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Subject Received
⚙️ How Sphinx built an enterprise AI accuracy layer Sat, 20 Jun 2026 12:36:42 GMT
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From: "The Deep View" <newsletter@...
To: [email protected]
Date: Sat, 20 Jun 2026 12:36:42 GMT
Subject: ⚙️ How Sphinx built an enterprise AI accuracy layer
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</style></head><body class="a" style="margin:0px auto;padding:0px;word-wrap:normal;word-spacing:normal;background-color:#FFFFFF;"><div role="article" aria-roledescription="email" aria-label="email_name" lang="en" style="font-size:1rem"><table role="none" width="100%" border="0" cellspacing="0" align="center" cellpadding="0" class="gg"><tr><td align="center" valign="top"><table role="none" width="670" border="0" cellspacing="0" cellpadding="0" class="aa" style="width:670px;table-layout:fixed;"><tr><td class="bodyWrapper" align="center" valign="top" style="padding:10px 10px 10px 10px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td align="center" valign="top" style="border-width:1px 1px 0px 1px;border-style: solid; border-color: #FFFFFF;border-radius:10px 10px 0px 0px;background-color:#FFFFFF;" class="c"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr id="header"><td style="padding:15px 15px 0px 15px;"><div style="padding-top:0px;padding-right:0px;padding-bottom:20px;padding-left:0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td class="f" align="right" valign="top"><p> June 20, 2026 &nbsp; | &nbsp; <a data-read-online-tooltip="true" href="https://elink983.thedeepview.co/ss/c/u001.gKxW2KpP8aPe_QMyOQduokBEDIe6892b9r6MySOyoYVTdyAx7EtbgRomEyGiqIeEl8DYhlFjr60yjpS0848OrUWuGxEp5gv89BSxPc4tURcXPwk4_gGbs9azl8RizcbHah1O_Z6aCimG16ZgAmKgPaIp_PPxD3DXIlgYpffgAQkvUjSi_m02fdYOpPADyPZ4q2v8rsHrXGFdUb6k3gjuASjxe2XSYJgtM3mVFpwnj_PLQ4vlfoKHXY0Rr29UQY5wm2NAWp2yrRCLYW9uQI0daX4yfXoQAXXEHGmhzy1GPhp4LXa25wWjTEG0V4rWkW_gIqXAS5Qc0lYCEZlYBGHFuFdeqb_dLY6knx6F6gUTwhqBSXi9bhK24X51BegyWJlhs6QcrMUi_AJX56bem5eFbR0nlpYbXc2w2fI8oBsBjE1qGuvQ4wOo7v_Pa3Ezhdvfgf59i1us-fKxPTykfXEu_rUYiW4v8SV8nm8Dq47GgmJc5i-tpwrDZ6qThjfUqlkhlR6Bv5ON4IPzUqweS2Ah9hGr7NgE9zkHfRmCcISmiXi-ZN_VfzA4-SU3d0IEEqMrk4d8nXsFYrDKSrtdFIDbeoqUD-TYCshKM50x8gRF5vFWsmKL1e7mne2BYX5fW8x6mOuM7pZSETC8lLA4-NdNyap-UKbxX9mcL5HOytAuYBkQp4O9cFYL5_GtMwFSYz4RZYnnzvE5-jbyJkFUwcLs4qsYQ4rPx4AJxA9bTFbnD_FdCxyOyMAW7WZjv3C2bOQs5sUxCPt_1x8z1ThXkkWSzn8jwd05QomeN20bVWn1uns5ok77pqWRaDvT2N9ipYD9/4rn/jPjMdvrGR-udV4YvAFjOKw/h0/h001.OOdSYwIicpzFWDAcnfBKc-jPQenD3rbfRZAZuNakccg">Read online</a></p></td></tr><tr><td style="line-height:0;"><div data-open-tracking="true"> <img src="https://elink983.thedeepview.co/ss/o/u001.8UYYf-fpC5t510J3InhYHg/4rn/jPjMdvrGR-udV4YvAFjOKw/ho.gif" alt="" width="1" height="1" border="0" style="height:1px !important;width:1px !important;border-width:0 !important;margin-top:0 !important;margin-bottom:0 !important;margin-right:0 !important;margin-left:0 !important;padding-top:0 !important;padding-bottom:0 !important;padding-right:0 !important;padding-left:0 !important;"/> </div></td></tr></table></div></td></tr><tr id="content-blocks"><td class="email-card-body" align="center" valign="top" style="padding-bottom:15px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" align="center"><tr><td align="center" valign="top" style="padding-bottom:8px;padding-left:15px;padding-right:15px;padding-top:8px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:604px;"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/245f3197-736b-4371-87d0-d0d9db53e3b2/BANNER__140_.jpg?t=1781871438" alt="" height="auto" width="604" style="display:block;width:100%;" border="0"/></td></tr></table></td></tr><tr><td align="center" valign="top" style="padding-bottom:8px;padding-left:15px;padding-right:15px;padding-top:8px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:325px;"><a href="https://elink983.thedeepview.co/ss/c/u001.MDn7gO0R2GYySjUIwjh9Jja6wi67vhSVw5_Uk0ZxvYU0JzB5SlQyPHCzvjYJMXQse_swxUE_z9PLd9C3mJzqDgqrndFHDZ-q23tmvSkc3xLPkBN8Qj0qi4qAQr-bekSUJCJTVzMOAh0X1zntZONDgI7TYJ6z21ad52nMoNBodlpYC1gzsgDcgjuSoMG-JUWZ/4rn/jPjMdvrGR-udV4YvAFjOKw/h1/h001.DTRk8v0bz3u7aG8abToNMbdZC2rm6J7KkDE8NBniwGQ" rel="noopener noreferrer nofollow" style="text-decoration:none;" target="_blank"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/cbe9100d-0de0-4603-94db-4623fb8ede5b/Together_W_Sphinx.png?t=1781871342" alt="" height="auto" width="325" style="display:block;width:100%;" border="0"/></a></td></tr></table></td></tr><tr><td class="dd" align="left" style="padding:0px 15px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><b>Hello, friends.</b> One of the biggest barriers to enterprise AI isn&#39;t the models. It&#39;s trust. This week, we look at Sphinx.ai, a startup tackling AI accuracy by creating an institutional knowledge layer that helps models understand how a business actually works. The goal: make AI fit enterprise data, not force enterprise data to fit AI. Thanks for tuning into this special weekend edition, presented in partnership with <a class="link" href="https://elink983.thedeepview.co/ss/c/u001.MDn7gO0R2GYySjUIwjh9Jja6wi67vhSVw5_Uk0ZxvYU0JzB5SlQyPHCzvjYJMXQse_swxUE_z9PLd9C3mJzqDgqrndFHDZ-q23tmvSkc3xLPkBN8Qj0qi4qAQr-bekSUXgXz5jZR7IYA0T56eP1aWRQmbyxi7fQram2rZ4e1lt1Qng0PYqwyeKwnnGto0Bur/4rn/jPjMdvrGR-udV4YvAFjOKw/h2/h001.INBsLx7rY3nchJe1q3lRaQflcZMmwaJTGK81JOI7MCw" target="_blank" rel="noopener noreferrer nofollow"><span>Sphinx</span></a>. Let us know what you think! <i>—</i><a class="link" href="https://elink983.thedeepview.co/ss/c/u001.wZPohD0JH12EksCsbt8ZeFCsxgaiiSFhhEZXgbyLVQFhr1A_BEAEp8cdvWudcCyss9zA-ck9db_DlffwGgC6GkvgY4ddaq81wTLdw5-Rvc-LKogiay9cxE_pXnVW6unCRFHIoAQXJl7SUNdLQ3r6TgxtgE-47Ln-wcjJ_VCPgHcDdzIY-kUxV3l4y0uYmoORRAAjexYeO_MLioqiqkKsOT_Qb2P2l6iHf6pgK4iS9kmAitWvz4YQfU2udQm43U3WyIaNvvEJS3VZNhsPoqHANj7A8li6wyg2kY9i3kgZ2IM/4rn/jPjMdvrGR-udV4YvAFjOKw/h3/h001.IW01R4D39SrEHLnDI3fmA9DpR4gPIXtlKmprSp2dG2g" target="_blank" rel="noopener noreferrer nofollow"><span><i>Jason Hiner</i></span></a></p></td></tr><tr><td><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0" style=""><tr><td height="5.0px" style="line-height:1px;font-size:1px;height:5.0px;"> &nbsp; </td></tr><tr><td bgcolor="transparent" style="background-color:transparent;border-color:#CDCDCD;border-radius:10px;border-style:solid;border-width:1px;padding:10.0px 10.0px 10.0px 10.0px;"><table role="none" width="100%" border="0" cellspacing="0" cellpadding="0"><tr><td class="dd" align="left" valign="top" style="color:#2A2A2A;font-weight:400;padding:0px 15px;text-align:left;"><h1 style="color:#2A2A2A;font-weight:400;mso-line-height-alt:125.0%;">How Sphinx built an enterprise AI accuracy layer</h1></td></tr><tr><td align="center" valign="top" style="padding-bottom:8px;padding-left:15px;padding-right:15px;padding-top:8px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:584px;"><a href="https://elink983.thedeepview.co/ss/c/u001.MDn7gO0R2GYySjUIwjh9Jja6wi67vhSVw5_Uk0ZxvYU0JzB5SlQyPHCzvjYJMXQse_swxUE_z9PLd9C3mJzqDgqrndFHDZ-q23tmvSkc3xLPkBN8Qj0qi4qAQr-bekSUIG6QXAHI5CPjTRrTcS9rQP5OwzHOmAFMHY0dOjUJIihsgfeA5JU4Jw2hPUei83vN/4rn/jPjMdvrGR-udV4YvAFjOKw/h4/h001.XRlOHcbQmdUC_WlgG-zZQjXoMGpqck7hbyqANiupA00" rel="noopener noreferrer nofollow" style="text-decoration:none;" target="_blank"><img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/6b3892da-e350-428b-8db8-3579191fba0d/ART__7_.jpg?t=1781871404" alt="" height="auto" width="584" style="display:block;width:100%;" border="0"/></a></td></tr></table></td></tr><tr><td class="dd" align="left" style="padding:0px 15px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Accuracy is one of the biggest problems plaguing enterprises as they seek to embed AI within their operations. The problem isn&#39;t only on the systems themselves, but rather the data they work from.  </p></td></tr><tr><td class="dd" align="left" style="padding:0px 15px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> It&#39;s the exact problem that <a class="link" href="https://elink983.thedeepview.co/ss/c/u001.MDn7gO0R2GYySjUIwjh9Jja6wi67vhSVw5_Uk0ZxvYU0JzB5SlQyPHCzvjYJMXQse_swxUE_z9PLd9C3mJzqDgqrndFHDZ-q23tmvSkc3xLPkBN8Qj0qi4qAQr-bekSUbUCnRdWMfRap9DY92QuUNZsha0ZFAj0Vcgo83MK5wAOyKjXT2q6K1EUUL8WrQqIO/4rn/jPjMdvrGR-udV4YvAFjOKw/h5/h001.-iX-ebI60Ftx7g0kVjPtGw17EF1tNQ-W0Hyh4zAU3ag" target="_blank" rel="noopener noreferrer nofollow"><span>Sphinx.ai</span></a> wants to solve. Sphinx, a seed-stage startup backed by Lightspeed and Bessemer Venture Partners, enforces data accuracy across any AI tool an enterprise uses, giving companies the freedom to leverage whatever model they want without worrying about answers that drift from reality.  </p></td></tr><tr><td class="dd" align="left" style="padding:0px 15px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> &quot;While AI is quite good at reasoning over things that are in the public domain that it&#39;s been trained on, every company has its own idiosyncratic way of laying out information,&quot; Rohan Kodialam, CEO and co-founder of Sphinx, told The Deep View. &quot;It&#39;s not a question of intelligence, it&#39;s really a question of not knowing that tribal knowledge of how your organization works that limits AI&#39;s effectiveness.&quot; </p></td></tr><tr><td class="dd" align="left" style="padding:0px 15px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Last month, the company unveiled Sphinx 2.0, the latest product in its lineup aimed at improving AI accuracy. </p></td></tr><tr><td style="padding-bottom:12px;padding-left:37px;padding-right:27px;padding-top:12px;" class="ee"><div style="margin-left:0px;" class="edm_outlooklist"><ul style="font-weight:400;list-style-type:disc;margin-bottom:12px !important;margin-top:12px !important;padding:0px 0px 0px 0px;"><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"> This system essentially creates an institutional knowledge layer through which every AI query is filtered for accuracy. It sits between your AI tools and your data, continuously monitoring and refining outputs to better reflect your business context.  </p></li><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"> It&#39;s a stark change from Sphinx&#39;s first product, which used an agent to adjust and fix organizational data to prepare it for AI. Now, rather than making your data AI-ready, said Kodialam, Sphinx gets &quot;your AI ready for your data.&quot;  </p></li><li class="listItem ultext"><p style="mso-line-height-alt:150.0%;padding:0px;text-align:left;word-break:break-word;"> The system also detects discrepancies and automatically surfaces them for human review by learning how an enterprise measures and tracks specific data. &quot;We will find those instances where what we&#39;re seeing is in disagreement with what we already believe to be true,&quot; Kodialam said.  </p></li></ul></div></td></tr><tr><td class="dd" align="left" style="padding:0px 15px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><a class="link" href="https://elink983.thedeepview.co/ss/c/u001.MDn7gO0R2GYySjUIwjh9Jja6wi67vhSVw5_Uk0ZxvYU0JzB5SlQyPHCzvjYJMXQse_swxUE_z9PLd9C3mJzqDgqrndFHDZ-q23tmvSkc3xLPkBN8Qj0qi4qAQr-bekSUsC7DsnHIklalIXYokc7-9lFF1X-szQLgBAUwDzbJJ9lzCyND0qHMy0H_cPn9R4jK/4rn/jPjMdvrGR-udV4YvAFjOKw/h6/h001.5E-GhyFQqRgZEc2UObrFIKP8P04woSSiK1XiIX1l3vc" target="_blank" rel="noopener noreferrer nofollow"><span>Sphinx 2.0</span></a> solves a major pain point that enterprises are grappling with amid AI adoption: the inability to trust or rely on the outputs of these models. With too little data and organizational context, these models tend to hallucinate when queried about business-specific topics, making things up due to gaps in their training data. However, enterprise data is a moving target, with data stockpiles growing at a constant rate, while continually migrating it safely into AI systems is costly.  </p></td></tr><tr><td class="dd" align="left" style="padding:0px 15px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> Additionally, if enterprises simply connect all their data to AI systems in real time, they lose the observability and governance layers that make it safe.  </p></td></tr><tr><td class="dd" align="left" style="padding:0px 15px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"> &quot;We think there&#39;s like a happy medium of not spending a huge amount of money getting your data ready, but preparing your AI to operate in your real ecosystem,&quot; Kodialam said. </p></td></tr><tr><td class="dd" align="left" style="padding:0px 15px;text-align:left;word-break:break-word;"><p style="mso-line-height-alt:150.0%;"><a class="link" href="https://elink983.thedeepview.co/ss/c/u001.MDn7gO0R2GYySjUIwjh9Jja6wi67vhSVw5_Uk0ZxvYU0JzB5SlQyPHCzvjYJMXQse_swxUE_z9PLd9C3mJzqDgqrndFHDZ-q23tmvSkc3xLPkBN8Qj0qi4qAQr-bekSUkQdxGxcYodYnLaQF7BK39IL2-popmQ_08bPTYEenPlw5MeXza5OS6QCUoz2N2Fe9/4rn/jPjMdvrGR-udV4YvAFjOKw/h7/h001.8HJeqsQVoDip-D34q58DMCaXamx2mMzSG6vJYe5jTUs" target="_blank" rel="noopener noreferrer nofollow"><span>See how it works</span></a></p></td></tr></table></td></tr><tr><td height="5.0px" style="line-height:1px;font-size:1px;height:5.0px;"> &nbsp; </td></tr></table></td></tr><tr><td align="center" valign="top" style="padding-bottom:8px;padding-left:15px;padding-right:15px;padding-top:8px; " class="dd"><table role="none" border="0" cellspacing="0" cellpadding="0" style="margin:0 auto 0 auto;"><tr><td align="center" valign="top" style="width:604px;"><img 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**Hello, friends.** One of the biggest barriers to enterprise AI isn't the models. It's trust. This week, we look at Sphinx.ai, a startup tackling AI accuracy by creating an institutional knowledge layer that helps models understand how a business actually works. The goal: make AI fit enterprise data, not force enterprise data to fit AI. Thanks for tuning into this special weekend edition, presented in partnership with [Sphinx](https://sphinx.ai/?utm_source=The+Deep+View&utm_medium=email&utm_campaign=deep_view). Let us know what you think! _—_[_Jason Hiner_](https://www.thedeepview.com/author/jason-hiner)

----------
# How Sphinx built an enterprise AI accuracy layer

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Accuracy is one of the biggest problems plaguing enterprises as they seek to embed AI within their operations. The problem isn't only on the systems themselves, but rather the data they work from. 

It's the exact problem that [Sphinx.ai](https://sphinx.ai/?utm_source=The+Deep+View&utm_medium=email&utm_campaign=deep_view) wants to solve. Sphinx, a seed-stage startup backed by Lightspeed and Bessemer Venture Partners, enforces data accuracy across any AI tool an enterprise uses, giving companies the freedom to leverage whatever model they want without worrying about answers that drift from reality. 

"While AI is quite good at reasoning over things that are in the public domain that it's been trained on, every company has its own idiosyncratic way of laying out information," Rohan Kodialam, CEO and co-founder of Sphinx, told The Deep View. "It's not a question of intelligence, it's really a question of not knowing that tribal knowledge of how your organization works that limits AI's effectiveness."

Last month, the company unveiled Sphinx 2.0, the latest product in its lineup aimed at improving AI accuracy.

* This system essentially creates an institutional knowledge layer through which every AI query is filtered for accuracy. It sits between your AI tools and your data, continuously monitoring and refining outputs to better reflect your business context. 

* It's a stark change from Sphinx's first product, which used an agent to adjust and fix organizational data to prepare it for AI. Now, rather than making your data AI-ready, said Kodialam, Sphinx gets "your AI ready for your data." 

* The system also detects discrepancies and automatically surfaces them for human review by learning how an enterprise measures and tracks specific data. "We will find those instances where what we're seeing is in disagreement with what we already believe to be true," Kodialam said. 

[Sphinx 2.0](https://sphinx.ai/?utm_source=The+Deep+View&utm_medium=email&utm_campaign=deep_view) solves a major pain point that enterprises are grappling with amid AI adoption: the inability to trust or rely on the outputs of these models. With too little data and organizational context, these models tend to hallucinate when queried about business-specific topics, making things up due to gaps in their training data. However, enterprise data is a moving target, with data stockpiles growing at a constant rate, while continually migrating it safely into AI systems is costly. 

Additionally, if enterprises simply connect all their data to AI systems in real time, they lose the observability and governance layers that make it safe. 

"We think there's like a happy medium of not spending a huge amount of money getting your data ready, but preparing your AI to operate in your real ecosystem," Kodialam said.

[See how it works](https://sphinx.ai/?utm_source=The+Deep+View&utm_medium=email&utm_campaign=deep_view)


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If you want to get in front of an audience of 750,000+ developers, business leaders and tech enthusiasts, get in touch with us here.

GET IN TOUCH WITH US HERE (https://www.passionfroot.me/the-deep-view)


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