Answers about AI underwriting, credit intelligence, and lender workflow automation.

Kaaj is an agentic AI credit intelligence platform for small business lenders. It ingests borrower documents, organizes loan packages, verifies businesses, analyzes bank statements, detects risk signals, and generates decision-ready credit memos tailored to a lender's credit policy. Kaaj is designed to reduce manual pre-underwriting and analysis work while keeping credit teams in control of the final decision.

Kaaj is built for equipment finance lenders, small business lenders, banks, credit unions, private credit funds, fintech lenders, MCA funders, and brokers. It is used by credit teams, underwriting teams, operations teams, fraud/risk teams, and executives who need to process more deals without scaling manual review linearly.

Kaaj solves the manual work between 'application received' and 'credit-ready decision.' Lenders often spend significant time collecting documents, checking business legitimacy, reviewing bank statements, detecting fraud signals, applying credit policy, and writing memos before a decision can be made. Kaaj automates and structures that work so teams can move faster with better consistency and traceability.

Kaaj is not a traditional loan origination system. Kaaj acts as an intelligence layer that sits on top of or alongside your existing LOS, CRM, inbox, portal, file storage, and workflow tools. It helps prepare, analyze, and document deals, then pushes structured outputs back into systems like Salesforce or your LOS.

No. Kaaj does not replace underwriters. Kaaj automates repetitive document review, data extraction, verification, analysis, and memo preparation so underwriters can focus on judgment, exceptions, structuring, and final decision-making. Humans remain in control.

Kaaj is built specifically for lending workflows. It understands borrower packages, bank statements, KYB checks, credit policy, fraud signals, exception handling, credit memos, and LOS/CRM handoffs. Generic AI tools can summarize text, but Kaaj is designed to produce structured, evidence-backed credit intelligence inside real lending operations.

Agentic AI underwriting uses specialized AI agents to perform parts of the underwriting workflow, such as document intake, KYB, bank statement analysis, fraud checks, policy review, and credit memo drafting. Instead of following one fixed checklist, agents can branch based on what they find, gather additional context, preserve reasoning, and prepare decision-ready analysis for human review.

Traditional underwriting automation usually follows fixed rules or linear workflows. Agentic AI can adapt to the context of a specific deal. For example, if a business name mismatch appears, an agent can compare SOS records, owner names, addresses, web presence, and document evidence before deciding whether the mismatch is likely material or explainable.

Kaaj is designed to generate decision-ready analysis, not to remove human judgment. Kaaj can apply lender-specific policy rules, surface risk signals, draft credit memos, and recommend areas for review. The lender's credit team remains responsible for final approval, decline, exception handling, and deal structuring.

Kaaj preserves reasoning by linking outputs to the source evidence used to generate them. For example, a bank statement classification, KYB finding, fraud flag, or policy exception should be traceable back to the documents, transactions, records, and logic that supported it. This helps credit teams review the analysis and maintain an audit trail.

More integrations can make data easier to retrieve, but they do not automatically interpret, reconcile, or document that data. Underwriting requires judgment across inconsistent documents, bank activity, business records, fraud signals, and policy exceptions. Agents help with the work that happens after data retrieval.

Kaaj can process common small business lending documents including applications, bank statements, tax returns, P&L statements, balance sheets, invoices, equipment invoices, titles, driver's licenses, voided checks, SOS filings, insurance certificates, and supporting documents. The exact document set can be configured by workflow and lender requirements.

Yes. Kaaj can classify incoming files, identify document types, rename files, detect missing documents, and organize borrower packages before underwriting begins. This helps reduce manual file prep and makes packages easier for credit and operations teams to review.

• Auto-classification of 40+ document types
• Intelligent file renaming for package organization
• Missing document detection against lender requirements
• Structured package delivery to your existing workflow

Yes. Kaaj can check whether required documents are present and flag common issues such as missing bank statement months, incomplete statement pages, unsigned applications, stale documents, missing invoices, or mismatched borrower information. Required-document rules can be aligned to the lender's workflow.

Yes. Kaaj can ingest submissions from email workflows, including attachments from borrowers, brokers, ISOs, or internal teams. It can classify the attachments, extract relevant fields, organize the file, and create a structured package for review.

Kaaj is designed for messy borrower packages, including PDFs and scanned documents. Output quality depends on file quality, but the system can use document intelligence, OCR, extraction logic, and review workflows to turn unstructured files into structured analysis.

Kaaj verifies businesses by cross-checking borrower-provided information against external and internal evidence. This can include Secretary of State records, business websites, domain age, address signals, reviews, web presence, business directories, ownership clues, and document consistency across the application package.

KYB automation is the process of using software to verify business identity, legitimacy, ownership signals, registration status, addresses, and supporting evidence. For lenders, KYB automation helps reduce manual checks, identify inconsistencies earlier, and document the basis for business verification.

Kaaj can support Secretary of State checks as part of business verification. It can compare entity names, status, registration details, addresses, and other available signals against the borrower's application and supporting documents.

Yes. Kaaj can analyze web presence signals such as business website, domain age, online reviews, maps, business directories, and other public indicators. These signals help credit teams understand whether a business appears consistent with the borrower's claims.

Kaaj can flag and help evaluate business name mismatches across applications, bank statements, SOS records, invoices, tax documents, websites, and IDs. The goal is not just to flag every difference, but to help determine whether the mismatch is explainable or risky.

Web presence can help lenders understand whether a business appears active, established, and consistent with its application. Signals such as website history, reviews, address presence, and business listings can help supplement formal records, especially in document-heavy SMB lending workflows.

Kaaj analyzes bank statements by extracting transactions, classifying deposits and debits, identifying revenue patterns, detecting transfers and non-revenue activity, calculating cashflow metrics, and surfacing risk signals such as overdrafts, NSFs, negative balance days, and recurring debt payments.

Basic OCR extracts text and transaction rows. Kaaj goes further by interpreting what those transactions mean in a lending context. It helps distinguish revenue from transfers, refunds, owner injections, loan proceeds, MCA deposits, and other non-operating activity.

Yes. Kaaj can classify deposits based on transaction descriptions, recurring patterns, borrower context, industry context, and supporting documents. Revenue classification is important because not every deposit represents true business revenue.

Kaaj can help identify potential MCA stacking by detecting recurring funder payments, loan-related credits, daily or weekly debits, and patterns that may indicate existing merchant cash advance obligations. These signals help credit teams understand debt burden and repayment capacity.

Yes. Kaaj can calculate common cashflow and bank statement metrics such as revenue trends, average daily balance, negative days, NSF/overdraft activity, ending balances, deposit concentration, recurring debt payments, and other lender-specific indicators.

Kaaj can support analysis across multiple bank accounts when the workflow requires it. It can help consolidate transaction activity, identify transfers between accounts, and summarize borrower-level cashflow across statements.

Yes. Kaaj can use business context to improve transaction classification. A payment type that may be normal in one industry may be suspicious or unusual in another. This context helps reduce false positives and improves review quality.

Kaaj helps detect fraud by flagging inconsistencies, suspicious document patterns, altered statement indicators, mismatched names or addresses, unusual bank activity, duplicate submissions, questionable web presence, and other anomalies that may require human review.

Kaaj can run integrity and consistency checks that help identify potential bank statement tampering or suspicious document patterns. These signals are surfaced for review and should be considered alongside other evidence.

Kaaj can help identify duplicate or related submissions by comparing borrower names, business entities, owners, addresses, EIN/FEIN data where available, documents, and other deal attributes. This is useful in broker-driven and high-volume workflows.

Kaaj does not have to replace existing fraud tools. It can complement them by bringing fraud and anomaly signals into the underwriting workflow, connecting document evidence, business verification, bank statement activity, and credit memo documentation.

Suspicious findings should be surfaced with evidence and reasoning so human reviewers can evaluate them. Kaaj is designed to help teams prioritize review, not to blindly auto-reject borrowers based on isolated signals.

Yes. Kaaj can generate structured credit memos that summarize borrower information, business verification, bank statement analysis, financial metrics, risk signals, policy exceptions, and supporting evidence. Memos are intended for human review and editing.

Yes. Kaaj can be configured around lender-specific credit policies, document requirements, risk thresholds, exception rules, and memo formats. This helps ensure outputs match the way your credit team actually evaluates deals.

Kaaj can flag when a deal appears outside defined policy thresholds, such as overdraft limits, revenue requirements, time-in-business expectations, negative balance days, or debt burden indicators. It can also help document the evidence behind an exception review.

Yes. Kaaj-generated memos should be reviewable and editable by human users. The goal is to accelerate preparation, analysis, and documentation while allowing credit teams to apply judgment and finalize the memo.

Kaaj is designed to preserve an evidence trail behind extracted data, classifications, findings, and memo outputs. This helps reviewers understand where conclusions came from and supports internal controls and second-line review.

Kaaj helps equipment finance lenders automate intake, KYB, invoice review, equipment detail extraction, bank statement analysis, fraud checks, financial spreading, and credit memo generation. This is especially useful for small-ticket deals where manual underwriting costs can hurt profitability.

Kaaj helps brokers build cleaner lender-ready packages by classifying documents, checking completeness, validating businesses, summarizing bank statement activity, and preparing structured deal outputs. This reduces back-and-forth and helps route deals to the right lender faster.

Kaaj helps MCA and revenue-based finance teams analyze bank statements, identify potential stacking, classify revenue, detect suspicious activity, summarize repayment capacity, and package files for fast review. It is designed for high-velocity SMB funding workflows.

Kaaj helps community banks and credit unions automate document-heavy small business credit workflows while preserving traceability. It can support intake, KYB, cashflow analysis, financial spreading, fraud checks, credit memo generation, and audit-ready documentation.

Yes. Kaaj is especially valuable in small-ticket lending because manual underwriting costs can be high relative to deal size. By reducing repetitive review work, Kaaj helps lenders process smaller deals more profitably without sacrificing consistency.

Yes. Kaaj can support broker-driven workflows by ingesting email packages, classifying attachments, checking for missing documents, summarizing borrower data, and producing lender-ready analysis. This helps reduce package cleanup and rework.

Kaaj can integrate with CRMs, LOS platforms, email systems, file storage, APIs, and workflow tools. Common integration patterns include Salesforce, inbox ingestion, document storage, API/webhook handoffs, and structured field sync into existing systems.

Kaaj can integrate with Salesforce to create or update records, push structured borrower and deal data, attach documents, and sync credit analysis outputs. The exact integration depends on your Salesforce data model and workflow.

No. Kaaj is designed to layer on top of existing systems. It can ingest from and push outputs back into your current workflow, reducing the need for a rip-and-replace implementation.

Implementation time depends on workflow complexity, integration scope, document types, and policy configuration. Many workflows can start with a focused pilot, and a typical go-live can be around a few weeks depending on scope.

Yes. Email-based intake is often a practical starting point because many lenders and brokers already receive borrower packages through shared inboxes. Kaaj can ingest attachments, classify documents, and structure the package before more advanced integrations are added.

Kaaj can support API and webhook-based workflows for teams that want to connect document intake, analysis outputs, status updates, or memo results into their existing systems.

Kaaj can be configured to align with a lender's memo structure, policy language, risk sections, and evidence requirements. This helps teams adopt automation without changing how credit committees or reviewers expect to see analysis.

Kaaj is SOC 2 Type II compliant. Visit the Kaaj trust center for security documentation.

Kaaj is SOC 2 Type II compliant. Visit the Kaaj trust center for current security documentation.

Kaaj is SOC 2 Type II compliant. Visit the Kaaj trust center for current data handling documentation.

Yes. Kaaj is SOC 2 Type II compliant. Security documentation is available through the Kaaj trust center.

Kaaj is SOC 2 Type II compliant. Visit the Kaaj trust center for current security documentation.

Kaaj is SOC 2 Type II compliant. Visit the Kaaj trust center for current data handling documentation.

Kaaj is SOC 2 Type II compliant. Visit the Kaaj trust center for current security documentation.

Kaaj pricing depends on workflow scope, volume, modules, integrations, and support requirements. Most customers start with a focused workflow or pilot and expand as Kaaj proves value across intake, analysis, and credit memo automation.

Yes. Kaaj can support focused pilots around specific workflows such as document intake, bank statement analysis, KYB, credit memo generation, or broker package processing. A pilot helps validate workflow fit, accuracy, integration needs, and operational impact.

Onboarding typically includes workflow discovery, document and policy review, configuration, sample file testing, user feedback, integration setup if needed, and go-live support. The goal is to match Kaaj to your actual underwriting process rather than forcing a generic workflow.

A strong Kaaj evaluation usually includes credit leadership, underwriting or analyst users, operations, risk/fraud, IT/security, and the system owner for your LOS or CRM. Involving all groups early helps align workflow, controls, and implementation.

You can book a demo to walk through a real lending workflow, from raw borrower package to structured analysis and credit memo. The best demos are tailored to your lending type, document package, credit policy, and current systems.