Custom AI for Real Estate - LLM Agents, RAG & Valuation Models
AI lead qualification, listing generation, tenant support, valuation models, fraud detection, and investor reporting built specifically for real estate operators. Every agent, model, and prompt is owned by you forever, deployed to your cloud, with no platform tax or vendor lock-in. From RAG-powered chatbots to multi-agent orchestration, the AI layer connects directly to your CRM, inventory, and operations data.
100+ operators · 14 countries · A decade building real estate technology
100% prompt and model ownership
Deployed to your cloud
SOC2-ready audit logging
What does a real estate AI agency do?
We build production-grade AI for real estate operators and PropTech founders: lead qualification agents, RAG chatbots over your listings and documents, listing-copy generation, AVM and valuation models, fraud detection, and investor copilots. Every build is owned by you and deployed inside your own cloud - no platform tax, no data leaving your tenant.
Trusted by 50+ operators, PropTech companies & digital-first brands
Harrington Housing
Hive Coliv
Edge Living
CDA Coliving
Fllat
Volley
TheVibes
CasaPay
JumboTiger
Everything Coliving
Bookmycoliving
Bhutani
Gulshan- Harrington Housing
- Hive Coliv
- Edge Living
- CDA Coliving
- Fllat
- Volley
- TheVibes
- CasaPay
- JumboTiger
- Everything Coliving
- Bookmycoliving
- Bhutani
- Gulshan
CRC
M3M
Godrej
Omaxe
Sikka
Lodha
Mahagun
Prestige
Sawasdee- CRC
- M3M
- Godrej
- Omaxe
- Sikka
- Lodha
- Mahagun
- Prestige
- Sawasdee
Six AI capabilities built for real estate
Every agent and model is trained on real estate data, integrated with property-specific systems, and deployed with compliance and cost guardrails from day one.
AI lead qualification agents
Custom agents that qualify, route, and book site-visit appointments via WhatsApp, email, and your CRM. Trained on your pipeline and ideal client profile. Runs 24/7 across time zones at a fraction of the cost of manual qualification.
RAG-powered tenant and buyer chat
Retrieval-augmented chatbots grounded in your inventory, FAQs, and policies. Multi-language out of the box. Typical deployments achieve 65% deflection on tier-2 support queries, freeing the team to focus on high-value interactions.
AI listing generation and classification
Listing copy, photo tagging, and amenity extraction from raw operator data. Fair Housing-aware copy generation that refuses protected-class language and flags drafts that drift. Saves 4 to 8 hours per listing across high-volume portfolios.
AI valuation and comparables
Custom AVM models and API integrations for pricing, IRR underwriting, and portfolio rebalancing. Built for developers, lenders, and institutional investors who need valuation logic tailored to their asset class and market.
Fraud and risk detection
ML classifiers for booking fraud, payment fraud, and identity verification. Tuned on your historical data with continuous retraining as new patterns emerge. Designed for property managers and marketplace operators handling high transaction volumes.
AI owner and investor reporting
Auto-generated monthly reports and investor copilots that answer portfolio questions in natural language. Connects directly to your financial data, occupancy metrics, and asset performance dashboards.
An AI solution for every stage of your real estate operation
- 01
Exploring AI for the first time
The opportunity is clear but the starting point is not. A discovery sprint maps the highest-value AI use cases against your funnel and operating costs, then identifies the single agent worth shipping first.
- 02
Shipping a single AI agent
One use case, production-ready in 6 to 8 weeks. A lead qualification bot, a RAG chatbot, or a listing generator connected to one data source, with eval suite and cost guardrails included.
- 03
Building a multi-agent platform
Two to three use cases running together. Multi-agent orchestration across your CRM, CMS, and inventory systems. Observability dashboard and quarterly accuracy reviews keep the system sharp.
- 04
Deploying custom models
Off-the-shelf models do not fit the use case. Custom fine-tuned models, production-grade evaluation pipelines, and end-to-end ops and finance copilots built as a dedicated AI engineering engagement.
- 05
Scaling across markets
An existing AI deployment expanding into new languages, geographies, or regulatory environments. Multi-language support, data residency configuration, and localized compliance rules added to the running system.
- 06
Replacing manual processes at scale
Lead qualification, listing creation, tenant support, and investor reporting are consuming headcount that could be deployed elsewhere. AI agents take over high-volume, repetitive workflows while the team focuses on relationships and strategy.
Not sure which stage fits your operation? Book a strategy call and the team will identify exactly where to start.
Built for real estate operators. Exclusively
Coliving and BTR operators
Handling multi-language tenant inquiries 24/7, automating maintenance triage, and generating owner reports. AI agents that understand the coliving operating model and tenant lifecycle from move-in to renewal.
Real estate developers
Automating channel-partner lead qualification, project inquiry routing, and sales team scheduling. Agents that qualify, score, and route leads based on project-specific criteria and buyer intent signals.
Property managers
Running AI maintenance triage, vendor dispatch recommendations, and automated owner reporting. Systems that reduce response times and free the operations team from repetitive ticket handling.
Marketplace and portal founders
Shipping AI-powered search, listing generation, and content classification. Natural language property search, automated listing descriptions, and photo tagging that scales with inventory volume.
Investment platforms
Screening deals with AI underwriting copilots, automating investor Q&A, and generating portfolio performance summaries. Agents trained on your fund structure and reporting cadence.
Brokerages
Automating listing copy, photo classification, CRM data hygiene, and lead follow-up sequences. AI that handles the administrative load so agents can focus on client relationships and closings.
Compliance, privacy, and safety from day one
Fair Housing-aware copy generation. AI listing tools refuse to write protected-class language and flag drafts that drift toward non-compliant phrasing.
GDPR, India DPDP, and UAE PDPL data handling with EU, India, and UAE data residency where required.
Deployment to your cloud (AWS, GCP, Azure) under your data-processing agreements. No third-party data exfiltration.
PII redaction before any prompt leaves your tenant environment.
SOC2-ready audit logging on every prompt, completion, and tool call.
Bias evaluations on every fine-tune with human-in-the-loop review for any user-facing automation.
A complete AI system built to scale
AI agents trained on your data
Not generic chatbots with a real estate skin. Every agent is trained on your pipeline, inventory, policies, and operating model. The responses reflect how your business actually works.
Production-grade eval suite
Accuracy benchmarks, regression tests, and A/B frameworks that run before any model change reaches production traffic. Measured performance, not guesswork.
Observability and cost guardrails
Per-tenant and per-route cost tracking, latency monitoring, and token usage dashboards. The system stays within budget as usage scales.
Multi-model portability
Agents are built model-portable. Swap between OpenAI, Anthropic, Google, or open-weight models based on cost, performance, or data residency requirements without rebuilding the system.
Full IP transfer
Prompts, eval suites, model contracts, deployment scripts, and documentation transfer to your team at handover. No licensing fees, no revenue share, no vendor dependency.
A six-phase build. Structured. Transparent. Fast
- Phase 01
Discovery (week 1)
Map the highest-value AI use cases against your funnel and operating costs. Identify the single agent worth shipping first. Define success metrics before any build begins.
- Phase 02
Data audit (week 2)
Audit data quality, integration surface, and privacy constraints. Gate the work where data is not ready. No point building an agent on unreliable inputs.
- Phase 03
Prototype (weeks 3 to 6)
Ship a working prototype on a sample of your data. Measured against a pre-agreed accuracy bar. Real outputs on real data, not a demo environment.
- Phase 04
Production build (weeks 7 to 12)
Integrate with your CRM, CMS, payment, and inventory systems. Add observability, cost guardrails, and compliance logging. Weekly demos throughout.
- Phase 05
Eval and tuning (weeks 13 to 14)
Full eval suite, A/B framework, and human-in-the-loop tuning before flipping production traffic. The agent does not go live until it meets the accuracy bar agreed in discovery.
- Phase 06
Operate (ongoing)
Retainer-backed monitoring, prompt updates, model swaps, and quarterly accuracy reviews. The AI layer continues improving after launch.
What operators saw after launch
- 01
40%
More qualified leads booked without growing the SDR team (coliving operator, WhatsApp lead-qual agent).
- 02
65%
Deflection rate on tier-2 support queries (multi-country developer, 4-language RAG chatbot).
- 03
4 to 8 hrs
Saved per listing with AI generation and photo classification.
- 04
6 to 8 wks
From discovery to working prototype on real data.
Every brief is scoped to the operator's goals
Find the engagement that fits your stage and budget. Scope, milestones, and timeline are shared within five business days of your first call.
AI prototype
One use case. Production-ready.
6 to 8 weeks
- Single agent or RAG chatbot
- Connected to one data source
- Eval suite and cost guardrails
- Two-week post-launch support
AI platform
Two to three use cases. Multi-agent.
10 to 14 weeks
- Multi-agent orchestration
- RAG over CMS, CRM, and inventory
- Observability dashboard
- Quarterly accuracy review and retainer
AI operating system
Full platform. Custom models.
16+ weeks
- Custom fine-tuned models
- Production-grade evals and monitoring
- End-to-end ops and finance copilots
- Dedicated AI engineering pod with on-call SLA
The stack shipped in every engagement
Foundation models
- OpenAI GPT-4o
- Anthropic Claude
- Google Gemini
- Open-weight (Llama, Mistral)
RAG and embeddings
- pgvector + Postgres
- Pinecone / Weaviate
- OpenAI / Voyage embeddings
Agents and orchestration
- LangGraph
- Vercel AI SDK
- OpenAI Assistants API
- Custom state machines
Observability and evals
- LangSmith
- Helicone
- Langfuse
- Custom eval harnesses
Cloud and DevOps
- AWS / GCP / Azure
- Terraform + Kubernetes
Compliance
- SOC2-ready
- GDPR / CCPA
- India DPDP
- UAE PDPL
Is your data infrastructure ready for AI?
AI is only as good as the data and systems behind it. Before building AI features, find out if your technology foundations can support machine learning, automation and intelligent workflows.
No login · Free tool · Results in five minutes
Services most operators pair with custom AI
AI rarely ships in isolation. Most operators combine an AI build with one or more of these services for a complete platform.
Frequently asked questions
Which models do you build on?
OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini), and open-weight models (Llama, Mistral) where data residency demands it. Model selection happens per use case rather than locking to one vendor. Every agent is built model-portable.
Do we own the prompts and the model?
Yes. Prompts, eval suites, fine-tuned model weights (where applicable), deployment scripts, and documentation transfer fully at handover. No licensing fees, no revenue share, no vendor lock-in.
How fast can you ship a prototype?
6 to 8 weeks from discovery to a working prototype on your data, measured against a pre-agreed accuracy bar. Production deployment follows in the subsequent 4 to 8 weeks depending on integration complexity.
What if our data is not ready?
The data audit phase (week 2) identifies quality gaps, missing integrations, and privacy constraints before any build begins. If data is not ready, the engagement is gated until the foundation is solid. No point building an agent on unreliable inputs.
How do you control costs?
Per-tenant and per-route cost tracking is built into every deployment. Token usage dashboards, latency monitoring, and budget alerts ensure the system stays within agreed thresholds as usage scales.
How do you handle compliance and privacy?
PII redaction runs before any prompt leaves the tenant environment. SOC2-ready audit logging captures every prompt, completion, and tool call. Data residency is configured per jurisdiction across GDPR, India DPDP, and UAE PDPL requirements.
How do you handle Fair Housing risk in AI listing copy?
AI listing tools are configured to refuse protected-class language and flag drafts that approach non-compliant phrasing. Bias evaluations run on every fine-tune, and human-in-the-loop review is required for any user-facing content automation.
Ready to scope your AI build?
Share your use case, data sources, and timeline. The team comes back with a scoped proposal within five business days covering architecture, compliance, timeline, and investment. No commitment, no generic packages, just a proposal shaped around your operation.
No slides. No sales pitch. Just a focused strategy call.
Custom AI for real estate · Reply within 24 hours