Haibrid provides a decision‑infrastructure platform that digitizes a private credit firm’s proprietary underwriting logic into a private, portable AI model, preserving confidentiality by never training on client data.
Funding
Funding not disclosed
Founders
Product
Problem
Private credit teams rely on manual, spreadsheet‑based processes to capture complex underwriting criteria, leading to inconsistent decision‑making, difficulty scaling across deals, and challenges maintaining audit‑ready documentation of investment‑committee (IC) approvals.
Solution
Haibrid offers a decision‑infrastructure platform that digitizes a firm’s proprietary underwriting logic into a private, portable AI model. The system learns each firm’s leverage thresholds, downside‑case rules, and IC approval drivers without ever training on the client’s raw data, ensuring confidentiality. It automatically ingests deal documents from VDRs or CIMs and applies the learned logic throughout the credit lifecycle, from initial screening through monitoring and refinancing. The resulting asset provides audit‑ready, mandate‑aligned judgments that can be consistently applied to every deal, memo, and covenant, preserving institutional discipline while accelerating conviction.
Target Audience
Primary customers are private credit investment teams and institutional credit analysts who need to codify and enforce proprietary underwriting policies across their deal pipelines.
Features
- AI model that captures firm‑specific underwriting thresholds, risk trade‑offs, and IC approval criteria
- Secure ingestion of deal documents directly from VDR/CIM sources for automated analysis
- Persistent house view that carries institutional preferences from screening through ongoing monitoring and refinancings
- Private, portable decision asset that never trains on client data and remains audit‑ready and model‑agnostic
- End‑to‑end workflow coverage, enabling consistent mandate‑aligned decisions across all stages of a private credit transaction