Defining the "AI credit OS"
Several AI-native vendors are converging on the same phrase: "AI operating system for commercial loans." Stripped of the marketing, the shorthand is a layer that reads every document and signal a credit team sees, reasons across them, and produces the artifacts the next person downstream needs. The reason multiple vendors are reaching for the framing at once is that the workflow is the prize, not any single bottleneck inside it.
An honest AI credit OS for a US commercial lender has to cover four phases. Origination and intake: capture the application, request the right documents from the borrower, and feed the credit team a complete file. Underwriting and analysis: spread the tax returns and statements, reconcile K-1s across guarantors, compute global cash flow, and draft a memo defensible to committee and a regulator. Booking and documentation: hand off to the LOS, closing docs, and core. Servicing and monitoring: track covenants, surface annual review triggers, and chase the documents that go stale after close, and on the unhappy path, work the delinquency and special-assets queue.
No vendor today shipping a single product covers all four phases at the depth a community bank needs. The useful question is which phase a given vendor anchors to and how much of the rest they can credibly own. Aloan anchors to underwriting and stretches forward into intake and back into post-booking monitoring. Proximitty's public surface anchors to servicing and collections, with broader "AI operating system" language stretching the marketing claim across the rest. Both framings are honest within their stage. The products underneath are not the same product.
A tale of two entry points
The cleanest way to compare two products positioned around the same category language is to put their actual entry points side by side. The closest sibling comparisons on this side of the market are Aloan vs Casca for the AI-native LOS-replacement framing, Aloan vs Lama AI for the agentic-LOS-infrastructure framing, and Aloan vs UPTIQ for the agentic-AI-across-wealth-and-lending framing.
| Area | Aloan | Proximitty |
|---|---|---|
| Lifecycle entry point | Document intake through cited credit memo | Booked-loan servicing and collections; document chasing on existing borrowers |
| Company stage | Shipped deployments at US community banks and credit unions; multiple exam cycles | Y Combinator W26, founded 2026, ~$500K reported seed; public surface is primarily a book-a-demo landing page |
| Primary buyer profile | US community banks, credit unions, regional banks, CUSOs, non-bank commercial lenders | Publicly markets to banks, credit unions, and fintechs servicing C&I, CRE, and SBA |
| Loan-type focus | C&I, CRE, SBA, ABL, equipment finance with multi-entity guarantor coverage | C&I, CRE, SBA per public materials |
| US tax return depth (1040, 1120, 1120-S, 1065, K-1, Schedule E) | Native end-to-end with K-1 reconciliation and multi-entity rollup | Not the publicly marketed wedge; positioned around servicing document chasing rather than underwriting tax-return analysis |
| Credit memo output | Drafted from the same workflow that built the spread; every figure linked to source page | Not described as a shipped product output in public materials |
| Covenant tracking | Post-booking covenant monitoring against the loan document, with trend analysis across reporting periods and annual review triggers | Listed in public materials as part of the servicing agent set; depth and reporting model not publicly described |
| Collections and special-assets workflow | Out of scope; the credit-file workflow does not extend into delinquency outreach or distressed-account servicing | Anchor use case: AI agents that request, chase, and ingest borrower documents and reconcile discrepancies on delinquent and at-risk loans |
| Examiner-grade auditability | Page-level source citations on every spread and memo number; override history attached to the credit file | Public materials describe AI agents at the platform level; page-level citation back to the source document is not the marketed audit story |
| Deployment posture | Two to four weeks alongside existing LOS, no data migration | No-code agent builder for servicing workflows; deployment timelines not publicly disclosed at the bank level |
| Coexistence with existing systems | Sits alongside nCino, Salesforce builds, Baker Hill, MeridianLink, LaserPro | Positioned as a servicing layer on top of existing systems rather than a full LOS replacement |
A bank cannot replace its underwriting workflow with a servicing-and-collections platform, and it cannot work a delinquency queue with a tax-return spreading tool. The two products sit at different stages of the same lifecycle, and the unit-of-work at each stage is different.
For loan origination and underwriting: the Aloan side
A US community-bank commercial credit file is rarely a single clean financial statement. The typical structure is a 1065 at the holding company, 1120-S returns at two operating subsidiaries, K-1s flowing to three guarantors, 1040s with Schedule E rentals at the personal level, bank statements covering deposit verification, and rent rolls on the CRE side. The IRS Form 1065 instructions spell out the K-1 mechanic: each partner receives their share of income, deductions, and credits, and the underwriter has to tie those K-1s into guarantor cash flow, ownership structure, and debt-service support across the whole borrowing group before a memo can land.
Aloan is built around that workflow. Document intake captures the file, the underwriting platform spreads the returns and statements, K-1s trace into personal cash flow, Schedule E rentals roll into global cash flow, and the bank's add-back policy applies consistently across files. The credit memo lands in the bank's format with every figure linked to its source page. That citation depth is what carries the file through an OCC, FDIC, or NCUA exam under SR 11-7 and the community-bank proportionality lens in OCC Bulletin 2025-26.
Proximitty's public materials do not describe a shipped origination-underwriting workflow at this depth. The product is described as AI agents that ingest documents, spread financials, and monitor covenants in the service of automating servicing and collections, a different unit-of-work than producing a defensible credit decision at committee. A bank that buys Proximitty for upstream underwriting on tax-return-driven multi-entity files is buying outside the wedge the public surface emphasizes.
For special assets and servicing: where Proximitty fits
Post-booking is its own problem. Once a loan books, the institution has to keep the file alive. Annual reviews need fresh financials. Covenants need tracking. Borrower contact information goes stale. Delinquencies surface and the special-assets team starts a different kind of conversation. The work is high volume and mostly document chasing, which is the kind of workflow that "AI agents that request, chase, ingest, and reconcile" maps to cleanly.
Proximitty's public framing is honest about this. The YC company blurb describes "autonomous business loan servicing." The LinkedIn launch posts talk about automating loan servicing and collections. The CEO came out of FinCrime and Growth at Taptap Send, and the founding team's background sits closer to operational risk and ops automation than to credit policy. A bank or fintech with a meaningful delinquency book, a servicing team that is spending most of its day chasing documents, or a collections workflow that is mostly templated outreach is the natural buyer.
That fit comes with the usual caveats for a YC W26 vendor. Public deployments are limited. Traction figures the company cites, including the "5 large banks and fintechs processing over $1B of delinquent loans in less than 3 weeks" line, are vendor self-claims rather than independently verified references. The product surface is mostly a book-a-demo page. A buyer evaluating Proximitty today is evaluating an early-stage design partnership, not a mature servicing-platform deployment.
Building a unified credit OS
The category language is real and the strategic question behind it is real. A community bank or credit union running commercial credit in 2026 has more AI-native tools available than at any prior point, and the procurement choice is no longer "do we add AI" but "where in the lifecycle do we add it first and how do the pieces fit together." A useful frame is to think about the credit OS as a stack with two active surfaces and a quieter middle: underwriting at the front, servicing at the back, and the LOS holding the booked file in between.
Aloan is the underwriting surface. The product is shipped, the audit trail has survived bank exam cycles, and the deployment posture is designed to coexist with whatever LOS the bank already runs. The AI-assisted underwriting playbook walks through how the depth work actually lives in a typical community-bank commercial file, and the community-bank AI underwriting shortlist covers the closest peer products against the same criteria. The overlay-vs-replacement argument explains why the LOS in the middle does not need to be the casualty of an AI buying decision.
Proximitty, in its public form today, is a candidate for the servicing surface. The shape of the product, the founding team's background, and the launch posts all point the same direction. A bank that already has its underwriting workflow under control and is feeling the pain of a servicing or special-assets queue could reasonably take a meeting with the Proximitty team to evaluate a design partnership.
The "AI operating system for commercial loans" headline is the same on both vendors. The lifecycle entry point is different, the buyer inside the institution is different, and the maturity profile is different. A senior credit officer evaluating Aloan is asking whether the cited memo will hold up at committee and on the next exam. A head of servicing evaluating Proximitty is asking whether AI agents can clear the document-chasing queue. Two real problems, neither solved by the same tool.
Who is Aloan for?
Aloan is the right fit when most of these conditions hold:
- The institution is a US community bank, credit union, CUSO, regional bank, or non-bank commercial lender. The product is shaped around US-tax-form depth and US bank-examiner expectations.
- The bottleneck is underwriting throughput. A five-person credit shop wants the output of a fifteen-person team without hiring.
- The book includes multi-entity borrowers. K-1 chains, Schedule E rentals, and ownership structures that have to consolidate without double counting.
- Examiner defensibility matters at the line item. The credit team wants page-level source citations on every number, not platform-level decision logs.
- The existing LOS is staying. A two-to-four-week deployment alongside nCino, Baker Hill, MeridianLink, a Salesforce build, or a homegrown stack matches the appetite for change.
Who is Proximitty for?
Proximitty looks like the right fit when most of these conditions hold:
- The pain is the back of the book. A servicing team or special-assets group buried in document chasing, borrower outreach, and reconciliation on already-booked loans.
- Delinquency volume justifies a dedicated tool. The institution has enough distressed loans to make autonomous collections workflows worth standing up.
- An early-stage design partnership is acceptable. The buyer is comfortable evaluating a YC W26 vendor with limited public deployments and self-claimed traction figures.
- Tax-return-driven multi-entity underwriting is already solved. The credit team already has a workflow for spreading, global cash flow, and cited memos, and is not relying on Proximitty to be the underwriting surface.
- No-code servicing agents map to the team structure. Operations and servicing leadership are willing to own configuration of the agent workflows.
Bottom line
Aloan and Proximitty share a category headline and almost nothing else operationally. Aloan owns the underwriting surface from document intake through the cited credit memo, with shipped deployments at US community banks running under bank-examiner expectations. Proximitty (YC W26) is one of the more credible new entrants pointing AI agents at the servicing and collections surface, with a founding team built for ops automation rather than credit policy. A bank that buys Aloan is fixing the front of the lifecycle. A bank that buys Proximitty is fixing the back. Either can be the right answer for the right institution; they are answers to different questions.
Frequently asked questions
What is the difference between Aloan and Proximitty?
Aloan is a US commercial underwriting platform built for community banks, credit unions, regional banks, CUSOs, and non-bank commercial lenders. It automates document intake, tax-return spreading, global cash flow, and source-cited credit memos before a deal goes to committee. Proximitty is a YC W26 company whose public surface centers on autonomous loan servicing and collections: AI agents that chase borrower documents, reconcile discrepancies, and automate workflows on already-booked loans. Both reach for the "AI operating system for commercial loans" framing, but each enters the lifecycle from a different end.
What stage is Proximitty at as a vendor?
Proximitty is a 2026-founded San Francisco company in the Y Combinator W26 batch with approximately $500K in reported seed funding. The CEO previously led FinCrime and Growth at Taptap Send and was at McKinsey; the CTO previously led security infrastructure at Bloomberg and was Head of Engineering at ACI.dev. As of mid-2026, the proximitty.ai public surface is primarily a book-a-demo landing page, and any traction figures the company cites are vendor self-claims rather than independently verified deployments.
Does Proximitty handle origination and underwriting?
Proximitty's YC company description and LinkedIn launch posts describe a product centered on loan servicing and collections: AI agents that ingest borrower documents, spread financials, monitor covenants, and service every borrower across C&I, CRE, and SBA. The homepage uses the broader "AI Operating System for Commercial Lending" framing. Public materials do not describe a shipped end-to-end origination underwriting workflow with examiner-grade source citations on a credit memo, which is the load-bearing artifact a US community bank credit team is buying for.
Where does Aloan fit alongside a servicing-and-collections platform?
Aloan runs upstream of servicing. It picks up at document intake, spreads 1040, 1120, 1120-S, and 1065 tax returns, traces K-1s into personal and global cash flow, applies the bank's add-back policy, and produces a credit memo where every figure cites the source page. Aloan also handles post-booking covenant monitoring and annual reviews. A bank with a meaningful special-assets or delinquency book could in principle run Aloan for origination-to-monitoring and a dedicated servicing platform for distressed accounts. The two address different parts of the lifecycle and the unit-of-work is different.
Is Proximitty a Casca, Lama AI, or UPTIQ-style competitor?
It is closer to a different category. Casca markets an AI-native LOS replacement weighted toward small business and SBA origination. Lama AI markets agentic LOS infrastructure plus a Lending Exchange. UPTIQ markets agentic AI across wealth and lending workflows. Proximitty's public emphasis on autonomous loan servicing, collections, and document chasing on already-booked loans puts it closer to a servicing-and-special-assets entrant than to those origination-side competitors.
When is Proximitty the better fit than Aloan?
Proximitty is the better fit when the bottleneck is the back of the book rather than the front: a bank or fintech with a delinquency problem, a special-assets queue that needs autonomous document chasing and borrower outreach, and a willingness to be an early-stage design partner with a YC W26 vendor. Aloan is the better fit when the bottleneck is the underwriting cycle itself: multi-entity tax-return files, K-1 reconciliation, cited credit memos, and shipped deployments at US community banks operating under bank-examiner expectations.
Can Aloan and Proximitty coexist?
Architecturally, yes. Aloan is built to coexist with the institution's existing LOS and adjacent tooling rather than replace anything end-to-end. A bank running Aloan upstream for origination, underwriting, and post-booking monitoring could in principle layer a servicing-and-collections specialist over the back of the lifecycle without conflict. The practical question is whether the bank wants two new AI vendors in the credit stack at once and whether the special-assets volume justifies a dedicated tool.