AI borrowing-base certificate automation reads the certificate a borrower submits along with its receivables aging, payables aging and inventory reports, applies the eligibility rules, advance rates and reserves from the credit agreement, and recalculates availability independently so the lender's analyst only reviews the differences. This guide describes how a Ryz AI pod built that workflow for an asset-based lender: the problem, the architecture and how analysts use it. The client is confidential and we do not publish figures for this project, so you will find no performance numbers here, only how the system works.
In asset-based lending, availability is only as good as the borrowing-base certificate behind it. Borrowers submit certificates on a set cadence, often monthly and sometimes weekly or more often for tighter facilities, and each one arrives with supporting reports in the borrower's own formats: accounting system exports, spreadsheets with custom aging buckets, PDFs of inventory listings. For every certificate an analyst has to:
The arithmetic is not hard. The work is in normalizing messy reports, matching customer names across files and remembering which special terms apply to which borrower. That is where errors creep in and where review time goes.
A Ryz pod worked on the lender's team, in its cloud and repos, and built the system in five parts.
Certificates and supporting files arrive by email or portal upload. The pipeline identifies the borrower and the period, classifies each file (certificate, receivables aging, payables aging, inventory report, ledger extract) and flags anything missing before an analyst opens the package.
Every borrower ages receivables a little differently: by invoice date or due date, with different bucket boundaries, with or without credits applied. Models extract line items and column meanings from each report, and code maps them to one canonical schema per facility: customer, invoice, amount, date basis and bucket. Customer names are matched across the receivables and payables files so contra accounts can be found.
The eligibility criteria, advance rates, concentration limits and reserve definitions are extracted from each credit agreement and its amendments into a versioned rule set, with the clause each rule came from. Analysts approve that rule set once per facility and again when an amendment changes it. This is the same extraction pattern used in our covenant monitoring build.
A deterministic engine applies the approved rules to the normalized data and produces the lender's own borrowing base: gross collateral, each ineligible category, eligible collateral, advance rates, reserves and availability. It sits beside the borrower's certificate line by line, and every difference above the analyst's tolerance is flagged with the records that cause it.
The analyst sees a reconciliation view: the borrower's number, the recalculated number and the drill-down to individual invoices or inventory lines. Analysts can accept, override with a note, or request more information from the borrower, and the approved availability is written to the loan system only after sign-off. Overrides and notes are kept, and the next period starts from them.
As with covenant testing, the boundary is deliberate: models interpret documents, code applies the rules, people approve availability. A model never approves a draw.
Borrowing-base review shares most of its foundation with other credit workflows: the document parsing, agreement extraction, eval harness and review screen are the same ones used for covenant monitoring and underwriting document processing. Building them once in your own cloud and reusing them is where most of the long-term value sits. For the broader picture, see financial services AI engineering; for agent-style workflows that also request missing documents from borrowers, see AI agent development for financial services.
Ryz AI pods are one accountable team of senior engineers who work on your team, in your stack, cloud and repos, reporting to your leads. You meet the named engineers before the engagement starts, and engineers can start in under two weeks once you say go. Typical cost is $7,000 to $15,000 per engineer per month, with leads from $15,000, quoted per team. A pilot pod of a tech lead and two senior engineers starts at $35,000 per month ($15,000+ plus 2 × $10,000 to $15,000), and a full pod of about seven senior engineers starts at $75,000 per month. Project cost is team size × months × monthly rate. See AI pod teams for how pods run.
If you have a handful of facilities with clean, standard reports, a well-built spreadsheet may be enough. If you would rather license a packaged collateral monitoring platform, a platform vendor fits better. A build pays off when borrower reporting is varied, the portfolio is growing, and you want collateral data and history in your own warehouse.
It is a system that reads a borrower's certificate and supporting collateral reports, applies the eligibility rules, advance rates and reserves from the credit agreement, recalculates availability independently and shows the analyst where the borrower's numbers differ.
No. Models read and interpret the documents, deterministic code applies the rules, and an analyst approves availability before anything is written to the loan system.
Yes. That is the main reason to use models here. Each borrower's aging and inventory formats are mapped to one schema per facility, and changes in format or date basis are flagged for review.
Yes. Reserves and amended terms are stored as versioned rules with effective dates, approved by the analyst, so each certificate is tested against the terms in force for that period.
Cost is team size × months × monthly rate. Ryz engineers typically cost $7,000 to $15,000 per month each; a three-person pilot pod starts at $35,000 per month.
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