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Due diligence document review
Extracts, classifies, and surfaces critical information from property and legal documents using specialized OCR and vector search.
Agentic AI in real estate
Due diligence documents, read and cross-referenced automatically .
We build agents that extract, classify, and surface critical information from property and legal documents — the same staged retrieval-and-validation pattern that eliminated hallucinations in Schmitt-Thompson Clinical Content's clinical-guideline RAG system.
Trusted by operators
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+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
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37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
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AI agent implementation for a global automotive enterprise.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
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Why agentic AI
Due diligence is a document-reading bottleneck, not a judgment bottleneck
Reviewing property and legal documents for the details that matter (encumbrances, zoning constraints, title issues) takes time because someone has to read every page. An agent extracts and classifies that content, cross-references it against the rest of the file, and surfaces the specific passages a reviewer needs to make the judgment call.
every extracted fact traces to its source document
Use cases
Where agents earn trust in real estate operations
Document-heavy workflows with a clear reviewer sign-off.
02
Lease and contract review
Reads lease terms and flags clauses that need attention, with reasoning attached for the reviewing attorney or analyst.
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Portfolio document search
Answers specific questions across a large set of unstructured filings — sourced to the actual document, not a generalized summary.
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Property document classification
Automatically sorts and tags incoming documents by type, routing each to the right workflow without manual triage.
What the mechanism delivers
Document-grounded reasoning, measured in production
The first two figures are ours from real estate: Mapline.AI, where due diligence on a property ran for weeks before we built a document agent on advanced RAG, specialized OCR and vector search. The third is ARIJ Network, media, and it is here because it measures the thing every title and lease workflow turns on — retrieval that answers only from the client's own documents. Every figure links to the case study behind it.
Agents do not price a property, sign a lease, or clear a title. They read the documents, tie every extracted fact back to the page it came from, and draft what sits before a broker's or attorney's decision — with each step logged for review.
Per-property due diligence, before the agent
weeks
Analysts read title documents, surveys and planning records by hand for every property under consideration.
$2k–3k
Saved per due diligence project
Not a projection — the published per-project saving from the Mapline.AI engagement.
1% → 100%
Knowledge-inquiry response rate before and after
Not a real-estate deployment — A bilingual English/Arabic autonomous agent embedded in ARIJ's Moodle environment, answering only from ARIJ's own knowledge base — across 22 countries.
Client results
Proof from production agentic deployments
Our real-estate work is Mapline.AI (due diligence from weeks to minutes, $2k–3k saved per project), and Guesthook, where LLMs generate property descriptions for a vacation-rental marketing agency. The studies below are the ones published end to end with the full mechanism written up: the same grounded retrieval, multi-tool orchestration and multi-step validation.
Delivery path
From workflow audit to a production agent
TriStorm keeps document accuracy and engineering aligned — extraction and review questions surfaced before full build commitment.
Map document types and workflow
We audit target document sets, review requirements, and data access — ranking automation candidates by volume and impact.
- Document & workflow audit
- Data access map
- Prioritised use case
Build and validate the agent
We implement against real property and legal document shapes, with an evaluation suite scored before any output reaches a reviewer.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring
Production rollout with monitoring and audit logging, plus a structured handoff so your team runs the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own listing and transaction-document workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
FAQ
Agentic AI in real estate, answered
Where does agentic AI fit into real estate operations?
Due diligence is the clearest case: reviewing property and legal documents for critical information is a retrieval-and-extraction problem, which is what an agent grounded in your own document set handles well. We have not yet shipped a real-estate-specific deployment; the closest published proof is Schmitt-Thompson Clinical Content's agentic RAG system, validated to zero hallucinations across 329+ nurse-reviewed scenarios — the same staged retrieval-and-validation mechanism due diligence review needs.
How is this different from OCR software we already use?
OCR extracts text. An agent reads the extracted text, classifies it, cross-references it against other documents, and surfaces the specific information a reviewer needs — the reasoning layer on top of extraction, not the extraction itself.
Do you have real-estate-specific proof, or is this adapted from other industries?
Real-estate-specific. For Mapline.AI we built a due diligence agent on advanced RAG, specialized OCR and Milvus vector search: analysis that took weeks now runs in minutes, at a published saving of $2k–3k per project. For Guesthook, a vacation-rental marketing agency, we automated property description generation with LLMs. Where we reach outside the industry is for the validation mechanism — Schmitt-Thompson Clinical Content's agentic RAG system hit zero hallucinations across 329+ nurse-reviewed scenarios, and retrieve-cross-reference-validate is the same discipline a title or lease review needs.
What other real estate workflows suit an agent?
Lease and contract review, property document classification, and portfolio-level document search across large sets of unstructured filings.
Can this integrate with our existing document management system?
Yes, through your existing APIs and storage — no need to migrate your document archive to a new system.
Do we own the system after it is built?
Yes. Full ownership of agent logic and integrations — no proprietary runtime lock-in.
Start with one workflow
Map one document-review workflow worth automating
A 30-minute call identifies the document types, review requirements, and a realistic path to a working agent.