AI agents for work that has to survive an audit
Loan files, KYC packets, and transaction exceptions get reviewed by hand because the output has to be defensible. We build agents that extract, classify, and flag — with the citation, the confidence score, and the human sign-off attached to every decision.
docs.ingestdoc store142 pages, 6 document typesentities.extractdoc storeborrower, collateral, covenantsanomaly.flagloan file2 mismatches against the filereport.buildloan fileaudit-ready summary attached
Trusted by teams building and running mission-critical platforms
Where the hours go in financial services
In a regulated shop the constraint is never enthusiasm for automation. It’s whether the output can be defended. That changes what you build.
The same document, ten thousand times
Loan files, statements, KYC packets. Analysts read the same handful of document types all year, and the reason a person does it is defensibility, not difficulty.
Exceptions eat the week
Straight-through cases already clear themselves. Your team spends its week on the small percentage that don’t — chasing missing pages and reconciling figures that refuse to tie.
A black box is unusable
Model risk management, SR 11-7, and your examiners all want to know how a decision was reached. An agent that’s right 95% of the time is worth nothing if it can’t show the work.
The core has no API
The system of record is a mainframe or a vendor platform with a nightly batch file. Anything real-time has to be engineered around it, and most vendors quietly assume it away.
Design, build, and run — in your stack
Your risk team sets the thresholds and your tenant holds the data. The people who scope it are the people who ship it, so nobody is defending a black box on our behalf.
Instrument the queue
We shadow the analysts and measure it: volume by document type, exception rate, time per file, and where rework originates. Workflows come back ranked by defensibility, not only by hours saved.
Controls in the architecture
Confidence thresholds set with your risk team. Every output carries its citation and a score, and anything below the line routes to a named reviewer. The control lives in the design, not in a policy PDF.
Through the core you actually have
APIs where they exist, batch files and message queues where they don’t. Deployed in your Azure or AWS tenant, with data residency and retention set where your policy says they go.
Evidence before anyone asks
Full run logs, exportable. Extraction accuracy tracked against a labelled set so drift shows up as a number. When examiners ask how it works, the report already exists.
Systems we work inside
Agents act in your systems of record. No parallel tool for your team to maintain.
- Core banking and lending platforms
- Loan origination and servicing systems
- Document imaging and content stores
- Mainframe batch and message queues
- Snowflake and Databricks
- Azure and AWS in your own tenant
The reviews we take off your analysts
Every run leaves a trail. The agent shows which page a number came from and how confident it is, then names who signed off. That’s the part that makes it usable in a regulated shop.
Loan document review
Ingests the file, extracts borrower, collateral, and covenant terms, and flags anything that doesn’t tie to the application. Audit-ready summary attached to the loan record.
Doc store → extraction → origination system
KYC and onboarding packets
Assembles the packet, verifies each document against the requirement list, and returns a single exception list instead of five separate email threads.
Intake → verification APIs → CRM
Transaction exception handling
Works the break queue: pulls both sides of the record, proposes the reconciliation with its reasoning, and escalates only what a human genuinely has to judge.
Core → ledger → exception queue
Regulatory report drafting
Pulls the period figures, drafts the narrative sections, and cites the source system and timestamp behind every number in the filing.
Warehouse → reporting layer → filing draft
Client servicing requests
Reads the inbound request, retrieves the account context under the requester’s own permissions, completes the change, and logs it. Handoff to a person is clean when it happens.
Email or portal → core → CRM
We’ve done this already
Firm-level numbers. Ask us for regulated-environment references on the call.
Across enterprise engagements in regulated and complex operating environments
in delivered value
Platforms carrying sensitive financial and personal data in production
users on systems we built
Senior-only squad. No generalists, no offshored execution
years on enterprise systems
Bring us your slowest review
The queue your analysts dread — loan files, exceptions, KYC. In 45 minutes we’ll tell you what an agent can take, what evidence it produces for your examiners, and where the human stays in the loop. No pitch deck.