Proximitty

Agents do the work from end to end, and the alternative is the manual servicing team the company exists to shrink. They autonomously monitor portfolios, chase and ingest borrower documents, and parse formats down to blurry scans and handwritten notes. They reconcile discrepancies with borrowers directly, spread financials to each institution's own conventions and draft credit memos. Learning an individual lender's edge cases and assumptions rather than applying a fixed template is model work, and none of the lifecycle survives without it.

The company answers the oversight question explicitly rather than leaving it implied. Reliability rests on a governance layer where every agent action is observed, logged and reviewable, with human review configurable at any step rather than at points the vendor chose. Agents run under scoped tools, approved templates and policy bound parameters, so the boundary is enforced by what an agent can reach rather than by instruction.

The whole is auditable specifically for model risk management functions and examiners. Naming the examiner as the audience is the tell that this was designed for a supervised institution rather than retrofitted.

The company builds for model risk review deliberately. It describes outputs as audit ready, agents as operating under approved templates and policy bound parameters, and the governance layer as supporting model risk management and examiner review. That anticipates the validation a bank performs rather than resisting it. Spreading to the institution's own business logic keeps the analytical convention under the lender's control. What is absent is measurement.

No extraction accuracy, spreading error rate or covenant detection rate is published, and for a product parsing handwritten notes and blurry scans into credit memos, extraction accuracy is the number that matters most.

For a company founded in November 2025 with two employees, the traction described is substantial. Five bank and fintech customers have more than $1 billion in delinquent loans on the platform, adoption is claimed among publicly traded fintechs and banks, and the company has a presentation slot at a major industry conference. The outcome claim is specific and well constructed.

One fintech reduced servicing staff from fifteen to two and redeployed the team to close $20 million of new originations in a single quarter, which frames the saving as capacity released rather than headcount cut. Founders bring relevant background in fintech operations and financial data infrastructure. No customer is named, funding is around $1 million, and an independent reviewer notes the traction claim needs validating.

The product's central promise makes the data boundary question pointed, and no boundary statement is published. Agents learn the servicing rules, assumptions and edge cases specific to each institution. That is exactly the encoded institutional knowledge a lender would least want generalized to a competitor, and the platform serves banks, credit unions and fintechs lending into overlapping markets. Nothing states whether captured rules remain tenant specific, who owns them, or what happens to them when a customer leaves.

The material handled is among the most sensitive in commercial banking: borrower tax returns, financial statements, rent rolls and debt schedules, frequently for privately held businesses whose owners are personally identifiable in them. Agents also contact borrowers directly to obtain more. None of its handling is described, and no data protection agreement, retention schedule, subprocessor list or deletion commitment is published.

There is no public attestation, certification, trust center or enumerated framework. The company is months old with two employees, so the absence is expected rather than negligent. It is also the most immediate obstacle to the bank segment it targets, since core system integration triggers the most demanding supplier assessment any vendor faces.

Regulatory awareness is embedded in how the product is described rather than claimed generically. Deposit insured banks are named as the buyer, government guaranteed small business lending is supported as a distinct loan class with its own programme rules, and the governance layer is stated to support model risk management and examiner review, which is the framework United States banking supervisors apply to any model influencing credit decisions. No supervisor, statute or guidance is named directly, and servicing and collections carry consumer and commercial contact rules that go unaddressed.

Borrowers here are businesses rather than consumers, which lowers the fair lending exposure without removing it, and the sharper question is treatment during distress. The platform performs early warning, covenant breach escalation and recoveries, so its judgements determine which struggling businesses get chased, restructured or pursued, and automated consistency in that work is double edged: it removes the arbitrariness of which officer noticed, and it also removes the discretion an officer might exercise for a borrower having a bad quarter. No analysis of outcomes across borrower types and no escalation policy is published.

No guarantee, indemnity or correction process is published. The lender is well served by comprehensive logging and configurable review, which gives it an audit trail to reconstruct any agent action. The borrower is the party contacted by agents, asked for documents, assessed against covenants and escalated on breach. Nothing describes whether they know they are dealing with an automated system, how a misread financial statement is corrected, or what happens when an early warning flag is wrong.

Downstream systems are enumerated in detail, so a buyer knows exactly what the platform connects to while knowing nothing about what powers it. No model provider, version or hosting arrangement is named, and there is no public subprocessor list. That is an odd asymmetry for a product whose governance pitch rests on examiner grade auditability, since model identity and change control are part of what an examiner reviews.

The integration disclosure is specific. Ten core banking systems are named individually across the major providers: two platforms each from three of the largest vendors, plus four others including an international core. Three loan origination and servicing platforms are named alongside them, and most integrations are stated to go live in under two weeks.

Naming particular core versions rather than claiming broad compatibility is a signal only a vendor that has actually connected to them can produce. It directly addresses why servicing automation usually stalls, since the loan data lives in systems that are decades old and mutually incompatible.

Proximitty names no hosting provider, region selection, residency commitment or private deployment option. Exposure is domestic, which narrows the question. Deposit insured institutions connecting a platform directly into their core banking system will require the processing arrangement documented as part of third party risk review regardless.

Proximitty publishes no pricing, packaging or basis of charge. The return case is stated concretely, through the staffing reduction and redeployment figure and through integration timelines of under two weeks, so a buyer can estimate benefit without knowing cost. Nothing indicates whether charge falls per loan serviced, per portfolio, per seat or as a platform fee.

Three buyer types are addressed: banks including deposit insured institutions, credit unions and fintech lenders. Loan coverage spans commercial and industrial, commercial real estate and government guaranteed small business lending, which differ in documentation, covenant structure and regulatory treatment. Lifecycle coverage is complete from document collection through spreading, covenant monitoring and early warning to recoveries. Scope is domestic and deliberately confined to servicing rather than origination or credit decisioning.