If you work in commercial real estate, you already know: the deal process is full of manual, repetitive work. Researching properties. Enriching contacts. Building offering memorandums. Searching for the right lender. Tracking deal status across spreadsheets and email threads.
Most CRE professionals spend more time on administrative tasks than on the relationship-building and strategy that actually close deals. The good news is that AI-powered tools can now handle many of these tasks faster and more accurately than doing them by hand. Here are five workflows you should be automating right now.
1. Contact and company research
The manual way
You find a property you're interested in, so you start Googling. Who owns it? Who's the decision-maker? What's their email? Their phone number? You check LinkedIn, county records, corporate websites, and maybe a paid database. Thirty minutes later, you have a partial profile with a few missing pieces.
Now multiply that by every contact on every deal. For a single outreach campaign targeting 50 sponsors, you're looking at 20+ hours of research.
The automated way
AI-powered enrichment tools can pull contact and company information in seconds — name, title, email, phone, company details, and portfolio history. You provide a name or company, and the platform fills in the rest from multiple data sources.
At Lev, the Originate product handles this. Basic contact enrichment costs 50 credits. A full company enrichment with portfolio details costs 500 credits. Smart search across the entire database is free. For a typical outreach campaign using Lev data to identify in-market sponsors and then build a multi-step outreach campaign, the total is about 10,000 credits.
Time saved: 20+ hours per campaign → minutes
2. Document extraction and deal data
The manual way
A sponsor sends you a stack of documents — rent rolls, operating statements, lease abstracts, appraisals. You open each one, manually pull out the key data points (NOI, occupancy, unit count, cap rate, lease terms), and type them into a spreadsheet or your deal tracking system. One typo in the financials can cause problems downstream.
The automated way
AI can now read documents — PDFs, spreadsheets, scanned files — and extract structured data automatically. Upload a rent roll, and the AI pulls every unit, every rent amount, every lease expiration date. Upload a T-12 operating statement, and it extracts income line items, expenses, and calculates NOI.
Lev's Deal Prep product does this at 50 credits per field extracted. You can also have the AI populate data fields automatically (100 credits per field) and let AI handle file naming and sorting (50 credits per action). For a typical deal, preparing all the data from uploaded documents costs about 5,000 credits.
Time saved: 2–4 hours per deal → minutes
3. Offering memorandum generation
The manual way
Building an offering memorandum from scratch typically takes 4–8 hours for an experienced broker. You're pulling data from multiple sources, formatting everything into a professional document, writing the narrative sections, double-checking that every number is consistent, and making it look good enough to send to a lender. For complex deals, it can take days.
The automated way
AI can generate a complete, professional offering memorandum from your deal data. It pulls the property details, financial summary, market analysis, and sponsor profile into a structured document that's ready for lender review.
With Lev, generating an OM from your deal data costs 1,250 credits. If you want the OM enhanced with Lev's proprietary market research and lender intelligence, that's 2,000 credits. You can even generate or edit images for the document with AI at 150 credits per image.
Time saved: 4–8 hours per deal → under 30 minutes
4. Lender matching and outreach
The manual way
You have a deal ready to finance. Now you need to figure out which lenders to approach. You pull up your spreadsheet of lender contacts, try to remember who's been active in this market recently, send a few emails, make a few calls, and wait. Maybe you reach out to 5-10 lenders from your personal network. If none of them bite, you start from scratch with a new batch.
The automated way
AI lender matching analyzes your deal against a database of thousands of lenders — their property type preferences, geographic focus, deal size sweet spots, and current lending appetite — and ranks them by fit. Instead of guessing which lenders might be interested, you get a data-driven shortlist.
Lev's Finance product matches your deal to lenders from a database of 7,000+ lenders and 50,000+ contacts, built from 3+ years of real deal flow data. AI-matching costs 100 credits per deal. Revealing a lender's full profile costs 500 credits. You can then enroll matched lenders directly into an outreach campaign at 10 credits per lender. A full end-to-end financing workflow costs about 15,000 credits.
Time saved: Days of outreach → hours
5. Deal room management and status tracking
The manual way
Once a deal is in motion, keeping everyone on the same page is its own job. You're emailing documents back and forth, tracking which lender has which version of the OM, following up on missing items, and updating your team on deal status via Slack, email, or (worst case) a shared spreadsheet that nobody remembers to update.
The automated way
Digital deal rooms give everyone involved — your team, the sponsor, even the lender — a single place to access documents, track progress, and see real-time status. AI can auto-sort files, generate checklists from templates, and flag when items are missing or overdue.
With Lev, creating a deal room or checklist is free. Generating a checklist with AI and templates costs 200 credits. Real-time status reports cost 500 credits. The platform also handles term sheet extraction (100 credits) so you can compare lender quotes side by side without manually transcribing each one.
Time saved: Hours per week of coordination → automatic
The ROI of automation
Let's put some numbers on this. For a broker closing 3 deals per month:
- Contact research: 20 hours saved per campaign
- Document extraction: 6–12 hours saved (2–4 hours × 3 deals)
- OM generation: 12–24 hours saved (4–8 hours × 3 deals)
- Lender matching: Days of outreach time saved
- Deal management: 5–10 hours per week in coordination
That's potentially 50–70+ hours per month of manual work that can be handled by AI — time you can redirect toward building relationships, sourcing new deals, and growing your business.
With Lev's credit-based pricing, you only pay for the AI-powered actions you use. Manual tasks like searching, storing files, managing deal data, and creating campaigns are free. Credits start at $50/month for 5,000 credits, with every plan including unlimited seats, 100% rollover of unused credits, and access to every workflow and feature.
Getting started
You don't have to automate everything at once. Start with the workflow that costs you the most time right now:
- If you spend hours researching contacts → Start with Originate for AI-powered enrichment
- If you dread building OMs → Start with Deal Prep for AI-generated memorandums
- If finding the right lender is your bottleneck → Start with Finance for AI lender matching
The CRE industry is at an inflection point. Teams that adopt AI-powered workflows now will be able to handle more deals with fewer hours of manual work — while teams that stick with spreadsheets and email chains will fall behind. The tools are here. The question is whether you'll use them before your competitors do.
Get started with Lev and see how credits-based AI tools can transform your deal workflow.