” or “24\/7.” It parsed natural speech into discrete, checkable propositions. When a rep said “basically compatible with all the major EMRs,” the platform extracted that as a claim about EMR compatibility. When a rep said “cleared in the US and most of Europe,” it extracted that as a claim about regulatory clearance.<\/li>\n\n\n\n
- It verified each claim against documentation.<\/strong> Every extracted claim was checked against the company’s current compatibility matrices, regulatory clearance lists, and service contract terms.<\/li>\n\n\n\n
- It returned specific verdicts with citations.<\/strong> Instead of highlighting keywords, it flagged contradicted claims with a clear citation:<\/li>\n<\/ol>\n\n\n\n
“Contradicted: Epic integration is not supported in the current version. See Compatibility Matrix v6.3, Section 2.1.”<\/p>\n\n\n\n
“Contradicted: Germany requires additional regulatory clearance. See Regulatory Status by Market, Appendix B.”<\/p>\n\n\n\n
The difference wasn’t subtle. One platform searched for words it already knew. The other identified claims it didn’t know to expect.<\/p>\n\n\n\n
The first 30 days, by the numbers<\/strong><\/h2>\n\n\n\nBecause this pilot is still early \u2014 35 reps, one month in \u2014 the results here aren’t a 90-day before\/after like a mature rollout. They’re a snapshot of what surfaced almost immediately once the company stopped searching for keywords and started extracting claims.<\/p>\n\n\n\n
![](\"blob:https:\/\/blog.themusio.com\/9ca9e612-d605-4652-858e-7ddbd8220e6d\")
The blank “before” cells aren’t a data gap \u2014 they’re the point. The keyword-search platform wasn’t measuring claim accuracy at all, so there’s no comparable prior baseline for most of these rows. That absence of a baseline is exactly what the audit in Call 1 through Call 5 exposed.<\/p>\n\n\n\n
What they found in the first month<\/strong><\/h2>\n\n\n\n\n- Reps were making claims they didn’t realize were wrong.<\/strong> One rep who had been with the company for five years discovered he had been stating a compatibility claim incorrectly for over a year \u2014 and no one had ever caught it because no one was checking.<\/li>\n\n\n\n
- The most common errors were in implied claims, not explicit ones.<\/strong> Reps rarely stated “this product is FDA cleared” incorrectly. But they frequently implied broader regulatory coverage than actually existed \u2014 “cleared in most major markets” when several key markets were still pending.<\/li>\n\n\n\n
- Managers could finally see what they couldn’t before.<\/strong> Using EOS’s manager dashboard, the sales enablement team could see which product areas generated the most contradicted claims \u2014 and could target training accordingly. Before, they were guessing. Now, they had data.<\/li>\n<\/ol>\n\n\n\n
“In the first month, we found errors that had been in the system for years. Not because reps were careless \u2014 because the old platform never checked. It wasn’t designed to. It was searching for words, not evaluating claims.”<\/p>\n\n\n\n
Is your training stack searching for keywords \u2014 or extracting claims?<\/strong> Our 12-page report, Beyond Roleplay: The Rise of the Sales Knowledge Engine<\/em>, includes the full evaluation framework, the 10-question stack audit, and the data-sovereignty questions to ask any vendor. [Download the whitepaper \u2192]<\/strong><\/a><\/p>\n\n\n\nWhy keyword search fails<\/strong><\/h2>\n\n\n\nKeyword search is useful for finding specific terms. But sales conversations aren’t written from a script. Reps paraphrase. They hedge. They use shorthand. They make implicit claims.<\/p>\n\n\n\n
A rep who says “basically compatible with all the major EMRs” is making a compatibility claim \u2014 but keyword search for “fully compatible” misses it entirely. A rep who says “cleared in the US and most of Europe” is making a regulatory claim \u2014 but keyword search for “FDA cleared” finds only part of it and can’t evaluate the rest.<\/p>\n\n\n\n
The underlying problem is structural. Keyword search doesn’t understand what was actually claimed. It only matches strings of characters. Claim extraction, by contrast, converts spontaneous speech into discrete, checkable propositions \u2014 statements specific enough that each one can independently be marked true or false against a source of truth. A transcript tells you the words. Claim extraction tells you what was actually asserted.<\/p>\n\n\n\n
The takeaway for sales leaders<\/strong><\/h2>\n\n\n\nIf your training platform relies on keyword search to catch factual errors, you have a blind spot you might not see until a post-installation issue lands on your desk.<\/p>\n\n\n\n
Keyword search only catches what it already knows to look for. It misses paraphrases, hedged language, compound claims, and implicit comparisons. A fact-grounded verification system, by contrast, doesn’t need to know what it’s looking for. It extracts every claim \u2014 regardless of how it’s phrased \u2014 and checks each one against your actual documentation.<\/p>\n\n\n\n
In a medical technology company where compatibility claims determine whether a system actually works in a hospital, and regulatory claims determine whether it can be sold at all, that distinction matters.<\/p>\n\n\n\n
See your own docs become a knowledge check.<\/strong> EOS turns your product documentation into practice and provable knowledge \u2014 claim extraction, fact-grounded verification, and auto-generated quizzes that reveal what reps actually know. Start free with up to 5 seats at akaeos.com<\/strong>, or [download the full whitepaper]<\/strong><\/a>.<\/p>\n\n\n\n<\/p>\n","protected":false},"excerpt":{"rendered":"
The head of sales enablement had a tool that could search every call transcript for any word she wanted. “Unlimited.” “48 months.” “Fully compatible.” She could find any mention of any term, instantly. The platform would highlight every instance, timestamp it, and let her review the context. But when she spot-checked actual practice calls for […]<\/p>\n","protected":false},"author":1,"featured_media":11272,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3642,3640],"tags":[],"class_list":["post-11271","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-en","category-all-en"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
Five calls. Five missed claims. Zero flags from the platform.<\/p>\n\n\n\n
The cost of missed claims<\/strong><\/h2>\n\n\n\nThe head of sales enablement shared her findings with the VP of Sales. He wasn’t surprised. He had been tracking post-sale issues for two quarters. In that time:<\/p>\n\n\n\n
\n- 12%<\/strong> of post-installation support tickets involved customers who received inaccurate product information during the sales process<\/li>\n\n\n\n
- 9%<\/strong> of delayed installations were caused by compatibility assumptions that turned out to be wrong<\/li>\n\n\n\n
- 7%<\/strong> of customers who churned within the first year cited “expectations set during sales didn’t match reality”<\/li>\n<\/ul>\n\n\n\n
The keyword search platform was generating reports. It wasn’t preventing errors.<\/p>\n\n\n\n
“We could search for every word we thought might be a problem. But we couldn’t search for what we didn’t know to look for. And that’s where the real mistakes were happening.”<\/p>\n\n\n\n
The intervention: replacing keyword search with claim extraction<\/strong><\/h2>\n\n\n\nThe company replaced their keyword-search platform with EOS (<\/strong>akaeos.com<\/strong><\/a>)<\/strong> \u2014 one that didn’t rely on predefined terms.<\/p>\n\n\n\nInstead of searching for specific words, EOS extracted every factual claim from each practice session and checked each one against the company’s actual product documentation:<\/p>\n\n\n\n
\n- It extracted claims, not keywords.<\/strong> The platform didn’t look for “fully compatible” or “24\/7.” It parsed natural speech into discrete, checkable propositions. When a rep said “basically compatible with all the major EMRs,” the platform extracted that as a claim about EMR compatibility. When a rep said “cleared in the US and most of Europe,” it extracted that as a claim about regulatory clearance.<\/li>\n\n\n\n
- It verified each claim against documentation.<\/strong> Every extracted claim was checked against the company’s current compatibility matrices, regulatory clearance lists, and service contract terms.<\/li>\n\n\n\n
- It returned specific verdicts with citations.<\/strong> Instead of highlighting keywords, it flagged contradicted claims with a clear citation:<\/li>\n<\/ol>\n\n\n\n
“Contradicted: Epic integration is not supported in the current version. See Compatibility Matrix v6.3, Section 2.1.”<\/p>\n\n\n\n
“Contradicted: Germany requires additional regulatory clearance. See Regulatory Status by Market, Appendix B.”<\/p>\n\n\n\n
The difference wasn’t subtle. One platform searched for words it already knew. The other identified claims it didn’t know to expect.<\/p>\n\n\n\n
The first 30 days, by the numbers<\/strong><\/h2>\n\n\n\nBecause this pilot is still early \u2014 35 reps, one month in \u2014 the results here aren’t a 90-day before\/after like a mature rollout. They’re a snapshot of what surfaced almost immediately once the company stopped searching for keywords and started extracting claims.<\/p>\n\n\n\n
The blank “before” cells aren’t a data gap \u2014 they’re the point. The keyword-search platform wasn’t measuring claim accuracy at all, so there’s no comparable prior baseline for most of these rows. That absence of a baseline is exactly what the audit in Call 1 through Call 5 exposed.<\/p>\n\n\n\n
What they found in the first month<\/strong><\/h2>\n\n\n\n\n- Reps were making claims they didn’t realize were wrong.<\/strong> One rep who had been with the company for five years discovered he had been stating a compatibility claim incorrectly for over a year \u2014 and no one had ever caught it because no one was checking.<\/li>\n\n\n\n
- The most common errors were in implied claims, not explicit ones.<\/strong> Reps rarely stated “this product is FDA cleared” incorrectly. But they frequently implied broader regulatory coverage than actually existed \u2014 “cleared in most major markets” when several key markets were still pending.<\/li>\n\n\n\n
- Managers could finally see what they couldn’t before.<\/strong> Using EOS’s manager dashboard, the sales enablement team could see which product areas generated the most contradicted claims \u2014 and could target training accordingly. Before, they were guessing. Now, they had data.<\/li>\n<\/ol>\n\n\n\n
“In the first month, we found errors that had been in the system for years. Not because reps were careless \u2014 because the old platform never checked. It wasn’t designed to. It was searching for words, not evaluating claims.”<\/p>\n\n\n\n
Is your training stack searching for keywords \u2014 or extracting claims?<\/strong> Our 12-page report, Beyond Roleplay: The Rise of the Sales Knowledge Engine<\/em>, includes the full evaluation framework, the 10-question stack audit, and the data-sovereignty questions to ask any vendor. [Download the whitepaper \u2192]<\/strong><\/a><\/p>\n\n\n\nWhy keyword search fails<\/strong><\/h2>\n\n\n\nKeyword search is useful for finding specific terms. But sales conversations aren’t written from a script. Reps paraphrase. They hedge. They use shorthand. They make implicit claims.<\/p>\n\n\n\n
A rep who says “basically compatible with all the major EMRs” is making a compatibility claim \u2014 but keyword search for “fully compatible” misses it entirely. A rep who says “cleared in the US and most of Europe” is making a regulatory claim \u2014 but keyword search for “FDA cleared” finds only part of it and can’t evaluate the rest.<\/p>\n\n\n\n
The underlying problem is structural. Keyword search doesn’t understand what was actually claimed. It only matches strings of characters. Claim extraction, by contrast, converts spontaneous speech into discrete, checkable propositions \u2014 statements specific enough that each one can independently be marked true or false against a source of truth. A transcript tells you the words. Claim extraction tells you what was actually asserted.<\/p>\n\n\n\n
The takeaway for sales leaders<\/strong><\/h2>\n\n\n\nIf your training platform relies on keyword search to catch factual errors, you have a blind spot you might not see until a post-installation issue lands on your desk.<\/p>\n\n\n\n
Keyword search only catches what it already knows to look for. It misses paraphrases, hedged language, compound claims, and implicit comparisons. A fact-grounded verification system, by contrast, doesn’t need to know what it’s looking for. It extracts every claim \u2014 regardless of how it’s phrased \u2014 and checks each one against your actual documentation.<\/p>\n\n\n\n
In a medical technology company where compatibility claims determine whether a system actually works in a hospital, and regulatory claims determine whether it can be sold at all, that distinction matters.<\/p>\n\n\n\n
See your own docs become a knowledge check.<\/strong> EOS turns your product documentation into practice and provable knowledge \u2014 claim extraction, fact-grounded verification, and auto-generated quizzes that reveal what reps actually know. Start free with up to 5 seats at akaeos.com<\/strong>, or [download the full whitepaper]<\/strong><\/a>.<\/p>\n\n\n\n<\/p>\n","protected":false},"excerpt":{"rendered":"
The head of sales enablement had a tool that could search every call transcript for any word she wanted. “Unlimited.” “48 months.” “Fully compatible.” She could find any mention of any term, instantly. The platform would highlight every instance, timestamp it, and let her review the context. But when she spot-checked actual practice calls for […]<\/p>\n","protected":false},"author":1,"featured_media":11272,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3642,3640],"tags":[],"class_list":["post-11271","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-en","category-all-en"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
The keyword search platform was generating reports. It wasn’t preventing errors.<\/p>\n\n\n\n
“We could search for every word we thought might be a problem. But we couldn’t search for what we didn’t know to look for. And that’s where the real mistakes were happening.”<\/p>\n\n\n\n
The intervention: replacing keyword search with claim extraction<\/strong><\/h2>\n\n\n\nThe company replaced their keyword-search platform with EOS (<\/strong>akaeos.com<\/strong><\/a>)<\/strong> \u2014 one that didn’t rely on predefined terms.<\/p>\n\n\n\nInstead of searching for specific words, EOS extracted every factual claim from each practice session and checked each one against the company’s actual product documentation:<\/p>\n\n\n\n
\n- It extracted claims, not keywords.<\/strong> The platform didn’t look for “fully compatible” or “24\/7.” It parsed natural speech into discrete, checkable propositions. When a rep said “basically compatible with all the major EMRs,” the platform extracted that as a claim about EMR compatibility. When a rep said “cleared in the US and most of Europe,” it extracted that as a claim about regulatory clearance.<\/li>\n\n\n\n
- It verified each claim against documentation.<\/strong> Every extracted claim was checked against the company’s current compatibility matrices, regulatory clearance lists, and service contract terms.<\/li>\n\n\n\n
- It returned specific verdicts with citations.<\/strong> Instead of highlighting keywords, it flagged contradicted claims with a clear citation:<\/li>\n<\/ol>\n\n\n\n
“Contradicted: Epic integration is not supported in the current version. See Compatibility Matrix v6.3, Section 2.1.”<\/p>\n\n\n\n
“Contradicted: Germany requires additional regulatory clearance. See Regulatory Status by Market, Appendix B.”<\/p>\n\n\n\n
The difference wasn’t subtle. One platform searched for words it already knew. The other identified claims it didn’t know to expect.<\/p>\n\n\n\n
The first 30 days, by the numbers<\/strong><\/h2>\n\n\n\nBecause this pilot is still early \u2014 35 reps, one month in \u2014 the results here aren’t a 90-day before\/after like a mature rollout. They’re a snapshot of what surfaced almost immediately once the company stopped searching for keywords and started extracting claims.<\/p>\n\n\n\n
The blank “before” cells aren’t a data gap \u2014 they’re the point. The keyword-search platform wasn’t measuring claim accuracy at all, so there’s no comparable prior baseline for most of these rows. That absence of a baseline is exactly what the audit in Call 1 through Call 5 exposed.<\/p>\n\n\n\n
What they found in the first month<\/strong><\/h2>\n\n\n\n\n- Reps were making claims they didn’t realize were wrong.<\/strong> One rep who had been with the company for five years discovered he had been stating a compatibility claim incorrectly for over a year \u2014 and no one had ever caught it because no one was checking.<\/li>\n\n\n\n
- The most common errors were in implied claims, not explicit ones.<\/strong> Reps rarely stated “this product is FDA cleared” incorrectly. But they frequently implied broader regulatory coverage than actually existed \u2014 “cleared in most major markets” when several key markets were still pending.<\/li>\n\n\n\n
- Managers could finally see what they couldn’t before.<\/strong> Using EOS’s manager dashboard, the sales enablement team could see which product areas generated the most contradicted claims \u2014 and could target training accordingly. Before, they were guessing. Now, they had data.<\/li>\n<\/ol>\n\n\n\n
“In the first month, we found errors that had been in the system for years. Not because reps were careless \u2014 because the old platform never checked. It wasn’t designed to. It was searching for words, not evaluating claims.”<\/p>\n\n\n\n
Is your training stack searching for keywords \u2014 or extracting claims?<\/strong> Our 12-page report, Beyond Roleplay: The Rise of the Sales Knowledge Engine<\/em>, includes the full evaluation framework, the 10-question stack audit, and the data-sovereignty questions to ask any vendor. [Download the whitepaper \u2192]<\/strong><\/a><\/p>\n\n\n\nWhy keyword search fails<\/strong><\/h2>\n\n\n\nKeyword search is useful for finding specific terms. But sales conversations aren’t written from a script. Reps paraphrase. They hedge. They use shorthand. They make implicit claims.<\/p>\n\n\n\n
A rep who says “basically compatible with all the major EMRs” is making a compatibility claim \u2014 but keyword search for “fully compatible” misses it entirely. A rep who says “cleared in the US and most of Europe” is making a regulatory claim \u2014 but keyword search for “FDA cleared” finds only part of it and can’t evaluate the rest.<\/p>\n\n\n\n
The underlying problem is structural. Keyword search doesn’t understand what was actually claimed. It only matches strings of characters. Claim extraction, by contrast, converts spontaneous speech into discrete, checkable propositions \u2014 statements specific enough that each one can independently be marked true or false against a source of truth. A transcript tells you the words. Claim extraction tells you what was actually asserted.<\/p>\n\n\n\n
The takeaway for sales leaders<\/strong><\/h2>\n\n\n\nIf your training platform relies on keyword search to catch factual errors, you have a blind spot you might not see until a post-installation issue lands on your desk.<\/p>\n\n\n\n
Keyword search only catches what it already knows to look for. It misses paraphrases, hedged language, compound claims, and implicit comparisons. A fact-grounded verification system, by contrast, doesn’t need to know what it’s looking for. It extracts every claim \u2014 regardless of how it’s phrased \u2014 and checks each one against your actual documentation.<\/p>\n\n\n\n
In a medical technology company where compatibility claims determine whether a system actually works in a hospital, and regulatory claims determine whether it can be sold at all, that distinction matters.<\/p>\n\n\n\n
See your own docs become a knowledge check.<\/strong> EOS turns your product documentation into practice and provable knowledge \u2014 claim extraction, fact-grounded verification, and auto-generated quizzes that reveal what reps actually know. Start free with up to 5 seats at akaeos.com<\/strong>, or [download the full whitepaper]<\/strong><\/a>.<\/p>\n\n\n\n<\/p>\n","protected":false},"excerpt":{"rendered":"
The head of sales enablement had a tool that could search every call transcript for any word she wanted. “Unlimited.” “48 months.” “Fully compatible.” She could find any mention of any term, instantly. The platform would highlight every instance, timestamp it, and let her review the context. But when she spot-checked actual practice calls for […]<\/p>\n","protected":false},"author":1,"featured_media":11272,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3642,3640],"tags":[],"class_list":["post-11271","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-en","category-all-en"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
Instead of searching for specific words, EOS extracted every factual claim from each practice session and checked each one against the company’s actual product documentation:<\/p>\n\n\n\n
- \n
- It extracted claims, not keywords.<\/strong> The platform didn’t look for “fully compatible” or “24\/7.” It parsed natural speech into discrete, checkable propositions. When a rep said “basically compatible with all the major EMRs,” the platform extracted that as a claim about EMR compatibility. When a rep said “cleared in the US and most of Europe,” it extracted that as a claim about regulatory clearance.<\/li>\n\n\n\n
- It verified each claim against documentation.<\/strong> Every extracted claim was checked against the company’s current compatibility matrices, regulatory clearance lists, and service contract terms.<\/li>\n\n\n\n
- It returned specific verdicts with citations.<\/strong> Instead of highlighting keywords, it flagged contradicted claims with a clear citation:<\/li>\n<\/ol>\n\n\n\n
“Contradicted: Epic integration is not supported in the current version. See Compatibility Matrix v6.3, Section 2.1.”<\/p>\n\n\n\n
“Contradicted: Germany requires additional regulatory clearance. See Regulatory Status by Market, Appendix B.”<\/p>\n\n\n\n
The difference wasn’t subtle. One platform searched for words it already knew. The other identified claims it didn’t know to expect.<\/p>\n\n\n\n
The first 30 days, by the numbers<\/strong><\/h2>\n\n\n\n
Because this pilot is still early \u2014 35 reps, one month in \u2014 the results here aren’t a 90-day before\/after like a mature rollout. They’re a snapshot of what surfaced almost immediately once the company stopped searching for keywords and started extracting claims.<\/p>\n\n\n\n
What they found in the first month<\/strong><\/h2>\n\n\n\n
- \n
- Reps were making claims they didn’t realize were wrong.<\/strong> One rep who had been with the company for five years discovered he had been stating a compatibility claim incorrectly for over a year \u2014 and no one had ever caught it because no one was checking.<\/li>\n\n\n\n
- The most common errors were in implied claims, not explicit ones.<\/strong> Reps rarely stated “this product is FDA cleared” incorrectly. But they frequently implied broader regulatory coverage than actually existed \u2014 “cleared in most major markets” when several key markets were still pending.<\/li>\n\n\n\n
- Managers could finally see what they couldn’t before.<\/strong> Using EOS’s manager dashboard, the sales enablement team could see which product areas generated the most contradicted claims \u2014 and could target training accordingly. Before, they were guessing. Now, they had data.<\/li>\n<\/ol>\n\n\n\n
“In the first month, we found errors that had been in the system for years. Not because reps were careless \u2014 because the old platform never checked. It wasn’t designed to. It was searching for words, not evaluating claims.”<\/p>\n\n\n\n
Is your training stack searching for keywords \u2014 or extracting claims?<\/strong> Our 12-page report, Beyond Roleplay: The Rise of the Sales Knowledge Engine<\/em>, includes the full evaluation framework, the 10-question stack audit, and the data-sovereignty questions to ask any vendor. [Download the whitepaper \u2192]<\/strong><\/a><\/p>\n\n\n\n
Why keyword search fails<\/strong><\/h2>\n\n\n\n
Keyword search is useful for finding specific terms. But sales conversations aren’t written from a script. Reps paraphrase. They hedge. They use shorthand. They make implicit claims.<\/p>\n\n\n\n
A rep who says “basically compatible with all the major EMRs” is making a compatibility claim \u2014 but keyword search for “fully compatible” misses it entirely. A rep who says “cleared in the US and most of Europe” is making a regulatory claim \u2014 but keyword search for “FDA cleared” finds only part of it and can’t evaluate the rest.<\/p>\n\n\n\n
The underlying problem is structural. Keyword search doesn’t understand what was actually claimed. It only matches strings of characters. Claim extraction, by contrast, converts spontaneous speech into discrete, checkable propositions \u2014 statements specific enough that each one can independently be marked true or false against a source of truth. A transcript tells you the words. Claim extraction tells you what was actually asserted.<\/p>\n\n\n\n
The takeaway for sales leaders<\/strong><\/h2>\n\n\n\n
If your training platform relies on keyword search to catch factual errors, you have a blind spot you might not see until a post-installation issue lands on your desk.<\/p>\n\n\n\n
Keyword search only catches what it already knows to look for. It misses paraphrases, hedged language, compound claims, and implicit comparisons. A fact-grounded verification system, by contrast, doesn’t need to know what it’s looking for. It extracts every claim \u2014 regardless of how it’s phrased \u2014 and checks each one against your actual documentation.<\/p>\n\n\n\n
In a medical technology company where compatibility claims determine whether a system actually works in a hospital, and regulatory claims determine whether it can be sold at all, that distinction matters.<\/p>\n\n\n\n
See your own docs become a knowledge check.<\/strong> EOS turns your product documentation into practice and provable knowledge \u2014 claim extraction, fact-grounded verification, and auto-generated quizzes that reveal what reps actually know. Start free with up to 5 seats at akaeos.com<\/strong>, or [download the full whitepaper]<\/strong><\/a>.<\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
The head of sales enablement had a tool that could search every call transcript for any word she wanted. “Unlimited.” “48 months.” “Fully compatible.” She could find any mention of any term, instantly. The platform would highlight every instance, timestamp it, and let her review the context. But when she spot-checked actual practice calls for […]<\/p>\n","protected":false},"author":1,"featured_media":11272,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3642,3640],"tags":[],"class_list":["post-11271","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-en","category-all-en"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t
- The most common errors were in implied claims, not explicit ones.<\/strong> Reps rarely stated “this product is FDA cleared” incorrectly. But they frequently implied broader regulatory coverage than actually existed \u2014 “cleared in most major markets” when several key markets were still pending.<\/li>\n\n\n\n
- It verified each claim against documentation.<\/strong> Every extracted claim was checked against the company’s current compatibility matrices, regulatory clearance lists, and service contract terms.<\/li>\n\n\n\n