Institutional CRE debt intelligence, produced at a new velocity.
$2T+ in CRE debt, normalized across private (LifeCo) and public (CMBS, HUD, Freddie, CRE CLO) debt layers. Entity resolution, capital-stack triangulation, and capital graph assembly connecting allocators, lenders, sponsors, and properties. Agent-native via MCP.
Built solo in 90 days using modern agentic tooling, by an operator with 20+ years in institutional CRE.
The CRE credit intelligence layer has been structurally missing.
Not for lack of data. The filings are public.
For lack of economics. Assembling institutional CRE credit intelligence from primary regulatory sources required labor no incumbent would fund at scale. MSCI worked from transactions. Trepp from consortiums. Preqin from fund reports. Each captured one layer. None captured the whole capital stack — because the old build model couldn’t justify it.
The toolchain changed.
Modern agentic tooling compressed what was a multi-year, multi-million-dollar platform build into something a single domain operator can produce in a quarter. Not every operator. Domain expertise still matters, and twenty years of it is what makes the extraction logic possible. But the economics flipped.
Alpine Systems is the first production instance of that shift, applied to CRE private credit.
Three debt layers. One normalized corpus.
Incumbents work one layer at a time. Alpine normalizes them into a single queryable intelligence layer, then triangulates sponsors across the stack. Property address is the exhaust data — not the organizing principle.
mREIT
Mortgage REITs aren’t required to disclose loan-by-loan — they report group-level Schedule IV debt buckets. Alpine normalizes both: the group buckets across the tracked public mREIT universe, and row-level loan detail where issuers voluntarily publish tapes. This is the opaque layer incumbents can’t see through.
LifeCo
Insurance company mortgage loans from Schedule B statutory filings. Loan-level, dedup’d across the 5-year rolling window with origination dates, rate detail and more. Complete first-mortgage CRE debt held on LifeCo general-account balance sheets.
CMBS · HUD FHA · Freddie
CMBS ABS-EE (EDGAR), HUD FHA active book, Freddie Mac MLPD, and CRE CLO disclosures. Loan-level public debt cross-referenced against the private and mREIT layers so sponsor exposure is visible across first-mortgage, mezzanine, and securitized tranches.
One first mortgage per deal. One sponsor across many deals. The capital family becomes visible only when the layers are normalized together.
Each deal has a single first-mortgage lender — a LifeCo, a CMBS trust, a Freddie K-deal, or a bank — often paired with a mezzanine tranche from an mREIT or debt fund. Alpine normalizes every layer and resolves the sponsor across the full book of financings. Capital-family dependency, cross-lender concentration, and refinancing clustering are visible at the sponsor level — not on any single stack.
A single intelligence layer across private CRE credit.
Primary-source extraction, cross-regime normalization, confidence-scored entity resolution, and capital graph assembly — delivered as agent-native infrastructure, not a dashboard.
Normalized Debt Coverage
$2T+ in institutional CRE credit across private (LifeCo) and public (CMBS, HUD FHA, Freddie MLPD, CRE CLO) debt layers. 130K+ loans. Extracted from primary regulatory filings and normalized into a single queryable schema.
Confidence-Scored Entity Resolution
Borrower LLCs resolved to operating entities, sponsors, allocators, and underlying capital. Confidence tier assigned at every link — not asserted, scored.
Capital Graph Assembly
150K+ nodes. 250K+ edges. Allocators, lenders, sponsors, borrowers, and properties connected into a single graph. Cross-lender exposure and capital-family dependency visible in one query.
Decision-Ready Signals
Rate pressure, maturity timing, concentration risk. Scored per loan on a continuous cadence. Delivered via REST, webhooks, and agent-native MCP.
Built in 90 days. Solo. Live now.
Alpine Systems was built by one operator using modern agentic tooling — the same class of tooling institutional data platforms are now scrambling to adopt.
Twenty years of domain expertise made the extraction logic possible. The tooling made the timeline possible.
What you see on this site is the result: a production intelligence layer across $2T+ in CRE debt spanning private and public layers, entity-resolved, capital-stack triangulated, graph-assembled, confidence-tagged, and delivered through REST and MCP interfaces — built in a timeframe traditional platform teams cannot match.
And the same tooling that built it runs it. Extraction and normalization operate autonomously against every new filing; human-in-the-loop review gates anything that crosses a confidence boundary. The 90-day build is not the end of the story — it’s the reason the platform can stay current without a 50-person operations team behind it.
Query-based. Rate-limited. API-first.
RESTful JSON API with cursor pagination, per-tier rate limits, and structured query parameters. Designed for direct integration into internal systems, models, and research workflows.
GET /v1/loans?state=NY&rate_pressure=SEVERE&limit=25
{
"data": [
{
"loan_number": "loan_demo_01842",
"lender": "Northbridge Life",
"city": "New York",
"state": "NY",
"book_value": 45000000,
"interest_rate": 3.15,
"maturity_date": "2027-09-15",
"maturity_timing": "CRITICAL",
"rate_pressure": "SEVERE",
"rate_pressure_score": 285,
"composite_market_factor": 87
}
],
"pagination": {
"cursor": "eyJpZCI6MTI0N30",
"has_more": true,
"total": 1247
},
"rate_limit": {
"remaining": 59,
"reset": "2026-04-18T12:01:00Z"
}
}One query. Immediate signal.
Every loan record is pre-scored for maturity timing, market conditions, and rate exposure, so teams can build directly on top of the dataset.
- 25+ fields per loan record
- Proprietary signal taxonomy across rate environment, maturity timing, and concentration risk
- Multi-year analytics across refinancing, amortization, and disposition context
- Cursor pagination, rate limiting, and usage controls
- Full OpenAPI 3.1 specification included
The agents run. Humans supervise.
The same agentic tooling that compressed the build from years into a quarter is what keeps the platform current. Extraction, normalization, and signal generation run autonomously on every new filing. Human judgment gates the decisions that require it — confidence boundaries, new entity resolutions, source schema changes. The machine does the volume. The operator adjudicates the edges.
Built for institutional standards
All platform components are deployed on independently audited US-hosted infrastructure. No offshore processing. No third-party data brokers.
Providers maintain SOC 2 Type II attestations. Data is protected with AES-256 encryption at rest and TLS 1.2+ in transit.
All data is sourced from public filings and regulatory records. No purchased consumer data. No opaque third-party sourcing.
Per-key API authentication, tier-based rate limiting, request logging, audit trails, and tenant-isolated architecture.
Security diligence materials and questionnaire responses are available on request.
Two paths in.
The platform is live. Access is credentialed and scoped per engagement.
Design Partner AccessFor LifeCo risk, reinsurance, distressed credit, and institutional LP advisory workflows. Scoped API and MCP access under custom commercial terms.
access@thealpinesystem.com
Strategic Discussions & PartnershipsFor platform partnerships, embedded deployments, exclusive field-of-use engagements, and acquisition conversations.
partnerships@thealpinesystem.com