00:05Welcome to HousingWire Demo Day on Demand. I'm Alison LaForgia, and HousingWire Demo Days
00:11spotlights some of the most innovative technology companies in mortgage, giving the HousingWire
00:16audience a front row seat to real product demos from teams building the technology moving our
00:21industry forward. In this session, we're featuring BlueSage AI. BlueSage AI embeds intelligent
00:28document analysis, workflow automation, underwriting support, predictive analytics,
00:34and conversational AI directly into BlueSage's digital lending and digital servicing platforms
00:40to help lenders improve efficiency, reduce manual work, and maintain traceability across AI-assisted
00:48workflows. In this BlueSage demo, Joey will be taking us through BlueSage AI. Joey, the floor is
00:54yours. Thanks, Alison. Hi, everybody. I'm Joey McDuffie of BlueSage Solutions, and I wanted
01:02to introduce a little bit about our product and some of the new initiatives we have going
01:06on, exciting new initiatives we have going on at BlueSage. For those that may not be familiar
01:12with BlueSage, we've been around since 2011, and our mantra was to build the most modern,
01:19dynamic platform in the industry. We've actually progressed a long way since 2011.
01:27And really, one of the things that makes us different is that we have what we call our
01:33digital lending platform, which is really the only multi-channel platform that has a combined
01:38POS, LOS, and even servicing now that's been written from the ground up in the last 10 years.
01:46Fully native. It's not a combination. It's not a Frankensteinian monster. We've written all of
01:51this from scratch, and we're excited to continue to add more and more customers to the platform
01:58that are realizing a lot of ROI. So from an AI perspective, since that's what we're focused on
02:07today, everyone in our industry seems like is focused on AI. BlueSage has actually been doing
02:15AI since 2022. We partnered with one of our current clients. We built some AI decisioning using ML
02:25models back in the day for HELOCs to do auto decisioning. And then since then, we've continued
02:31to add more and more features and functionality. In 2025, we added SageVision, which is what we call
02:39intelligent document analysis, which we'll go over in just a second. And then in 2026, we've added
02:45a few additional functionality called AI Studio and some AI agents to further streamline the process
02:53for customers. So let's jump right into see how SageVision works. As I mentioned before, we do
03:03actually have a POS. So in this particular case here, as the borrower, after I've submitted the
03:10application, you can see that this loan is in processing. And there is a number of conditions
03:16or a number of conditions that have been requested from this particular borrower. Instead of actually
03:23going in and associating a specific document to a condition, what we can do is we can basically just
03:29request a number of documents from the borrower. So in this case, I'm just going to call this all docs.
03:37And then we can add all of the files that are necessary from the borrower. So in this particular case,
03:44you can see that the borrower is uploading 1040s and pay stubs and driver's license and
03:49sales contracts and bank statements and you name it, right? All of those things can be uploaded in
03:56one fell swoop. So once we, once the borrower actually uploads those, then we actually put these
04:03through SageVision. So SageVision in a matter of minutes, what it'll do is it'll actually go in,
04:12analyze each one of these documents, both for what the document type is, as well as start doing
04:21some extractions that can be used further in the process, right? So not only do we just go in and
04:28say, is this a 1040? We can go in and actually check to see if this is Alice's 1040. Is
04:35it the right
04:35one from last year, the right year, the right employer, you name it? And that's where all of
04:42the AI capabilities come in. So in a matter of minutes, once these documents are processed,
04:50then we can actually go over and see what happened in the LOS. So I'm going to flip from the
04:54POS over to
04:55the LOS and go and see what happened behind the scenes. So in this particular case, I'm logging in
05:03as PADI processor. So I'm going to log in and find that particular loan. In this case, here's my loan
05:12here, 5543. All of those documents, I think there were 14 documents that were uploaded, all actually
05:19funneled into what we call our document manager. So in this case, you can see that while Alice,
05:28that simulated bar, were uploaded all of these, these were all indexed appropriately based on what
05:36was being uploaded at that particular point in time. And not only were all of these classified,
05:42you can see that we also provide an extraction panel that has various data insights that are
05:49gleaned from that each individual document, each individual page of the document.
05:54One of the key aspects of that is that we have this confidence factor, right? So as the
06:00intelligent document analysis engine called SageVision is processing each one of these,
06:06we can actually go in and see not only where did that information come from on that particular
06:12document, but is it enough to actually feed something else to automate the process even further?
06:19So we can do this with any number of documents, whether it's a bank statement, or like I said,
06:23a driver's license. And that information can then be used by agents and other aspects of the loan
06:31process. So in this particular case, for example, if we were to go look at the driver's license,
06:37we pulled off the expiration date, the date of issue, the name, et cetera.
06:42So before we go in and look at a couple other things, you can see that once the borrower uploads
06:48this information, we can pre-populate the loan application as well. So instead of in the old days
06:54with the processor having to go in and look at the image and then come over to the screen and
06:59say,
06:59okay, this is Alice's driver's license. It's not expired. Once it passed all of those things,
07:04as long as it was a valid driver's license, we actually take the engine and pre-populate this
07:09information. Same thing with the sales contract, right? So in the sales contract, we can actually
07:15pull off the realtor or any other information. So really any part of the loan application can be
07:20pre-populated and streamlined using this approach with SageVision. Now, in addition to SageVision,
07:27one other thing that we've added in the last few months is something called AI Studio.
07:32So AI Studio is basically another tool that is used for pre-underwrite. As I like to say,
07:41it's job enhancement, not job replacement. It's not an auto approval engine. It's really a more of a
07:47pre-underwriting feature designed to apply agency guidelines, portfolio guidelines more consistently
07:53without taking the judgment away from you, the underwriter. So I always like to say that it takes
08:01the scavenger hunt out of the equation, right? So there's two components to AI Studio. One of them
08:06allows us to go in and use loan data to create scenarios. We run that through the AI guidelines
08:13provided, and it will return based on the guidelines for that particular product, whether or not that
08:20loan is qualified or not qualified. This gives the ability for the underwriter to go in and really
08:27sandbox this particular loan to not affect the actual loan data, but to go in and say, well,
08:32what if we pay off this particular item? And you can see that the AI engine actually came back with
08:39a
08:39few recommendations for approval, and that's all going through AI. The other piece of the puzzle with
08:45AI Studio is that we have a number of conditions that were auto assigned to the loan. The AI engine
08:54actually goes through and figures out what is needed to satisfy those conditions, what documents
09:00are needed, and then once we actually do the analysis with a combination of SAGE vision and
09:06agents, you can see that it'll come back and say, well, our proposed status is to auto clear that
09:12particular condition. It auto associates the images with that particular condition and then gives us a
09:18finding, right? So in this particular case, you can see that all of the data points matched for the
09:24W-2, including the name, the social security number, et cetera. The underwriter can go in and review that
09:29there. But in the case of the bank statement, you can see that they, the AI model actually revealed
09:37multiple undisclosed recurring liabilities, including a payment. The direct deposits did not match the
09:43employer that we have on the loan. So again, that's a pinned. So the idea is that we can create
09:49any number of
09:50agents to analyze documents as well as data that we find from verification providers and other providers
09:56to go in and streamline the loan process. So the AI studio allows us to go in and see how
10:04each one of
10:04these agents were evaluated. So basically, almost like your sixth grade math teacher wanting you to
10:11show your work, we have that analysis based on some of the agency recommendations to go in and make sure
10:18you
10:18actually arrive at that conclusion. And then finally, the last piece of the puzzle, not only do we have
10:25all of this in our AI studio, but we've also moved this into loan conditions to make it very simple
10:32for
10:32the underwriter as they're going through and evaluating conditions. You can see that we update the status
10:39automatically if there's an issue with the content that they provided, as well as those findings that we
10:46had from the AI studio. So that makes it very easy for the underwriter to go in and understand exactly
10:55what was evaluated, what the findings were. And again, we can go through and do auto clearing of
11:03conditions and auto escalation of conditions. So in that particular case, we've gone through and
11:11looked at a little bit of Sage vision, we looked at AI studio. And the bigger piece of the puzzle
11:18with
11:18Blue Sage is that what we're finding with our all of our clients is that we have native AI built
11:26in. So
11:27we're continuing to provide additional innovation and value to our customers, they don't have to
11:33actually go out and get another vendor. And the biggest thing is that since we're the system of record,
11:38both on the POS, the LOS and the servicing side, these agents can operate on the data as this data
11:46is
11:46continuously being updated via more information from the borrower, third party services. And again,
11:54there's no data loss. Lastly, there's fast data ramp up to add agents. And, you know, don't wait,
12:02scan the barcode for more information.
12:03Joey, thank you so much for taking us through Blue Sage AI. I have a couple of questions for you.
12:12Can you explain how Sage Vision handles a real document scenario end to end, including confidence
12:19scoring, source references, and what happens when a human review is needed?
12:24So really, you know, what I always like to say is Sage Vision is your grandfather's
12:30automated document recognition and data extraction engine. It's amazing how much technology has come on
12:39in the last few years. So what Sage Vision gives us the ability to do is really analyze a particular
12:46document, extract data, virtually 100% of the data. And then, but where it may have some questions,
12:57we can actually apply that confidence score. And that confidence score, if it's all above a certain
13:03threshold that the lender can, that the lender can define, then we can say, we're going to do something
13:08with that. We're going to clear conditions. We're going to automate the process, progress of the loan
13:15to the next phase. We're going to actually go clear to close. If for some reason, the confidence score
13:20is such that it's below a certain threshold, then we can actually do auto escalation and auto tasking
13:26to a specific party on the loan so that there's further follow-up on that particular document.
13:33You had mentioned that Blue Sage AI is embedded directly in the platform. How does data flow
13:40through the workflow and how do auditability, traceability, and customer data protection work?
13:47I think that's really one of the benefits of having native AI within our platform versus actually adding
13:54a bolt-on or a plug-in from some other service. All of the data resides in each lender's environment.
14:02So nothing's actually being, you know, nothing's leaving their protected environment with all of
14:09the security aspects that every lender expects. From an auditability perspective, with all of our AI
14:17and agents, we basically show the work, right? So each one of these agents, we have the ability to do
14:26configuration. So all of the lenders' guidelines, et cetera, can be configured to reflect their policies.
14:37And then as these agents are processing that data, we have full traceability and auditability
14:44so that, you know, based on Freddie and other agency recommendations that you have a record of exactly
14:52how you arrived at that decision or that review without actually basically just using a calculator
15:00and saying two plus two is four, right? We know how we got there and what happened behind the scenes,
15:06what documents were used, what data was used to arrive at that decision, whether it's a pass
15:11or it's a fail or it's a review. And Joey, how do lenders get started with Blue Sage AI and
15:18what capabilities are actually available today versus ones that are planned roadmap enhancements?
15:25So that's a good question as well. So, you know, again, for any of our existing clients,
15:29it's very simple to turn on these agents. You know, some of these can be stood up in a matter
15:35of
15:35days. We've actually created these agents so that they are LOS agnostic. So we have about,
15:42I think we have about eight or 10 agents today. We're continuing to add others as we speak.
15:49And again, like, again, we can use data from documents. We can use data from third-party
15:54services like verification, BOI, BOE providers. And we continue to add agents through our portal,
16:02our agentic portal, you know, every week to further streamline the mortgage process and the,
16:10you know, the verification process to get, you know, to get your cost per loan down and to get
16:16clear to close faster. Joey, thank you so much for joining me today. To our audience,
16:22for more information about Blue Sage AI, click the link below.
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