Industry Solutions

How are AI agents used in real estate?

Real estate teams use AI agents to qualify leads, answer listing questions, draft market summaries and keep CRM follow-ups moving. The architecture is an OpenAI-compatible agents API with retrieval over listing and market content, tool calls into CRM and listing systems, and human review of anything sent to clients. Plugsky offers 30+ models behind one API with deployment to match your brokerage's rules.

Key facts

API compatibilityOpenAI-compatible /v1/chat/completions (change the base URL)
Models30+ models from free to frontier tiers behind one API
Agent primitivesFunction calling, JSON mode and streaming are live
RetrievalEmbeddings and RAG over your own corpus
DeploymentPlugsky cloud, VPC, on-prem or air-gapped
PricingFlat monthly self-serve plans with fair-use usage; see the live pricing page
CRM toolsFunction calling into your CRM and calendar
Approval flowClient-facing sends reviewed by licensed staff

TL;DR

  • Keep your OpenAI SDK — change the base URL and model name.
  • 30+ models behind one API, from free chat models to frontier reasoning.
  • Deployment options from hosted cloud to VPC, on-prem and air-gapped.
  • Pilot qualification and listing Q&A for one office.
  • Licensed agents approve all client communication.

How it works, step by step

  1. Define the job, the permitted data sources and where a human must approve.
  2. Pilot lead qualification with one office's listings.
  3. Create a Plugsky account and generate an API key (free plan, no card required).
  4. Point your OpenAI SDK at the Plugsky base URL and map your model names.
  5. Index the approved corpus with embeddings and keep retrieval role-scoped.
  6. Keep client communication behind licensed review.
  7. Measure quality on your own samples, then scale with usage monitoring.
1Define the job, thepermitted datasources and where a2Pilot leadqualification withone office's3Create a Plugskyaccount andgenerate an API key4Point your OpenAISDK at the Plugskybase URL and map5Index the approvedcorpus withembeddings and keep6Keep clientcommunicationbehind licensed

Try it yourself

Open the AI agent builder →

Where AI agents pay off in real estate

Real Estate teams do not lack ideas for agents; they lack a safe path from demo to production. The pattern below targets repetitive, document-heavy work where a human can check the output, which is where agents earn their place first. Treat the agent as a new team member with a narrow brief, explicit permissions and a probation period, and rollout becomes an operations exercise rather than a leap of faith.

  • Lead qualification — capture budget, timeline and preferences, and route hot leads
  • Listing Q&A — answer property questions from approved listing content
  • Market summaries — draft area and comparable reports from data you supply
  • CRM follow-up — prepare personalised follow-up drafts for agent approval

A reference architecture for real estate agents

A qualification agent gathers structured lead details and updates your CRM through tools, while a listing agent answers from approved content and flags questions it cannot ground. Licensed agents approve all client communication.

  1. Listing and market retrieval collections
  2. Tools into CRM, calendar and listing feeds
  3. Lead scoring and routing rules
  4. Approval before client-facing sends

Data governance and human oversight

Property and client data is personal and sometimes regulated. Keep retrieval scoped, log interactions, and make sure your advertising and fair-housing obligations are reflected in what the agent may say — review outputs rather than assuming.

  • Scoped retrieval per market or office
  • Audit logs for generated messages
  • Human review before client communication
  • Documented sources for market claims

From pilot to production

Pilot lead qualification and listing Q&A for one office. Measure response time and qualification quality, then extend to market summaries.

Keep the rollout reversible: run the agent in shadow mode alongside the current process, compare outputs on your own samples, and move it into the workflow only when the evidence holds. Document what you measured so expanding to the next team is a decision, not a hope.

Honest comparison

CapabilityPlugskyTypical cloud AI APIBuilding in-house
API compatibilityDrop-in base URL changeUsually compatibleFull rewrite
Model access30+ models behind one APIVendor's own catalogueYou host each model
PricingFlat monthly self-serve plans; see live pricingOften per-tokenGPU + ops cost
DeploymentCloud, VPC, on-prem or air-gappedUsually vendor cloud regionsYou own the stack
Client adviceHuman-owned; agents prepareVariesYou enforce it
Listing contentApproved collections onlyVariesYou curate it

Frequently asked questions

Do we have to rewrite our application?

No. The chat completions API is OpenAI-compatible, so you change the base URL and model name and keep your existing SDK.

Is there a free plan?

Yes — the free plan includes two free AI models, plugsky-micro and plugsky-lite, with no credit card required.

How is pricing structured?

Self-serve plans are flat monthly with fair-use usage and no per-token charges; see the live pricing page for current plans.

Which endpoints are live today?

Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live. Audio, images, moderation, files, batch, fine-tuning, assistants and responses endpoints are coming soon — check the docs before planning around them.

Can agents give valuations?

They can summarise data you supply, but valuations and advice should come from licensed professionals; label any estimate as unverified.

Can it write to our CRM?

Yes, through tool calls your team defines; keep write permissions narrow and log every update.

How do we stay compliant in advertising?

Ground outputs in approved listing content, avoid ungrounded claims, and keep a human review step before anything reaches the public.