Custom AI Agent Development for Real Estate

Build a real estate AI assistant that answers property questions, captures and qualifies leads using defined criteria, recommends relevant listings, and supports research workflows. We can develop a custom AI agent or add AI capabilities to your existing real estate software.

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  • Top 1% of global
    Software Service providers

  • ISO 27001 certification
    by Bureau Veritas

  • GDPR-Compliant processes
    for responsible data protection

  • AWS trusted infrastructure
    for scalable solutions

  • Bureau Veritas —
    an independent global leader in testing, inspection, and certification.

Why Property Teams Use a Real Estate AI Assistant

A real estate AI assistant can take on routine inquiry handling across calls, chat, and listing platforms. Using speech-to-text, language models, and defined workflow rules, it can identify intent, collect key details, and route each request to the appropriate next step. A real estate listing AI agent can reduce repetitive work while keeping your team in control of lead qualification and follow-up decisions.

  • High inquiry volume

    AI voice agent services for real estate provide quick responses to common listing questions and capture essential details across calls and messages. Prospective buyers or tenants get a faster first response, while human teams spend less time repeating the same information.

  • Missed leads outside business hours

    An AI chatbot for real estate keeps your services available outside working hours, handling routine questions, recording contact details, and offering suitable next steps. More complex requests remain available for human follow-up rather than disappearing into an unattended inbox.

  • Manual lead qualification

    Early-stage screening becomes more structured when a chatbot collects details such as budget, preferred location, property type, and timing. Human agents can review the context and decide which conversations to prioritize.

  • Repetitive rental questions

    AI for property management is well suited to standard questions about availability, pricing, parking, pet policies, amenities, and application steps. Unusual, sensitive, or property-specific cases can be passed to a property representative.

  • Limited visibility into inquiry patterns

    An AI chatbot for real estate agents organizes data on inquiry sources, recurring questions, response times, and stated customer preferences. Connected analytics tools then help teams identify service gaps and support market analysis.

  • Scheduling friction

    CRM integration and calendar access make it possible for an AI agent to present available viewing slots, record bookings, and update customer history. The level of booking automation depends on available APIs and the scheduling rules agreed with your team.

  • Explore where AI could reduce repetitive work in your real estate workflows

Core Workflows a Real Estate AI Agent Can Support

An AI agent can connect inquiry handling with the systems and workflows your team already uses. Depending on the available data and integrations, it can improve the flow of information between prospective clients, property teams, and internal tools.

  • Listing discovery and recommendations

    Use stated preferences such as location, budget, property type, and amenities to surface relevant listings from connected data sources.

  • Inquiry context and CRM updates

    Capture contact details and conversation history in a structured format, giving property teams extensive context for follow-up.

  • Viewing coordination

    Present available time slots, record bookings, send confirmations, and pass exceptions to a team member when calendar and scheduling integrations allow it.

  • Property information and virtual tours

    Answer questions using approved listing data and direct prospective buyers or tenants to relevant floor plans, media, or virtual tours.

  • Follow-ups and document workflows

    Trigger agreed reminders, request missing information, and update workflow records, while leaving legal review and contractual decisions with the appropriate specialists.

  • Conversation and demand insights

    Organize recurring questions, stated preferences, inquiry sources, and response patterns to support reporting and market research.

Weighing which workflows are worth automating in your current setup?

Who We Build Real Estate AI Agents For

Property businesses work with different data sources, customer journeys, and internal processes. We design custom AI agents around the workflows that matter most to your team, from handling rental inquiries and coordinating viewings to adding conversational features to an existing PropTech platform.

  • Real Estate Agencies and Brokerages

    AI assistants can respond to listing questions, collect buyer or renter criteria, and prepare inquiry summaries before handing the conversation to an agent. CRM and calendar connections give realtors more context for follow-up and reduce manual updates between systems.

  • Property Management Companies

    For property managers, AI agents can route maintenance requests, answer approved tenancy questions, and collect the details needed for the next action. Where tenant screening forms part of the workflow, the system can gather application information, flag missing fields, and pass the case to the responsible team for review. It can also support renewal reminders and lease abstraction.

  • Rental Platforms and Marketplaces

    An AI agent embedded in a rental platform can help users search and compare listings, understand application steps, and navigate product features. Recommendations are based on connected property data and the criteria each user provides.

  • PropTech Companies

    We help PropTech teams add conversational AI to existing products or include it in new platforms. Depending on the use case, this may involve onboarding support, structured data capture, workflow automation, or analytics features built around approved product and customer data.

Tell us about your platform, users, and current workflows, and we’ll help you explore a suitable AI setup.

Our AI Chatbot Development Services for Real Estate

Beetroot engineers design and build custom AI chatbots for real estate business workflows, from property inquiries and viewing coordination to internal knowledge access. Our team can support a focused technical challenge or take the project through discovery, architecture, development, integration, testing, and post-launch improvement.

  • Chatbot Conversation Design

    Our UI/UX designers map conversation flows around practical user goals, such as finding a listing, checking availability, or reporting a maintenance issue. Prototyping and usability testing help reduce unnecessary steps and create clear handoffs when a request requires human attention.

  • Multimodal Chatbot Development

    Add text, voice, or image input where the workflow calls for it. Depending on the use case, our engineers can combine natural language processing, speech-to-text, and image processing components so users can ask questions, provide details, or submit visual information via supported channels.

  • AI Architecture and RAG Implementation

    We design the application, data, and model architecture around your existing systems, expected usage, and security requirements. Where appropriate, retrieval-augmented generation can ground responses in approved listing, property, or policy data and reduce unsupported answers.

  • Custom Chatbot Development

    Build a chatbot around a defined real estate workflow, such as inquiry handling, property search, viewing coordination, or internal support. We select and combine LLM, NLP, and software engineering components based on the use case rather than applying the same technical setup to every project.

  • CRM and Property Data Integrations

    Connect the chatbot with CRMs, calendars, communication tools, property databases, and listing feeds through available APIs. MLS, IDX, and marketplace integrations depend on provider permissions, data licensing, and the technical access available for the project.

  • Post-Launch Support and Optimization

    After release, our engineers can monitor system performance, review response quality, apply security updates, and adjust prompts, retrieval, integrations, or models as requirements change. Support can range from targeted fixes to ongoing engineering and maintenance.

  • Have a real estate workflow in mind? Let’s clarify the technical scope and integration requirements.

Developing an AI Agent for Real Estate: Our Process

Each project starts with a closer look at the workflows, systems, and data the chatbot needs to support. From there, we move through design, model configuration, integrations, testing, and iteration, adapting the process to your technical environment and project scope.

  • Discovery and Workflow Mapping

    Step 1

    Our business analysts work with key stakeholders to understand your current inquiry, sales, or property management workflows. Together, we identify where conversational AI could reduce repetitive work, clarify the project scope, and define the requirements for an initial solution.

  • Data and Compliance Planning

    Step 2

    Our data and security specialists review the available data sources, their quality, and how information should move through the system. We also consider relevant data protection requirements, access controls, retention rules, and internal review needs from the start.

  • Conversational Design and Prototyping

    Step 3

    We define the main user groups, common intents, conversation paths, tone, and handoff rules. Early prototypes help the team test how the chatbot should respond to routine requests, collect missing details, and escalate cases that require human attention.

  • LLM Selection and RAG Configuration

    Step 4

    Based on the use case, we assess suitable language models and deployment options against factors such as response quality, privacy, latency, language support, and cost. Where appropriate, we prepare the knowledge base and configure retrieval and prompting to ground answers in approved property, listing, or policy data.

  • System and Channel Integrations

    Step 5

    Our engineers connect the chatbot with relevant CRMs, property databases, calendars, communication channels, and listing systems through available APIs. The integration scope depends on your current software, data access, and workflow requirements.

  • Validation, Testing, and Iteration

    Step 6

    Before release, we test response quality, retrieval behavior, integrations, edge cases, and human handoffs against agreed scenarios. After launch, the system can be reviewed and adjusted as new data, user patterns, and business requirements emerge.

Cooperation Models

Choose a setup that fits your project scope, internal capacity, and current stage. Beetroot can extend your engineering team, deliver a defined solution, or run custom training around the AI topics most relevant to your business.

  • Dedicated Engineering Teams

    Long-term partnership

    Add AI/ML, chatbot, and cloud engineers to your existing team or build a dedicated remote team with Beetroot. This model suits long-running initiatives that need continuity, flexible team capacity, and close collaboration with your internal specialists.

  • Project-Based Development

    Scoped, milestone-driven delivery

    Work with a cross-functional team on a defined real estate AI initiative, such as a proof of concept, chatbot build, integration, or upgrade of an existing product. We agree on the scope, milestones, validation process, and handover before development begins.

  • Custom AI Workshops for Teams

    Advanced skill-building

    Build shared understanding around conversational AI, LLMs, RAG, workflow automation, or other topics relevant to your team. Each workshop is shaped around your current knowledge, business context, and practical business cases.

Not sure which model fits your current setup? Let's find the right one together

Example Tech Stack for a Real Estate AI Assistant

We select technologies based on the workflows, data sources, integrations, and deployment requirements of each project. The stack may combine natural language processing, generative AI, speech technologies, and standard application infrastructure.

Build your AI stack

  • LLM and Response Generation

    • OpenAI API/Anthropic API/Google Gemini API/
    • Locally-hosted LLMs

  • NLP and Semantic Search

    • Hugging Face Transformers
    • spaCy
    • txtai

  • Speech Recognition

    • OpenAI Whisper
    • ESPnet
    • Google Cloud Speech-to-Text
    • Azure AI Speech

  • Sentiment Analysis

    • VADER
    • Flair
    • AWS Comprehend
    • Google Cloud Natural Language
    • TextBlob

  • Databases and Caching

    • PostgreSQL
    • MongoDB
    • Redis
    • AWS DynamoDB

  • Machine Learning Frameworks

    • TensorFlow
    • PyTorch
    • Keras
    • scikit-learn

  • Application Development

    • Backend: Python, Node.js
    • Frontend: React, Vue.js, Angular
    • Mobile: Flutter

  • Cloud Infrastructure and Deployment

    • AWS
    • Microsoft Azure
    • Google Cloud Platform
    • Docker
    • Terraform

Meet Our Engineers

Our AI/ML engineers, NLP specialists, and cloud developers bring experience across conversational AI design, system integration, deployment, and post-launch improvement. Explore the example profiles below to find expertise that fits your project needs.

  • $55/h

    Full-Stack Chatbot Developer

    Daria P., 6+ years of experience
    Daria builds end-to-end chatbot solutions, covering backend, frontend, and mobile app integration. She ensures seamless deployment through cloud platforms and containerization tools.
    • Cloud Platforms: AWS, Azure, GCP
    • Flutter
    • IaC/Config: Terraform, CloudFormation (IaC), Ansible
    • JS/TS: Node.js, Next.js, Express, NestJS
    • Python (Django/Flask/Fastapi)

    Request full CV

  • $52/h

    Backend Chatbot Developer

    Tanya B., 8+ years of experience
    Tanya focuses on scalable backend solutions for chatbot frameworks and has deep expertise in database management and caching systems that optimize performance under heavy loads.
    • MongoDB / Redis / DynamoDB / InfluxDB
    • PostgreSQL / MySQL / SQL (general) / Snowflake / Redshift

    Request full CV

  • $50/h

    AI Chatbot Developer

    Anastasiia H., 6+ years of experience
    Anastasiia specializes in building robust chatbots using HuggingFace Transformers, AWS Comprehend, and Flair. As a chatbot programmer, she focuses on sentiment analysis, NLP pipelines, and conversational AI systems tailored to diverse industries.
    • Google Cloud Natural Language API
    • HuggingFace Transformers (BERT-based models) / VADER / SpaCy / txtai

    Request full CV

  • $22/h

    Data Engineer

    James N., 6+ years of experience
    Skilled in Kubernetes, AWS, GCP; experienced in managing production clusters across clouds.
    • Cloud Platforms: AWS, Azure, GCP

    Request full CV

  • $60/h

    Data Science Automation Engineer

    Olena S., 8+ years of experience
    Olena excels in automating data pipelines and integrating ML solutions into existing systems. Her expertise ensures scalable, secure data management and continuous improvement in predictive analytics, empowering your business with reliable insights.
    • Backend
    • Python (Django/Flask/Fastapi)

    Request full CV

  • $65/h

    Cloud Developer

    Tetiana K., Infrastructure Automation, 8+ years of experience
    Specializes in Google Cloud Platform and AWS with extensive experience in Infrastructure as Code using Terraform and CloudFormation. Skilled in building reliable and secure cloud environments, optimizing cloud costs, and implementing automated deployment workflows. She’s excellent at setting up infrastructure automation from scratch.
    • CI/CD
    • Cloud Platforms: AWS, Azure, GCP
    • IaC/Config: Terraform, CloudFormation (IaC), Ansible
    • Jenkins / GitLab CI / GitHub Actions / Git
    • Orchestration: Kubernetes, Docker
    • Python
    • Serverless Framework, AWS Lambda / GCP Cloud Functions

    Request full CV

  • $60/h

    DevOps Engineer

    Roman L., 8 years of experience
    Automation-focused and proficient in building and maintaining CI/CD pipelines for rapid and reliable software delivery.
    • Basic scripting (Python, Bash), Linux Systems
    • Cloud monitoring (AWS, GCP, Azure)
    • Jenkins / GitLab CI / GitHub Actions / Git
    • Orchestration: Kubernetes, Docker

    Request full CV

  • $42/h

    Middle ML Engineer

    Daniel M., 3+ years of experience
    Experienced with crafting end‑to‑end CNN pipelines in Python, leveraging PyTorch / TensorFlow and frameworks such as YOLO, RetinaFace, and SSD to deliver fast, accurate object‑ and face‑detection models.
    • CUDA / ONNX / TensorRT
    • Keras / TensorFlow / PyTorch
    • Matplotlib
    • NumPy
    • OpenCV
    • Python
    • RetinaFace
    • scikit‑image
    • SciPy
    • SSD (Single Shot Detectors)
    • Torchvision
    • YOLO

    Request full CV

  • $60/h

    Machine Learning Specialist

    Filip D., 5+ years of experience
    Filip applies deep learning frameworks to chatbot personalization and recommendation features. He leverages Keras and TensorFlow to fine-tune models for specific industries.
    • Keras / TensorFlow / PyTorch

    Request full CV

  • $78/h

    Lead ML Platform Engineer | Model Serving & Observability

    Taras B., 11+ years of experience
    Taras designs serving and monitoring layers for teams running several models at once, including drift detection and rollback controls in regulated environments.
    • Evidently
    • Go
    • KServe
    • MLflow
    • Prometheus / Grafana / ELK Stack / Google Cloud operations
    • Python

    Request full CV

Why Choose Beetroot to Build a Real Estate AI Agent?

Beetroot brings together AI engineering, product development, and domain expertise. We shape each project around the business’s workflows, systems, and practical constraints rather than applying a fixed chatbot setup.

  • Responsible AI Development

    We design AI systems with attention to data handling, human oversight, and the risks associated with each use case. Project goals and evaluation criteria are agreed early so the team can assess whether the solution is delivering useful results.

  • Support Across the Development Lifecycle

    Our cross-functional teams can support discovery, UI/UX design, architecture, LLM, and RAG implementation, integrations, testing, and post-launch improvement. The exact setup depends on your project scope and internal capacity.

  • Sustainability in Technical Decisions

    We consider maintainability, infrastructure use, and computational requirements when selecting models and designing the system. Where appropriate, this may include choosing more efficient models, limiting unnecessary processing, and building with long-term operation in mind.

  • Cross-Industry AI Experience

    Our teams bring experience in conversational AI, generative AI, and custom software development across different industries. This expertise helps us assess real estate use cases realistically and shape the technical approach around your workflows, data, and existing systems.

What Our Clients Say

Hear from clients who have worked with Beetroot across software development, dedicated teams, and AI projects. Their feedback reflects different types of collaboration, project scopes, and business needs.

  • Hans Fredrik Unelsrød
    CTO, Inspera AS

    Beetroot AB’s teammates’ success rate is at the same level as our local recruitment. The dedicated team members Beetroot AB provides operate within our team structure, so we handle the project management. When it comes to consultancy projects, their project management has been good; they’ve met the timeline targets, and their organization has worked well.

Featured Cases

These projects show how our teams apply conversational AI, workflow automation, and platform engineering across customer-facing and property-related systems.

  • AI Assistant for a Leading Tour Operator

    Our engineers built a CRM-integrated AI assistant that reduced inquiry handling time by up to 60% and manual workload for customer care and sales teams by 30%.

    Read the full story

    • Python
    • Django
    • Celery
    • OpenAI API
    • AWS ECS
    • Terraform
    • Github Action

Discuss Your Real Estate AI Project

Tell us about your current systems, the workflows you want to improve, and any technical constraints. We’ll review the details and get back to you with practical options based on your needs.

    FAQs

    Here are practical answers to common questions about real estate AI agents; for guidance on your specific use case, contact our team.

    Nick Tykhomyrov, CBDO, Beetroot

    Nick Tykhomyrov

    CBDO