We build private, auditable workflows that read account applications, client questionnaires, KYC packs, financial statements, term sheets, and supporting documents, then prepare structured work for analyst or reviewer approval.
Teams spend too much time normalizing applications, questionnaires, statements, IDs, entity documents, term sheets, and supporting files before real review begins.
Back-and-forth piles up when missing fields, stale documents, mismatched names, or incomplete counterparty data are discovered deep into review.
Financial teams cannot treat public AI tools as a dumping ground for borrower, investor, customer, counterparty, or portfolio records.
Industry document automation research points to account applications, client questionnaires, KYC and KYB packs, financial statements, proof-of-income files, proof-of-assets files, identity documents, term sheets, capitalization tables, company filings, and pitch decks.
Financial-document automation research frames manual review and data entry as a scaling bottleneck across lending, onboarding, customer due diligence, portfolio review, and deal analysis.
Industry document automation research highlights dense multi-page tables, inconsistent layouts, multi-language content, handwriting, stamps, signatures, and traceable field extraction, all relevant to risk and compliance workflows.
Extracts borrower, customer, entity, income, asset, and identity data from application packets for reviewer approval.
Turns financial statements, bank statements, and supporting schedules into structured fields with source evidence.
Finds required KYC, KYB, identity, incorporation, ownership, and counterparty fields before onboarding stalls.
Prepares proof-of-income, proof-of-assets, collateral, identity, and supporting-document packets for credit review.
Extracts facts from term sheets, company filings, financial statements, capitalization tables, and pitch decks for analyst review.
Staff manually sort application packets, KYC files, statements, term sheets, and supporting documents before analysts can assess risk or make decisions.
The workflow extracts verified fields, flags missing information, links source evidence, and packages work for analyst, compliance, or underwriting review.
Private workflows can use hosted private-cloud inference, dedicated cloud or VPC deployment, or local/on-prem inference when client or counterparty data cannot leave your environment.
Avoid billing surprises with clear workflow-based packages. We do not penalize you for using more AI like other vendors.
Every result can include source files, extracted fields, reviewer approval, risk notes, exception status, and write-back history.
We choose one onboarding, underwriting, due diligence, or statement-processing workflow with clear cycle-time and risk impact.
We configure extraction, source evidence, reviewer queues, approval gates, and client-data boundaries.
You see cycle time, missing-info reduction, reviewer quality, exception rates, and the next workflow roadmap.
Founder, Performance AI Lab | Former Meta SuperIntelligence Lab
With 25 years in software engineering and enterprise infrastructure, I've built systems for some of the world's most demanding environments. I founded Performance AI Lab to bring private, auditable AI workflows that reduce manual work and preserve operating know-how to mid-market operators.
If your team is buried in documents, emails, approvals, spreadsheets, or software handoffs, we can help identify the first workflow worth automating.
Private AI workflows for the work that falls between your documents, apps, approvals, and operating knowledge.