AI due diligence for tokenized assets
Before an asset is tokenized on PazaLabs, the AskIsta GenAI-RAG engine reads its documents. It ingests loan tapes, title deeds, appraisals and legal agreements, flags missing or inconsistent terms, scores the asset, classifies it against regulatory frameworks and keeps monitoring it after issuance.
The AI intelligence layer
PazaLabs' Retrieval-Augmented Generation (RAG) engine is embedded throughout the platform to automate asset due diligence, improve asset risk assessment, streamline compliance and enhance operational efficiency. Rather than replacing human oversight, AI augments institutional decision-making with speed (months to hours), consistency and actionable insights.
What the engine does
- 1
Document Ingestion
Loan tapes, title deeds, appraisals, and legal agreements ingested in bulk across PDF, XML, and structured formats.
- 2
Semantic Validation
Retrieval-augmented search flags missing fields, inconsistent terms, and regulatory red flags in real time.
- 3
Risk Scoring
Each asset and pool receives a structured score across credit, legal, operational, and market dimensions.
- 4
Compliance Classification
Assets classified against SEC Reg D, ESMA, MiCA, and other frameworks with a logged rationale.
- 5
Performance Monitoring
Monitors pool performance, flags covenant breaches, and notifies parties automatically.
Where it runs in each asset class
- Mortgage pools. GenAI-RAG validates documents and scores the pool.
- Hard assets. Independent valuation, title verification, and insurance docs validated by GenAI-RAG.
- Receivables. GenAI-RAG validates docs, credit signals, and concentration limits before pool entry.
Ready to tokenize your assets?
Book a session with our structuring team and we'll scope the right asset class and legal wrapper for your use case.