Banking · 03

The cleanest 12-to-16-week build in the dossier.

A document agent that drafts the memo, cites the source clause, flags the contradiction. The approver still owns the decision.

Proven pattern

A document agent ingests messy, multi-format, multi-language commercial-loan, mortgage, or regulatory filings; performs entity extraction, clause analysis, contradiction detection, and credit-memo or filing-draft generation; presents to a human approver with citations. Bounded, deterministic, and the cleanest 12–16-week build cycle in the dossier.

Hard KPIs · operator-disclosed where possible
60–80%
time-per-loan / mortgage / filing reduction
3–5×
throughput per analyst
13–14w
kickoff to live (Bancolombia bar)

Buyer · Head of Wholesale Banking, Head of Mortgage Operations, or Head of Regulatory Reporting + COO.

Operator references

Provenance tiered explicitly. Audited > Operator-disclosed > Vendor-published. Treat all vendor-tier figures as directional.

JPMorgan Chase · COiN (US)
~360,000 lawyer-hours/year reclaimed on commercial-loan review. Canonical reference; repeatable; internal-first.
Vendor-published
Bancolombia (Colombia)
Appian + RPA + AI document automation built in 13–14 weeks; ~80,000 docs/month; ~70% manual-time reduction. Cumulative 127K hours saved; 1,300% ROI claim.
Operator-disclosed
Morgan Stanley DevGen.AI (US)
Adjacent code-modernization pattern. Reviewed 9M+ lines of legacy code, ~280,000 developer hours reclaimed.
Operator-disclosed
BBVA Retail Banking Legal Assistant GPT
40,000+ legal questions/year handled, 9-attorney team. The canonical regulatory-knowledge agent template.
Operator-disclosed
Why this beats RPA or OCR-only

RPA breaks on document variance; OCR-plus-templates breaks on bilingual contract corpora and non-standard formatting. The agent reasons about the document's intent, cross-references policy, and produces narrative output. Human approver still owns the decision.

Why this is hard

Bilingual (PT/ES/EN) contract corpora are the differentiator and the labor sink. Evidence retention and lineage requirements are heavy. Integration into core banking (Temenos, FIS, Finacle, proprietary), document stores, and CRM is non-trivial.

Mexico · LATAM specifics

Bilingual (Spanish + English) contracts are the norm in cross-border lending; Brazilian Portuguese is its own corpus, structurally distinct. Mexican mortgage origination at Banorte, BBVA México, Santander México, and Banco Azteca is structurally similar to Bancolombia's pattern and a clean adjacent target.

Reference architecture

Each node maps to a regulatory anchor.

  1. 01
    Document intake: multi-format, multi-language
  2. 02
    Entity extraction + clause segmentation
  3. 03
    Contradiction detection across the document and against policy
  4. 04
    Credit-memo / filing draft generation with inline citations
  5. 05
    Human approver review with explicit accept / reject / edit
  6. 06
    Write-back to CLM, core banking, and document store
  7. 07
    Lineage + audit trail tied to retention policy
Anti-positioning

Not A better OCR. A faster RPA. A Hyperscience replacement.

But Extraction + reasoning + memo drafting + audit-grade evidence assembly + human approval, in your stack and your languages.

What didn't work initially

Early deployments fail when the bilingual corpus is treated as a translation problem. Mexican-Spanish contract clauses are not Iberian Spanish; Brazilian-Portuguese mortgage instruments are their own register. The fix is region-specific evaluation sets owned by the bank, not the vendor.

A working session, not a sales call.

Two hours with a partner. Your incumbent stack, your data posture, and the regulatory surface against a sovereign reference architecture for banking.

Request a working session →

A working session, not a sales call.

Two hours with a partner. We map your AI spend, data exposure, and governance posture against a sovereign reference architecture. You leave with a memo. We leave with a decision.

By invitation.