How Tenant-Rep Teams Use AI Agents to Draft RFP Responses in Their House Format

How can tenant-rep and brokerage teams use AI to draft RFP responses?

Teams can configure an AI agent inside their licensed enterprise AI tool with their house format, voice, and past material, then use it to produce a first draft of each RFP response. A broker or coordinator edits and approves the draft. At Cresa, the RFP drafting agent we built in ChatGPT Enterprise writes in the house format and is now used in production.

RFP responses are a natural fit for an AI agent. They arrive often, they follow a familiar structure, and they draw on material the team has written many times before. They also carry the firm's name, so the draft has to sound like the firm, not like a generic AI tool. Getting both right is a matter of configuration and training, not of buying something new.

Why do generic AI tools produce weak RFP drafts?

A general-purpose chat window knows nothing about your firm's structure, voice, credentials, or approach. Asked to write an RFP response, it produces something fluent and generic that a team then rewrites from scratch. The fix is configuration: giving the agent your format, examples of strong past responses, and clear instructions, so its first draft starts close to what you would send.

This is the gap we see most often. Someone tries an AI tool on an RFP once, gets a draft that could have come from any firm, and concludes AI is not ready for the work. The tool was never told what the firm sounds like.

What goes into an RFP drafting agent?

Four things: the house format the response must follow, examples of strong past responses, written instructions on voice and what to include or leave out, and the specific RFP being answered. With those inputs, the agent drafts a response that matches how the team already writes. Without them, it guesses, and the team spends its time correcting the guesses.

• House format. The sections, order, and headings your firm uses, so every draft arrives in the right shape.
• Approved source material. Strong past responses and the standard descriptions of your team and services that you are happy to reuse.
• Instructions. How the firm writes, what to emphasize, what never to promise, and what to do when the RFP asks for something the material does not cover.
• The RFP itself. The specific questions and requirements, so the draft answers this request rather than a similar one from last year.

How did Cresa's RFP agent come together?

We built it with Cresa's team inside ChatGPT Enterprise, which Cresa had already rolled out across one of its highest performing offices. The agent drafts responses in Cresa's house format from the team's own material, inside a governed, IT-approved enterprise agent workspace. The team adopted it, and it is now used in production.

The build followed the same steps we use for every agent. We baselined the growth and finance teams with SINA, our AI literacy platform, so we knew who would be running the agent. We mapped the RFP workflow with the people who do it. We configured the agent in the tool Cresa already licenses. Then we taught sessions built on Cresa's real deal cycles, so the team practised on live work. The full story is in the Cresa case study.

The RFP agent was one of three. The others extract lease economics from landlord proposals and research prospects before broker meetings. All three are described on our AI agents for commercial real estate page.

Who should review an AI-drafted RFP response?

The person who would have written it. A broker or coordinator who knows the client and the deal reads the draft, checks facts and commitments, adjusts the emphasis for this prospect, and approves it. The agent saves drafting time. It does not decide what the firm promises a client, and it should never send anything on its own.

A short review checklist helps keep the standard consistent across the team:

• Every fact about the firm, its people, and its experience is accurate and current.
• Nothing is promised that the team has not agreed to.
• The response answers what this RFP actually asks.
• The tone fits the prospect and the opportunity.
• Anything the agent flagged as missing has been filled in by a person or deliberately left out.

How do you keep RFP content and client data safe?

Keep the agent and its source material inside the firm's governed enterprise AI workspace, with connectors IT approves. RFPs and past responses often contain client names and deal terms, so they should never be pasted into free personal AI accounts. Training should cover which tools are approved and what belongs in them, so people do not have to guess.

Is an RFP agent better than buying proposal software?

It depends on the job. A configured agent in a tool you already license suits a firm-specific workflow like RFPs, where house format and voice matter, and it adds nothing new for IT to govern. Dedicated proposal software can make sense for very high volume or deep integration needs. We advise honestly on which one fits your situation.

For most tenant-rep and brokerage teams we meet, the question is less about software and more about whether anyone has set up the tool they already have. Starting with a configured agent is also a cheap way to learn what you would need from a larger system, if you ever need one.

How do you get a team to keep using an RFP agent?

Train people on live RFPs, not demos, and give the agent an owner. When the team drafts real responses with the agent in a session and sees how much editing is left, they decide for themselves whether it helps. A small group of champions then improves the instructions and examples as the firm's material changes over time.

Agents drift if nobody maintains them. New credentials, new team members, and new service lines all need to find their way into the source material, or the drafts slowly fall out of date. Our AI champions program explains how champions carry that work. For the broader training program behind the agents, see our commercial real estate page.