Operational demands in real estate are growing faster than traditional systems can keep up. With 59% of organizations already adopting artificial intelligence and 72% planning a dedicated budget, AI has quickly shifted to a practical tool for managing daily complexity. Manual workflows slow entire portfolios, fragmented datasets make valuations harder to trust, and customer-facing processes often struggle when volume spikes.
This is where AI development companies in real estate bring value. They do so not by adding more technology for its own sake, but by helping teams streamline the parts of the business that have become too time-consuming or inconsistent to manage manually.
This guide helps you identify which companies actually solve those problems. If you’re done patching old processes and want AI that fixes real bottlenecks, start here.
Table of Contents
How We Selected These Top AI Companies for Real Estate
To create a list that’s genuinely useful for domain leaders, we assessed each company using criteria that reflect real operational needs rather than marketing claims or surface-level features. We focused on factors that any organization, whether a brokerage, PropTech startup, asset manager, or enterprise, should consider when assessing providers in real estate, and applied them consistently across all contenders.
Our first filter was relevance and proven experience. We prioritized vendors with established portfolios in real estate or PropTech, validated by real use cases. Companies with vague positioning or no documented results were excluded.
Next, we evaluated technical maturity. This comprised the types of AI capabilities they deliver (LLMs, NLP, computer vision, predictive analytics, automation, or multi-agent systems), their ability to support integrations across MLS, CRM, and property management ecosystems, and whether they demonstrate real engineering depth versus simple API-wrapping solutions.
We then reviewed delivery capacity and consistency. Team size, organizational stability, global presence, and clear engagement models helped us distinguish between players that can facilitate complex, multi-layered implementations and those better suited for smaller, contained projects.
Because real estate relies heavily on compliance and sensitive information, we also examined security posture. These covered ISO/SOC certifications, data-handling practices, and familiarity with industry-specific requirements.
Finally, we looked at practical fit. Some excel at niche tasks like computer vision or reporting, while others offer broader platform engineering or enterprise-scale artificial intelligence. Our goal was not to rank them, but to give readers a reliable cross-section of credible partners, each with different strengths, so they can match the right vendor to the right use case.
This criteria-driven approach ensures the firms listed represent the most capable, relevant, and dependable options for real estate teams evaluating intelligent applications today.
10 Best AI Companies for Real Estate Industry
Master of Code Global
Year founded: 2004
Employees: 200+
HQ: San Francisco Bay Area; global presence (US, EU, Canada)
Website
Specialization: End-to-end AI implementation, enterprise automation, custom conversational and agentic solutions.
Key services: AI strategy and audits, multi-agent systems, GenAI assistants, voice solutions, integrations, custom development, MVP, and full lifecycle delivery.
Main use cases: Lead qualification and nurturing (62% faster response times), property search assistance, virtual tour scheduling, 24/7 customer support (40% FAQ deflection), multilingual engagement, document Q&A, tenant service automation.
Strengths: Stable managed dedicated teams; deep expertise in multilingual Conversation Design (11 languages supported natively); proprietary LOFT framework enabling 43% faster setup and 20% savings at scale; structured AI PoC reducing development waste by up to 70%; ISO 27001-certified delivery; long-term partnerships with leading global brands.
Limitations: Smaller one-off tasks may not fully benefit from the company’s enterprise-grade delivery model.
Cherre
Year founded: 2016
Employees: ~115
HQ: New York, NY
Website
Specialization: Centralized data infrastructure for commercial real estate.
Key products: Knowledge Graph, Universal Data Model, Agent.STUDIO, Data Observability tools, API connectors.
Main use cases: Portfolio aggregation, reporting, valuation inputs, AI model dataset preparation.
Strengths: Scalable data organization and consistent schemas across fragmented sources.
Limitations: Relies heavily on third-party information quality; less suited for teams needing custom-built features.
LocalizeOS
Year founded: 2021
Employees: ~110–140
HQ: New York, NY; Tel Aviv (R&D)
Website
Specialization: AI-driven lead engagement and sales automation for residential real estate.
Key products: Hunter (texting assistant), LocalizeHQ, LocalizeBI, SMS engagement tools.
Main use cases: Lead qualification, buyer readiness scoring, follow-up automation, property matching.
Strengths: Scales agent outreach through conversational and behavioral analytics.
Limitations: Focused primarily on SMS workflows; limited applicability outside prospect nurturing, pre-built setups restrict deeper customization and advanced complexity.
Restb.ai
Year founded: 2015
Employees: ~38–69
HQ: Barcelona, Spain; Dallas, TX (US office)
Website
Specialization: Computer vision and image recognition for real estate.
Key products: AI Tagging, Photo Compliance, Property Descriptions, Appraisal Complexity Scoring, Document Compliance.
Main use cases: Listing enhancement, assessment support, MLS adherence, condition scoring, alt-text generation.
Strengths: Real estate-specific computer vision models with extensive tagging and high accuracy.
Limitations: Concentrated almost entirely on imaging workflows; limited solutions beyond visual and document analysis.
Aimprosoft
Year founded: 2005
Employees: 350+
HQ: Cyprus; development centers in Ukraine.
Website
Specialization: Artificial intelligence consulting and full-cycle software development with 10+ years of real estate focus.
Main services: Custom ML model creation, AI-assisted SDLC, predictive analytics, NLP and voice cloning, computer vision, IoT.
Main use cases: Lease abstraction, valuation tools, property management systems, CRM development, underwriting workflows.
Strengths: Large engineering team with AI-first delivery approach and long-term client retention.
Limitations: Broad industry coverage; may require additional coordination for deeply tailored intelligent workflows.
RealReports (formerly BHR)
Year founded: 2022
Employees: ~10–20
HQ: Brooklyn, NY
Website
Specialization: Property intelligence reports and AI-assisted file check.
Key products: RealReports (Standard, Premium, Investor), Aiden Copilot, Lead Generation Widget, white-label reports.
Main use cases: Property data aggregation, document review, climate and risk evaluation, agent presentation materials.
Strengths: Consolidates information from multiple providers into accessible formats with AI summaries.
Limitations: Narrow feature set centered on reporting; limited customization for broader operational workflows.
AscendixTech
Year founded: 1996
Employees: 200+
HQ: Dallas, TX; additional offices in Portugal, Poland, Ukraine, Luxembourg.
Website
Specialization: CRM platforms and custom software development for commercial real estate.
Key products: AscendixRE CRM (Salesforce and Dynamics), Ascendix Search, Composer, MarketSpace, AscendixDA.
Main use cases: CRM customization, property search, document processing, valuations, workflow automation.
Strengths: Long-standing CRE expertise with combined product and services capabilities.
Limitations: AI functionality is secondary to CRM and software development; broader projects may require external specialists.
Leobit
Year founded: 2014
Employees: 170+ engineers
HQ: Austin, TX; dev centers in Ukraine.
Website
Specialization: Full-cycle .NET, artificial intelligence, and web development with PropTech experience.
Main services: Platform engineering, AI/ML integration, predictive analytics, mobile development, blockchain-enabled workflows.
Main use cases: Real estate investment platforms, property management systems, lending solutions, tenant-facing apps.
Strengths: Broad stack and long-term PropTech delivery background.
Limitations: Capabilities depend heavily on client requirements; limited proprietary real estate models.
Plavno
Year founded: 2007
Employees: ~150
HQ: Warsaw, Poland; London office
Website
Specialization: Custom software and AI development with domain-specific PropTech teams.
Solutions built: IDX/MLS integration modules, property portals, chat/voice assistants, virtual tour and co-touring tools, IoT-based building systems, and the PlavnoNova platform with reusable components.
Main use cases: Listing replication, tenant workflows, prospect engagement, automated touring experiences.
Strengths: Pre-formed teams and modular blocks that shorten development time.
Limitations: Operates across many industries; depth of real estate AI proficiency may be limited.
Hicron Software
Year founded: 2012/2015
Employees: 300+
HQ: Wrocław, Poland; offices in Spain, Switzerland, Australia
Website
Specialization: Enterprise-grade PropTech development with SAP RE-FX and artificial intelligence.
Main services: AI lease abstraction, predictive maintenance, property management platforms, valuation tools, custom CRE analytics, enterprise system integrations.
Main use cases: Automating document workflows, detecting equipment issues, forecasting valuations, modernizing legacy real estate systems.
Strengths: Deep SAP ecosystem expertise combined with AI development capacities.
Limitations: Strong enterprise focus; may be less aligned with early-stage PropTech teams seeking rapid experimentation.
Conclusion
Operational demands in real estate are accelerating, and traditional workflows aren’t keeping pace. Many businesses are now facing challenges that can’t be solved by hiring more staff or adding another point solution. The companies in this list reflect a shift already underway: teams need partners who can reduce manual work, organize scattered data, and keep processes running even when volume spikes. The right choice isn’t about who sounds the most high-tech. It’s about who can reliably handle the parts of your operation that aren’t working today.
What this comparison shows is that there’s no single “best” vendor. Some AI solutions providers for real estate specialize in vision models, others in workflow automation, data infrastructure, or conversational assistants. The right fit depends on where your bottlenecks are, how your systems are structured, and what level of customization you require.
Use this list as a practical filter: narrow your options, understand what each company actually delivers, and focus on partners whose strengths match your priorities. The next step is reviewing case studies, asking direct technical questions, and running a small pilot to validate fit before committing resources.
