For most commercial real estate firms of 4 to 20 people, automated offering memorandum software is the right answer and a custom OM generator is not — a platform like Crexi Create, Henry, Buildout, or PropertyMetrics will turn your financials and photos into a branded OM in minutes, carry the template, and cost a subscription rather than a build. Custom OM generation earns its keep only under narrow conditions: high deal volume against a repeating asset type, a brand and template no vendor will bend to, or a data layer you need to own. This is a decision framework, not a product ranking. It defines the three real routes — a thin workflow on ChatGPT, Claude, or Gemini; an off-the-shelf OM platform; or a custom build — names where each one breaks, and gives you a short test that maps your firm’s actual situation to one of them before you spend a dollar.
Three routes, honestly named
The choice is usually framed as two options — buy software or build your own. It is really three, and naming the middle one changes most small-firm decisions.
Route one — a thin LLM workflow. You use ChatGPT, Claude, or Gemini directly, with a saved prompt that turns your deal facts and a rough outline into OM prose: the executive summary, the investment highlights, the location narrative, the tenant overview. No integration, no template engine, no engineering. A person drops in the numbers and the model writes the words. Cost is a per-seat subscription. This is the route the tool listicles never mention, because no one sells it to you.
Route two — automated offering memorandum software. You buy a purpose-built product — Crexi Create, Henry, Buildout, PropertyMetrics, CREOP, or CREBuilder — that ingests your financials, rent roll, and photos and assembles a branded, editable OM with maps, demographics, and a designed layout. The vendor carries the template, the auto-fill data, and the export. Cost is a subscription, often priced per seat or per deck.
Route three — custom OM generation. You build your own system on general-purpose components: a document-AI reading layer that abstracts your leases and rent rolls into structured fields, a language model that drafts the narrative sections, and code that stitches it into your exact template and pushes a finished file. You own the logic, the brand, and — the part that decides the whole question — the maintenance.
“Automated offering memorandum software” and “custom OM generation” are the two poles of the choice. The thin LLM workflow sits before both, and for a broker producing a few OMs a month it is often the honest answer. Every route depends on reading the source documents correctly first, which is the subject of our guide to turning lease stacks into structured data.
What automated OM software actually gets you
An OM is not a hard document to write. It is a tedious one to assemble, and that is exactly what a platform removes. Purpose-built tools earn their subscription on four things a blank document does not give you.
Assembly, not just drafting. A general model writes a paragraph; a platform builds the whole package. Enter a property address in CREBuilder and it pulls parcel data, tax records, maps, aerial imagery, and demographics from a database it reports at more than 155 million properties. PropertyMetrics generates a branded brochure in under ten minutes. The saved hours are in the layout and the data-gathering, not the prose.
A brand that stays consistent. Vendor templates embed your logo, colors, and section order into every OM your team produces, so the associate’s deck looks like the principal’s. Buildout ships professionally designed templates and syndicates the finished listing to CRE portals; its listing assistant drafts property and location descriptions and refines them as brokers edit.
Financials wired to the document. The better platforms ingest your workbook and place the numbers for you. Henry, for instance, accepts effectively any Excel model and parses the relevant tabs, metrics, and assumptions rather than forcing you into a standard template; when your underwriting changes, it regenerates the affected pages. Crexi Create takes financials, leases, rent rolls, or an address and returns a structured, editable OM draft in minutes, enriched with its own marketplace data to fill gaps.
Maintenance you never perform. When a data source shifts or a model needs updating, the vendor’s team handles it. For a firm with no IT department, that is the quiet core of the value: someone else keeps the machine running.
The cost is loss of control and per-unit pricing. You accept the vendor’s template logic and a bill that grows with seats or deck volume — Henry’s subscriptions, for reference, start around $1,500 a month and scale with team size and output. For a small firm that mostly needs clean, on-brand OMs out the door fast, that trade is usually worth it. For how buying versus building plays out across document work more broadly, see our decision framework for off-the-shelf document AI versus custom pipelines.
What custom OM generation actually costs
The consultant version of a custom OM generator sounds like a weekend of wiring: read the rent roll with a document-AI service, hand the fields to a model, drop the output into a template, export a PDF. The build genuinely is close to that simple. The build is not the cost. The maintenance tail is the cost, and it is where small firms get hurt.
A custom generator in production has to survive the real world. Rent rolls arrive in a dozen formats, and a T-12 comes as a scanned PDF one month and a fresh export the next. A model that placed every figure correctly in March starts drifting in June as documents shift and the underlying model updates. None of this breaks loudly. It breaks silently — a wrong occupancy figure in the investment summary, a stale cap rate in the pro forma — and an OM is a document a buyer relies on and a seller signs. In a firm with no engineer, no one is watching, and the generator degrades into a tool nobody trusts.
That is the honest total cost of ownership: not the initial build, which a competent developer or an agency delivers inside a normal automation budget, but the standing obligation to keep every generated OM accurate and on-brand. In the current market a scoped custom automation project runs roughly $25,000 to $150,000 depending on complexity, and the number small firms forget is the recurring cost of the person — internal or retained — who owns it after launch. A platform folds that maintenance into a subscription; a build hands it to you. The full cost picture is laid out in our breakdown of what a custom document-automation project actually costs a CRE firm.
None of this makes custom wrong. It makes custom a commitment rather than a purchase, and a firm should build only when the return clearly justifies carrying that commitment.
The five-question decision test
Run your firm through these five questions in order. The first unavoidable “build” answer points you toward custom OM generation; if none is unavoidable, you belong on one of the two lighter routes.
1. How many OMs do you produce per month? Under a handful, a thin LLM workflow or an entry platform tier is almost always right — the volume never repays a build. In the dozens, automated OM software earns its keep on assembly and consistency. In the hundreds, against the same asset type repeating, per-deck platform pricing starts to exceed what a custom generator would cost to run, and build enters the conversation.
2. How specific is your brand and template? If a vendor’s designed templates fit your look with minor tuning, buy. If your OMs carry a distinctive design that platforms cannot reproduce — a signature layout, an unusual section order, institutional-grade formatting a client demands — a custom generator built to your exact template can beat a constrained product. Most small firms are well served by vendor templates.
3. How complex are your financials? OMs live or die on the rent roll, the T-12, and the pro forma, not the prose. If your models are conventional, platforms ingest them cleanly. If your underwriting is unusual — bespoke waterfalls, non-standard escalations, asset types vendors parse poorly — the financial layer is where a custom build, tuned to your exact workbook, can pull ahead. This is the same commercial real estate document AI problem as lease abstraction, wired in reverse: reading the source correctly before anything is generated.
4. Who fixes it when it breaks? This is the question that ends most build fantasies. If your firm has no one who can diagnose a generator that placed a wrong figure, a custom system is a liability regardless of the other answers, because it will drift and no one will be watching. A platform’s vendor is your maintenance team. Be honest about whether you have a replacement.
5. Is fluency in place first? A firm that automates OM production before its people can judge an AI-drafted summary has built a machine no one can quality-check. The associate who signs off on a generated OM — on any route — needs to know what a good one reads like and where models fabricate. That capability comes before the tooling decision, and it runs through the small-firm playbook for out-operating larger competitors.
If questions one through four all point to buy, buy. The test is not trying to talk you into a build — only to make sure you build when the situation genuinely demands it.
The three routes, side by side
| Dimension | Thin LLM workflow | Automated OM software | Custom OM generation |
|---|---|---|---|
| Best for | A few OMs/month, prose help | Dozens/month, standard brand | Hundreds/month or a signature template |
| Setup cost | A subscription | A subscription | Build inside an automation budget |
| Ongoing cost | Per-seat | Per-seat or per-deck | Compute plus a maintenance owner |
| Brand control | You format manually | Vendor templates | Full — your exact template |
| Financial auto-fill | You paste and check | Vendor ingests your model | You build the ingestion |
| Who keeps it accurate | You, per document | The vendor | You |
| Breaks silently? | Visible, one OM at a time | Vendor catches it | Only if you are watching |
The table makes the pattern plain. Moving right buys control and, at high volume, lower unit cost — and it transfers a maintenance burden onto a firm that may have no one to carry it. Moving left keeps you cheap and safe but caps how much structure and brand precision you get. The right seat depends on your answers to the five questions, not on which product demos best.
The default for a small firm, and what flips it
The honest default for a 4-to-20-person CRE firm is buy, not build — a thin LLM workflow for low volume, or automated OM software once deal count and brand consistency justify it. Most small firms never hit the conditions that make a custom generator pay, and the ones that build prematurely spend an automation budget on a system that quietly rots because no one owns it.
Three triggers flip that default toward custom OM generation, and it usually takes more than one:
- Volume plus repetition. Hundreds of OMs a month against a repeating asset type, where per-deck platform pricing has grown past what a maintained generator would cost to run.
- A brand no platform will bend to. When a distinctive template or an institutional client requirement is worth real money and no vendor can reproduce it.
- Data as strategy. When the structured deal data behind your OMs is a proprietary asset you must own and embed, not a convenience you rent.
Absent at least one of these, a build is expensive insurance against a problem you do not have. The small-firm edge is speed and low overhead, and a maintenance obligation you cannot staff quietly erases both.
How to verify before you buy or build
Whichever route the test points to, prove it on your own deal before you sign or build. The verification is the same shape every time.
Take one real, representative property — ideally a messy one, with a rent roll that is not pristine and a T-12 that arrived as a scan. Produce a full OM on the candidate route: the platform’s trial, the thin workflow’s prompt, or a prototype of the generator. Then have someone who knows the deal check every number against the source — occupancy, base rents, expenses, the cap rate, the pro forma — and read the narrative for anything the model invented. A tool that lays out a beautiful OM and misplaces the NOI is not ready, and the demo will never show you that. This one-deal test costs an afternoon and saves a firm from a subscription or a build it will regret. For a head-to-head look at the platforms themselves, see our review of the best AI tools for small CRE firms working with leases and deal documents.
Frequently asked questions
What is the difference between automated offering memorandum software and custom OM generation?
Automated OM software is a purpose-built product — Crexi Create, Henry, Buildout, PropertyMetrics, CREOP, or CREBuilder — that ingests your financials, rent roll, and photos and assembles a branded, editable OM, with the vendor maintaining the templates, data sources, and export. Custom OM generation is a system you build yourself on general components — a document-AI reading layer, a language model for the narrative, and your own code that fits your exact template — where you own the brand, the logic, and the ongoing maintenance. The first is a subscription; the second is a build with a standing accuracy obligation.
Can I use ChatGPT or Claude to write an offering memorandum?
Yes, for the prose, at low volume. A saved prompt in ChatGPT, Claude, or Gemini will draft the executive summary, investment highlights, and location narrative at the cost of a subscription. What a general model will not do reliably is place your financials — it can transpose or invent a number that reads plausibly — so a person must paste the verified numbers and check every one. Firms producing dozens of OMs a month usually move to a platform for the assembly, template, and auto-fill a chat window does not provide.
How long does it take to create an offering memorandum?
Manually, a polished OM commonly takes 20-plus hours of analyst and design time. Automated OM software compresses the assembly to a few hours or less — PropertyMetrics reports branded brochures in under ten minutes, and Crexi Create and Henry generate structured drafts in minutes from uploaded financials and rent rolls. The time you keep is verification: someone still has to confirm every figure and read the narrative before the OM goes to market, because speed on the layout does not remove the need to be right on the numbers.
How much does offering memorandum software cost?
Platform pricing is a subscription priced per seat or per deck and varies widely by vendor and volume — Henry’s subscriptions, as a public reference point, start around $1,500 a month and scale with team size and output. A thin LLM workflow costs only a per-seat model subscription. A custom OM generator is a different order of spend: a scoped automation build generally runs $25,000 to $150,000 depending on complexity, plus the recurring cost of whoever maintains it.
When should a small CRE firm build a custom OM generator instead of buying?
Only when at least one specific trigger is present: hundreds of OMs a month against a repeating asset type where per-deck pricing exceeds a maintained generator’s running cost, a distinctive brand or template no vendor can reproduce, or a strategic need to own the deal data underneath. Absent those, and absent someone who can fix a generator that drifts, buying is the right call for a 4-to-20-person firm. Custom is a commitment to maintain, not a one-time purchase.
Does AI get the financials in an OM right?
Not reliably on its own — the financials are exactly where the risk sits. Platforms that ingest your workbook place the numbers more dependably than a general chat model, which can transpose or fabricate a figure, but neither removes the need for review. Every credible approach keeps a human confirming occupancy, rents, expenses, the cap rate, and the pro forma against the source, because a wrong number in an OM is a number a buyer relies on and a seller signs.
Is my confidential deal data safe in OM software?
It can be, but you have to verify the specific plan. OMs are built from the sensitive material a firm must protect — rent rolls, tenant financials, and sometimes an off-market seller’s identity. The safe pattern is a business-tier account or API where inputs are not used to train models by default, plus a rule to withhold or anonymize the most sensitive details until a buyer is under NDA. Confirm the terms of the exact plan you buy or the exact API you build on, because defaults differ and terms change.
Do I still need an analyst or designer if I use OM software?
Yes, in a changed role. The software removes the assembly, so the analyst spends less time formatting and more time on the underwriting and verification that decide whether the OM is credible. The designer’s involvement shrinks to setting up the branded template once rather than rebuilding every deck. AI is a fast first-pass assembler, not a substitute for the judgment that confirms the numbers and the story hold up.
What is the biggest mistake small CRE firms make automating OMs?
Building a custom generator they cannot maintain. The build is cheap and quick; the standing obligation to keep every OM accurate and on-brand as documents and models drift is neither, and a firm with no one watching ends up with a system that fails silently and loses trust. The second mistake is automating before the team is fluent enough to catch the errors any route produces — a beautiful OM with a wrong NOI is worse than a plain one that is right.
Where to start
The first question is not which platform to buy or whether to build. It is which of the three routes your firm’s real numbers point to — and whether your team is fluent enough that any of them is safe yet. A free AI-readiness assessment produces that read: a short working session that maps your deal volume, template needs, and financial complexity, and returns an honest recommendation for whether a thin workflow, an automated OM platform, a custom generator, or a month of fundamentals first is the right next move. Book a free AI-readiness assessment before you commit a dollar to either side of the buy-versus-build line.
Dirk Jan van Veen, PhD
