Quote Matrix

Vaults and deal rooms are the same thing - think of them like a secure Dropbox for sharing files with lenders. Every deal automatically creates a general "deal room vault" that contains high-level documents (like your memo and pro forma) to help get lenders from initial interest to a term sheet. You can also create private vaults for specific lenders or groups of lenders when you need to share sensitive documents or information that you don't want all lenders to see. For example, if a lender provides a term sheet and requests specific financials, you can create a dedicated vault just for them rather than adding those documents to your main deal room.

The AI automatically extracts loan terms from structured emails and uploaded documents like term sheets. It works best when lenders provide sufficient detail in bullet points or structured format (interest rate, LTV, loan amount, amortization, etc.). If you receive terms via email, you can either upload the document directly or copy and paste the email text into the AI extraction tool. The system will then automatically populate the terms fields and add them to your quote matrix for comparison. Note that very basic information like "5 year at X amount" may not provide enough data for accurate extraction.

Yes, the AI can extract information from uploaded documents including Excel files, rent rolls, and other financial data. The AI only uses the documents you upload - it won't make up information or perform calculations. For best results, upload clean, relevant documents and avoid dumping too many conflicting files, as this can confuse the AI about what information is most important. The platform has enhanced capabilities for processing complex financial documents and extracting key data points for your deal memo.

Lev adds approximately 100 new lenders per month, with the database growing from about 7,000 to over 7,400 lenders recently. Each lender is thoroughly vetted to ensure they're legitimate and actively lending. The capital markets team verifies contact information and program details before adding lenders to the platform. You can also request specific lenders to be added by contacting your Lev representative, or add private lenders to your own deals for a competitive advantage. Lev is also developing an AI system called 'Lead Cortex' that will automatically identify and add new lenders by scanning their websites and public data.

Lev uses intelligent contact routing based on your deal parameters, geographic location, property type, and historical data. The system analyzes transaction history, responsiveness rates, and territory coverage to surface the best contact for your specific deal. For example, a lender might have different contacts for different regions or property types - the system will automatically route to the contact who covers your area and asset class. If you have your own contact at a lender, you'll see a dropdown option to choose between your contact and Lev's recommended contact. You can also separate your outreach between existing relationships and new contacts to customize your messaging appropriately.

Lev uses an AI-powered matching algorithm that applies five key filters to narrow down from over 7,000 lenders to those best suited for your deal. The primary filters are: loan type (bridge, permanent, construction, or land), property location/market, property type (office, multifamily, hospitality, etc.), loan amount, and sponsor location. The system pulls data from multiple sources including county records of closed transactions, publicly available deal information, uploaded term sheets, and ongoing feedback about what each lender is actively doing. This data is updated constantly to ensure you receive the most current and accurate lender recommendations, so you won't be matched with lenders who don't fit your deal parameters.

Yes, Lev includes pipeline management for both acquisition and financing stages. You can create custom pipelines with your own statuses and terminology, or use the built-in acquisition pipeline that tracks deals from initial radar through underwriting, LOI signing, and due diligence. The financing pipeline covers the process from preparing materials through lender outreach, negotiating, and closing. You can easily move deals from the acquisition pipeline to the financing pipeline without recreating the deal, keeping all your resources and deal information in one place throughout the entire process.

An MSR (Marketing Status Report) is an AI-generated PDF summary that compiles all activity and progress on your deal into a professional 4-8 page document. The report includes an executive summary, deal description, conversation histories with lenders, response statuses (reviewing, passed, terms received), and a quote matrix comparing any terms you've received. This report is particularly useful for sharing with borrowers to demonstrate your marketing efforts and show deal traction. Many users leverage MSR reports to prove they've done thorough outreach when borrowers request extensive lender coverage.

Lev's AI automatically reads and processes lender communications within email threads to track deal progress and extract important information. When lenders send term sheets via email (whether in the body or as attachments), the AI extracts the terms and populates them into a quote matrix for easy comparison. If you receive terms outside of Lev that don't get automatically captured, you can manually upload term sheets and the AI will extract the relevant information. The system can also track external lender conversations - you can forward emails from lenders to Lev and they'll be automatically added to your placement table.

When you upload documents to Lev, the AI serves two functions: it creates an automated 8-10 page offering memorandum (deal book) with executive summary, marketing overview, and property details, and it builds an interactive data room similar to Dropbox. The AI processes any documents you'd typically share with lenders - the more comprehensive your uploads, the better the output. All uploaded files become part of the data room that lenders can access through custom shareable links. This gives lenders everything they need in one organized location, from the initial deal book through supporting documents like leases, appraisals, and financial statements.

To export your lender list to a spreadsheet:

1. First, add the lenders you want to include to your deal
2. Click the export button at the top of your lender list
3. The system will generate a CSV file containing lender names, primary contacts, and conversation status

You can customize the exported spreadsheet by removing contact information or status updates if you only want to share lender names for approval purposes. This is particularly useful when borrowers want to review and approve your lender outreach list.

The Quote Matrix automatically compiles all lender quotes side-by-side as you receive them via email, whether in email body text or PDF term sheets. It extracts key details like loan amounts, interest rates, LTV ratios, and calculates debt service payments for easy comparison. You can export this data to Excel or CSV format.

MSR (Marketing Status Report) generates an AI-built 6-8 page PDF summary of your deal's marketing progress, showing which lenders provided terms, which haven't responded, and which passed. This report is ideal for sharing updates internally with your team or with sponsors who want to track your outreach efforts.

Once you add lenders to your deal, key fields become locked to maintain consistency with your generated lender list. These "hard coded filters" include asset type, market location, loan size, and loan type - the four primary inputs that determine which lenders appear on your list.

If you change these core parameters after generating your lender list, it would create conflicts since your selected lenders may no longer be appropriate matches. If you need to modify these locked fields, you'll need to create a new deal with the updated parameters. However, you can still edit other details in your memo as needed before sending to lenders.

Top matches use a real-time feedback system that continuously improves accuracy based on actual lender responses. When a lender responds positively to a deal, they move up in future rankings. If they pass or indicate the deal is outside their parameters, they move down the list.

The accuracy varies, but the system learns from each interaction across the platform. Your existing relationships also matter - a lender might work differently with you than with other users. Most successful users focus on top matches for better results rather than casting a very wide net, as this typically yields higher response rates and better terms.

Yes, you can use the platform to identify the right lender contacts without sending emails through the system. When you add lenders to your deal, the AI identifies the best contact at each lender based on their transaction history and responsiveness rates. Currently, the platform doesn't support sending to multiple primary contacts at the same lender in one bulk email, but you can work around this by CCing additional contacts when sending individual outreach emails from the outreach table.

Yes, you can absolutely upload your own PowerPoint deck. You have two options: 1) Use the platform to input your deal information and financials, generate the offering memo, export it to PowerPoint, make your edits, then upload your customized version, or 2) Create your presentation entirely outside the platform and upload it directly. Once uploaded, you can add your custom deck to the vault and set it as the featured document, which will designate it as your offering memorandum. When uploading, save your PowerPoint as a PDF for best formatting results.

Yes, you can expand your search using the filters. Click on the filter button to add or remove specific types of lenders from your results. You can also use "search by lender focus" to view a more curated list of lenders. If you find a specific lender through your research that doesn't appear in the automated results, you can request to have them manually added to your lender list. Keep in mind that filters in any direction might exclude otherwise suitable lenders, so expanding your search criteria can help when your initial outreach doesn't get traction.

To view your generated offering memorandum, click on your deal and then select "review deal package." This is where you'll find the actual OM that was created for your deal.

You can view all lender feedback in the outreach tab, which organizes information into clear sections: terms from lenders at the top, ongoing conversations without terms in the middle, and all pass reasons at the bottom. The deal overview page also shows high-level notes including specific terms and pass reasons from each lender. Additionally, you can generate a Marketing Status Report (MSR) that creates a 6-10 page PDF summarizing all lender feedback, passes, terms, and deal status to share with borrowers. A consolidated checklist feature for tracking lender questions and to-dos is currently being developed.

Yes, the quote matrix includes filtering capabilities. You can access these options through the display settings to customize how you view and organize your quotes.

Yes, Lev is actively developing equity search functionality. We've recently launched the ability to create equity raise and investment sales memos, and we're building the underlying contact system to handle different types of contacts beyond just lenders and sponsors, including family offices and other equity sources.

This feature is still in development because equity data is more private and scattered compared to debt markets, making it challenging to ensure data accuracy. We're implementing AI-powered data enrichment to better identify and validate equity contacts. While we don't have a specific launch date, this capability is a priority as we expand beyond debt financing.

Yes, lender feedback is valuable and gets automatically logged in our system. When lenders provide updates about their programs (like "we don't do ground-up construction"), this information flows to our team to update their profile data, though it may take multiple instances to trigger real-time updates to ensure accuracy.

For now, we recommend keeping notes directly on lender profiles so you can quickly reference important details about their preferences or restrictions. We're working toward making this feedback process more real-time while maintaining data accuracy through our capital markets team review.

Lev's recommendation system uses an internal scoring algorithm that considers multiple factors:

1. Historical transaction data - publicly available information about the lender's past deals
2. Contact responsiveness rates - how frequently specific contacts respond to deal submissions
3. Deal feedback - positive or negative responses from previous outreach efforts
4. Real-time activity - recent transactions, LinkedIn posts, and current market activity

Lenders are ranked from best to worst fit based on this scoring. If a lender recently passed on similar deals or indicated they're not active in certain asset classes, they'll appear lower in the rankings. The "Lev Recommended" designation also prioritizes contacts from your own network when available, as these relationships typically yield better response rates.

The PSA expiration date field provides timeline context for your deal but doesn't significantly impact lender matching or rankings. This information is primarily shown in your deal overview and isn't heavily weighted by most lenders unless you specifically share those timeline details with them.

The main factors that affect your lender matching are the hard filters: property location, asset type and sub-type, loan type, loan amount, and your company's location. If you don't have a signed PSA yet, you can simply select "no signed PSA" and proceed with your lender search.

Direct database searches aren't available by design. Instead, the platform uses AI-driven matching to suggest the most relevant lenders for your specific deals and markets. This approach ensures that lenders receive deals that actually fit their lending criteria, maintaining positive relationships and higher response rates. If you need contacts for a specific property type or location, you can create a deal with those parameters and use the high-level facts to generate an accurate lender list. We're also building functionality to let you search and tag your own imported contacts.

You can send bulk emails using the lightning bolt (actions) icon in the Quote Matrix. Click the lightning icon and select "Launch to all lenders" to send to your entire list at once. Alternatively, go to the Outreach section, select the lenders you want to contact by checking their boxes, then click "Send a bulk email" at the bottom of the screen. You can personalize these bulk emails using input fields like "lender name" which will automatically populate each recipient's name. When editing templates, you can save them for this deal only or for all future deals by checking the appropriate box.

The platform categorizes lenders into suggested matches, your network, and other lenders in the database. The suggested lenders are filtered based on your deal parameters to ensure good fit and maintain platform integrity. Your network includes lenders you've imported, some of which may show as "not a match" but can still be added to deals. The total lender count may change when you launch a deal because the system removes lenders who don't meet the specific criteria or are outside the target market. This filtering prevents lenders from receiving irrelevant deals, which helps maintain positive relationships and response rates.

Portfolio deals aren't currently available as a built-in feature, but you can work around this in two ways. First, you can combine all properties into a single deal by adding up the total numbers and presenting it as one package in your outreach and offer memorandum. Second, you can create separate deals for each property, which may give you different outcomes since some lenders might be interested in smaller deals but not larger ones. The platform considers property locations when generating lender lists, so properties that aren't too spread out geographically should work well as a combined deal.

Lev uses a dynamic ranking system that adjusts lender positions based on their response patterns and deal feedback. When a lender declines deals that match their stated programs, they move lower in the rankings rather than being removed immediately. If negative feedback continues over time, they may eventually be removed from relevant deal lists. The system also uses AI to scrape transaction data from the web and other sources to automatically build and update accurate lender programs. This approach accounts for rapidly changing market conditions while ensuring lenders aren't permanently penalized for occasional deal declines that might have legitimate reasons.

When lenders send you tear sheets or email descriptions of their lending criteria, this information is automatically added to their profile in the system. You can also add details manually in the lender's notes section. The platform tracks all deal activity and lender responses in the background, building a comprehensive view of each lender's actual appetite and preferences. An upcoming feature will provide much more granular tracking of exactly how lenders respond to specific deals and their stated terms. If you have an Excel spreadsheet with lender contacts, the system can automatically scan it and identify which contacts are lenders for you.

Lender quotes are automatically extracted from emails and PDFs when they contain sufficient detail beyond basic statements. For manual entry, go to your placement table, click on the specific lender conversation, select 'Terms,' then 'Add New Quote.' You have two options: 1) Enter information manually in the form fields, or 2) Use the 'Upload with AI' feature where you can either paste the terms into a text box or drag and drop a PDF file. The AI will automatically map all the quote details for you, eliminating the need for manual data entry.

You have two options for handling multiple guarantors: 1) Add each individual guarantor as a separate sponsor in the system, or 2) Keep the main entity as the sponsor and include detailed information about individual guarantors in the 'About Me' section, which will appear in the deal book. For document collection from multiple sponsors while maintaining privacy, the system can create separate tasks assigned to different guarantors. When you send them collection links, each person will only see their own requirements and tasks, not those of other guarantors on the deal.

When a lender provides updated terms (such as changing proceeds from $1.7M to $1.8M), the system creates a new quote entry rather than updating the existing one. This preserves the history of all quote revisions. For quotes that don't automatically populate, it's usually because there isn't enough required information for the AI to build a complete term sheet - certain essential details like loan amount are needed for processing.

To regenerate your deal book, click the "Edit" button at the top right of your deal book page, then select "Regenerate" from the top menu. You have two main options: you can regenerate specific slides by selecting which ones you want to update, or regenerate the entire deal book. If you want to add new content like photos, simply upload the additional files first - the regeneration will be based on both your existing files and any new files you've added. You can also add individual slides manually by clicking "Add Slide" at the top of the slide deck and choosing from different template options.

The X mark on max LTV indicates that your deal's loan-to-value ratio might be higher than what these lenders have historically funded. However, this shouldn't prevent you from reaching out to these lenders. LTV requirements can vary significantly based on the overall strength of your deal, and these lenders may still be a great fit for your project. We recommend treating this as general guidance rather than a hard restriction and still consider sending your deal to these lenders.

No, project costs and land acquisition should be entered separately. When inputting your deal financials, keep the land cost separate from project costs. You can include items like closing costs within project costs, but exclude the actual land purchase price.

Yes, you can download your placement table by clicking "Actions" and selecting "Download Placements." This feature exports an Excel spreadsheet that includes all the information from your placement table, including lender names, contact email addresses, next steps, latest notes, term sheets, and conversation context. The downloaded file will have the same structure as what you see on screen. Note that this feature requires an active subscription and is not available during trial access.

Yes, you can do both. You can generate an AI-powered deal book first to review and edit it, and then decide whether to use it or upload your own offering memorandum instead. To create a deal room with your own materials, click "Get Started with Lev" and upload your files where it says "OM." The AI will analyze your documents and any other relevant files to compile the information into an 8-10 slide presentation that you can further customize.

Yes, you can use the platform to search for lenders by market and property type to get a high-level overview of available lenders. This will show you lenders with programs that match your broad criteria (like multifamily properties in New York), but won't provide specific contact information. You can refine your search by market type (primary, secondary, tertiary) to narrow your results. For the most accurate lender matches and contact details, you'll need to create a full deal with specific property information and loan request details. The platform covers most real estate lenders, though we don't currently support equity contacts like family offices, and while there's no specific SBA filter, most banks offer SBA programs.

The platform now supports equity raise memo generation directly from your deals. You can create professional equity memorandums using the same deal information, with content automatically tailored for equity investors. We're also actively developing a full equity investor platform that will allow you to connect with equity sources, similar to our debt platform. This equity platform is expected to launch by the end of Q1 or beginning of Q2. Currently, our primary focus remains on senior debt and subordinate debt financing, but equity functionality is a key part of our roadmap.

The memo generation feature uses AI to create professional offering memorandums and deal books from your uploaded files. You upload documents like rent rolls, sponsor bios, underwriting models, and other supporting materials, and the AI interprets this information to generate a comprehensive presentation. The system offers customizable templates and gets you about 90% of the way to a finished product. You can then use the built-in editor (similar to Google Slides) to make final adjustments, edit tables and images, and customize the layout. The latest version can create marketing-quality deal books in about 15 minutes that would typically take a marketing team 2-5 days to build in PowerPoint.

We recommend starting with 10-15 lenders from your recommended list for your initial outreach batch. This number is manageable and allows you to move quickly while gathering valuable feedback. You can start from the top of the list, as all recommended lenders should be solid matches for your deal. Don't spend too much time researching each lender individually, as program details and appetite can change frequently - you might do extensive research only to find their current focus has shifted. Instead, use your initial outreach emails to gauge interest and gather real-time feedback. After your first batch, incorporate the responses and feedback you receive to refine your approach before expanding to the next 15 lenders. This iterative method is more effective than sending one large blast to all recommended lenders at once.

Currently, when you change deal data like purchase price or total cost, the Quote Matrix does not automatically update. You have two options: regenerate the entire Quote Matrix with the new data, or manually update the data fields one by one. Automatic updating functionality is planned for release in 2026, which will also include better detection of data changes and visibility into data sources.

Lender tear sheets in the platform come directly from our collection efforts with lenders. We obtain these through direct communication with lenders or when they submit updated materials to us. If you receive a tear sheet that conflicts with the program data shown in the platform, you can share it with your contact and our capital markets team will verify and update both the tear sheet and the underlying program data. We also collect anonymized deal data from client transactions to improve our lender matching, with specific deal information available upon request when needed for similar situations.

It's generally better to start with conservative valuations and improve your loan-to-value ratio later, rather than starting optimistically and having to request higher leverage. When you have confidence in a higher property value (such as expecting a better appraisal), you can mention this potential in your initial outreach to lenders without committing to specific numbers until you have documentation in hand. Moving from a higher loan-to-value ratio (like 78%) to a lower one (like 70%) is easier for lenders to accept than the reverse scenario.

Deal rooms are currently only accessible to lenders. However, once your deal room has been generated, you can download a PDF version of the deal book and share that with borrowers or other stakeholders. While they won't have access to the interactive deal room features, they'll be able to review the complete deal book content through the PDF.

Yes, you can include deal room links in mass emails using the input dropdown in the email template. Each lender will automatically receive their own unique deal room link tied to their domain name. However, we recommend different approaches based on your relationship with the lender:

For existing relationships: It's fine to include the deal room link in your initial outreach.

For new lenders: Wait until your second touch to include the link. Including links in first-time outreach to new contacts can increase the chance of your email landing in their spam folder.

You can generate comprehensive deal progress reports using the Marketing Status Report feature. To create one:

1. Click the Quick Actions dropdown
2. Select "Generate New Report"
3. Provide any deal updates when prompted by the AI

This generates an 8-10 page PDF that includes your placement table, lender response status, number of term sheets received, specific terms offered, pass reasons, and overall deal progress. The report is easily shareable with borrowers and provides a complete snapshot of your marketing efforts and results.

You can easily update lender contacts in the Outreach tab when you're redirected or need to replace unresponsive contacts:

For redirected contacts: When a lender refers you to a better contact, click on the conversation to open the drawer, then replace the primary contact. Going forward, all conversations will be tracked under the new contact while maintaining the lender's historical data.

For unresponsive contacts: When contacts are marked as unresponsive (either manually or after not responding to multiple emails), the AI will suggest alternative contacts at the same institution with better response rates. You can then replace the contact and relaunch your outreach.

The OM generator uses AI to create offering memorandums from your uploaded documents. You can upload any deal-related files (PDFs, Excel files, deal overviews) to the deal room, and the AI will read and analyze the content to generate a comprehensive OM.

The AI typically gets you about 90% of the way there, producing around 10 slides of content that may need minor aesthetic tweaks to reach the finish line. A more robust version of this feature is currently in development and will be released soon.

Yes, you can copy and paste loan terms directly from emails into the 'enter terms received' section. You also have the option to input terms manually if needed - for example, if you received terms over the phone. When lenders send terms via email, the system can automatically populate the information for you.

The data room is an interactive document sharing platform that functions like a secure Dropbox for lenders. When you publish your offering memorandum, it creates a data room that features your OM in the center along with other relevant deal documents like fact sheets and supporting materials. Lenders can access this data room through a secure link that you can share directly or include in a PDF download. This link can be customized or disabled based on your preferences.

You can export your quote matrix at any point by downloading it as a CSV file, which you can then share with your team or clients. The system automatically populates the quote chart as terms are received, reducing manual work.

The lender data comes from three primary sources: historical transaction data pulled from public county records through third-party partnerships, live transaction data from deals currently being processed through the platform, and direct validation with lending institutions through our lender strategy team. This ensures you have access to both verified loan program details and real-time market data from billions of dollars worth of transactions.

The Quote Matrix has different automated follow-up behaviors depending on your relationship with the lender. For new lender contacts, automated follow-ups are sent 2 business days after the first touch, then again 2 business days after that if there's no response. For existing relationships in your system, follow-ups are handled differently and may require manual sending to allow for more personalized communication. You can identify existing relationships by looking at the lender logos in your placement table - darker logos indicate existing contacts, while lighter logos show new recommended contacts who will receive automated follow-ups.

Unfortunately, deals that are lost due to session timeouts cannot be recovered and will need to be recreated from scratch. To avoid losing work in the future, make sure to save your progress regularly and be aware of session timeout limits when creating new deals.

Currently, term sheets cannot be uploaded directly by users. If you have a term sheet that would update a lender's program information, you'll need to send it to your account contact so the capital markets team can manually verify and add the information to the system. The future goal is to allow users to upload their own term sheets directly and work off that data, but this self-service functionality is not yet available.

The Quote Matrix shows lender programs and lending parameters but doesn't display specific interest rates due to their high variability. You can view verified programs for each lender and check the insights tab for pulse updates that provide general market information about where lenders are active and what they're looking for. While some information is anonymized for confidentiality, you can generally get an idea of a lender's current market position and available term sheets through these program details.

Approximately 70-75% of deals launched through the Lev platform ultimately close. Some deals don't move forward for reasons outside of the lending process, such as appraisals coming in short, environmental concerns, acquisition deals falling through, or market conditions not meeting expectations for refinances.

Deals that do close may finish with either existing lending relationships or new relationships discovered through the platform. Both scenarios are common, and sometimes existing lenders become more competitive on terms when they know other institutions are also competing for the deal.

While the platform connects you with vetted lenders based on historical transaction data, it's important to thoroughly vet each lender's process and understand how firm their initial term sheets are to minimize potential retrades during the transaction.