Lama AI

Agents run the whole chain from intake through spreading, underwriting and decisioning to closing and monitoring, turning an unstructured borrower package into a decision ready credit memo in about five minutes. Take them away and what is left is the legacy systems the company diagnoses precisely: static forms, rigid workflows and services heavy implementations.

Those cannot account for the thousands of borrower variations, document exceptions, policy nuances and edge cases that define small business lending. That diagnosis explains clearly why this work resisted automation until models could handle exceptions.

The containment principle is stated well and is the right one for this buyer: the platform operates within each bank's existing policies, credit standards, approval processes and compliance requirements, giving teams scale without replacing human judgment, and it deploys alongside incumbent systems rather than displacing the institution's controls. The described workflow runs through decisioning, approval and closing, and no threshold, referral rule or mandatory review point is published for where automation stops and a credit officer begins.

One accuracy figure is published: financial spreads are produced from source documents with 95 percent or better accuracy, configurable to each bank's templates and chart of accounts. No method, sample, period or definition of an error comes with it, and nothing is published for the credit memos and narratives the agents draft at dozens of regulated banks. The other published figures are speed rather than correctness. One of them is qualified, describing decisioning cut from weeks to minutes in targeted workflows rather than across the board, which is candid without being a measurement.

Banks are named and their executives are quoted by name. SouthState Bank's president of institutional banking, Chris Nichols, says it tested several small business origination platforms, chose Lama AI and has been served well for three years. Gate City Bank, a $3.6 billion institution, reports approvals ten times faster, and Colony Bank cut a 95 step manual process to 32.

Accordia Bank chose it after evaluating twelve vendors, and Luminate Bank, Capital Community Bank and First Bank are also named. The company states dozens of banks are live and billions in loan volume have been processed. A Series A in June 2026, led by EJF Ventures with Fin Capital participating, took total funding past $20 million, alongside revenue growing threefold year on year. IDC named it a Major Player in corporate loan origination.

Dozens of community and regional banks lending into overlapping local markets route their borrower packages and credit outcomes through one platform, and Lama AI publishes no boundary statement on whether models learn across that customer base or what an institution contributes by joining. Its embedded finance network also connects platforms to a wider set of lenders, so the company sees demand and performance across competing lenders.

Borrower packages, including financial statements and tax records, flow into the platform from dozens of institutions, and Lama AI publishes no data protection agreement, retention schedule, subprocessor list or deletion commitment for them. Its embedded finance arm extends that intake to end customers of third party platforms.

Lama AI states that it is SOC 2 Type 2 certified and compliant with the California Consumer Privacy Act and the California Privacy Rights Act, with 256 bit Advanced Encryption Standard encryption, role based access and single sign on. Its trust center, run on Sprinto, shows the SOC 2 certification and 31 documents, among them a letter of good standing, a certificate of insurance and its security policies, all behind an access request. No ISO certification, audit period or subprocessor list is published. A public platform status page is maintained.

Government guaranteed lending is named program by program: SBA 7(a), SBA 7(a) small, SBA 504, SBA Express and USDA loans each get their own workflow, and the platform connects to the SBA's E-Tran system for submission. The company also publishes a guide to Section 1071, the Consumer Financial Protection Bureau rule requiring small business lending data collection.

What is not published is how the platform tracks rule changes that decide whether a guarantee survives, or which compliance checks run automatically and which stay with the bank. No license or supervisor of its own is named, which fits a software provider whose bank customers carry the regulatory obligations.

Lama AI's agents spread financials, draft the credit memo and carry a commercial or SBA loan through underwriting and decisioning, so the AI shapes the credit decision even where a bank officer signs it. Lama AI publishes no disparate impact testing of its own models and no method for adverse action reasons, the evidence a fair lending review of an automated small business process starts from.

Its fairness material is a mission, fair and transparent capital for the businesses that power local communities, and a guide to the Section 1071 small business lending data rule its bank customers must follow. The platform is built to run inside each bank's credit policies and approval processes, and Gate City Bank's account of it is that the AI does not make the decision.

A small business applicant is not told whether agents assembled its credit assessment, how a misread financial statement is corrected, or what recourse follows a decline that an automated workflow produces in minutes, and Lama AI publishes no guarantee, indemnity or correction process. The applicant deals with the bank, whose credit standards and approval process the agents work within.

Its agents run on no named base model, model provider or hosting arrangement. The data chain is named in full: bureau and verification data from Experian, Equifax, Middesk and Alloy, bank data from Plaid, tax data from the Internal Revenue Service (IRS) and TaxGuard, property data from Cotality and industry research from IBISWorld, so a bank can map every source its credit file depends on.

Integration gives the platform its reach. It exposes an application programming interface with more than 100 prebuilt connections, grouped by kind: core banking (Fiserv, FIS, Finastra), customer relationship management (Salesforce, where it is listed on Financial Services Cloud and Experience Cloud), data and verification (Experian, Equifax, Plaid, Middesk, Alloy, Cotality, IBISWorld), and government and closing (the Internal Revenue Service (IRS), the SBA's E-Tran system, DocuSign).

Distribution partnerships extend it further, through the Mastercard Engage program and the Bridge commercial loan marketplace. The platform deploys alongside a bank's existing origination and core systems rather than replacing them, and a bank can go live in as little as three weeks on standard templates.

For a platform serving United States banks, Lama AI publishes no hosting provider, region selection, residency commitment or private deployment option. It also runs a research center outside the United States.

Lama AI publishes no pricing, packaging or basis of charge, including the cost per origination that would show whether the economics it describes improve for a bank. Its central argument is that underwriting a small loan costs as much as a large one, and its agents are sold as the way to close that gap.

Product coverage is broad within commercial lending. It spans small business and government guaranteed loans alongside commercial and industrial, commercial real estate, construction and specialty products. A separate embedded finance arm lets platforms launch credit products against a bank network. Buyers are community and regional banks nationwide. Coverage is a single country and a single institution class, with no international or large bank presence evidenced.