P-01PAIN POINT
Terminology drift triggers compliance risk
Bilingual prospectuses run hundreds of pages. Manual term-by-term comparison is brittle and error-prone — a single mismatch between '净利润' and 'net profit' can trigger a regulator inquiry.
Delivered / Production
GSAI · 2026 · 05 · 0023
AI Solution BriefCapital markets documents, from manual review to AI-powered automation
End-to-end automation for prospectuses, annual reports, circulars, and offering circulars — bilingual term checks, version diffing with risk flags, email archival, and project workflow.
8 min read · v1.0 · updated May 2026
96.1%
Terminology accuracy
Bilingual EN/ZH documents
1.8s
Average processing time
End-to-end · P95 ≤ 3.0s
-82%
Manual review workload
Pre/post deployment
11 months
Production runtime
3 use cases / multiple clients
Terminology accuracy
96.1%
Bilingual EN / ZH
Background
What documents do we handle?
Capital markets documents are among the most complex, highest-stakes documents in finance — bilingual, drafted across multiple parties, revised many times, and unforgiving of error. A single terminology mismatch, number slip, or version mix-up can trigger a compliance issue.
Prospectus
Length
200–600 pages
Frequency
IPO / Follow-on
Annual Report
Length
100–300 pages
Frequency
Annual
Circular
Length
50–200 pages
Frequency
Deal-driven
Offering Circular
Length
100–400 pages
Frequency
Bond issuance
/ 01The Problem
Where capital markets document handling breaks down
Handling capital markets documents is high-touch work. Terminology checking, version diffing, file archiving — repetitive labor that consumes huge amounts of team time, with zero tolerance for error.
Industry ·Securities · IB · Law firms
Volume ·200+ pages per document
Current flow ·Manual review + email handoff
Goal ·End-to-end automation
P-02PAIN POINT
Endless revisions, version chaos
An average IPO project goes through 10+ rounds of revisions. Every round means manually diffing old and new versions to find changes and assess risk. Blackline production is slow and easy to get wrong.
P-03PAIN POINT
Files scattered, collaboration broken
Documents move through email, WeChat, and shared drives with chaotic naming and manual archiving. Project managers burn hours just locating files and confirming versions.
P-04PAIN POINT
Small mistakes carry big risk
A wrong number, date, or name doesn't just hurt layout — it lands directly in compliance review, client trust, and delivery.
/ 02Core Scenarios
Three high-value scenarios, broken down
From prospectuses to annual reports, from circulars to offering circulars — every document drives heavy terminology checking, version control, and multi-party handoff.
Term-match accuracy
96.1%
Processing time
<3min/doc
S-0102-A · Bilingual Consistency
Bilingual terminology consistency
Line-by-line checking, handled by AI
Built around your project- and company-level term bank, the AI scans bilingual documents end-to-end — flagging inconsistent translations, formatting mismatches, and uncatalogued new terms — and outputs a structured consistency report.
- Supports PDF / Word / Excel / scanned documents
- Project-level + company-level term bank management
- Auto-detects numbers, dates, names, and key fields
- Consistency report with inconsistencies pinpointed
- Term bank learns over time — accuracy compounds
Change detection rate
99.2%
Blackline generation
<2min
S-0202-B · Version Diff & Risk
Version diff with risk detection
Every change, surfaced and explained
On every revision, the AI auto-generates a Blackline diff report, flags high-risk changes (amounts, dates, names), and produces a change summary with risk rating.
- Auto-generated Blackline diff reports
- Smart flagging for high-risk changes (numbers / dates / names)
- One-click change summary
- Multi-round version-chain diffing
- Email notifications to responsible owners
Archive accuracy
99.5%
Manual intervention
-85%
S-0302-C · Archive & Workflow
Email archival and project workflow
From inbox to delivery, fully automated
RPA pulls attachments from email and shared drives, archives by project code, then runs format preprocessing, system intake, approvals, and notifications — keeping every handoff traceable.
- Auto-download and archive from email / shared drives
- Smart naming and routing by project code
- Format preprocessing and system intake
- Task notifications + auto-generated check reports
- Approval flow + operation log, fully traceable
/ 03How We Build It
Three layers: understanding, execution, and collaboration
All three layers share a unified project object — every IPO or annual report project gets its own workspace, version tree, check report, and operation log.
Evaluation set · Real project documents·Cycle · 10 weeks
APath A
AI Engine
Understand, align, judge
Reads bilingual financial documents at the document layer.
- Document parsing (PDF / Word / Excel / scans)
- Translation review and term bank
- Numbers / dates / names consistency checks
- Version diff with high-risk change detection
BPath B
RPA Bot
Move, archive, notify — quietly
Closes the loop on repetitive system tasks.
- Auto-download attachments from email / shared drives
- Archive and name by project code
- Format preprocessing and system intake
- Task notifications + auto-generated check reports
CPath C
Workflow Platform
Multi-party collaboration with a clear paper trail
Surfaces project status across teams.
- Project workspace + version management
- Approval flow + client portal
- Log tracking + operation audit
- Granular permissions and document-visibility controls
End-to-End Pipeline
Inbound
Email / shared drives
Intake
RPA intake + archive
Analysis
AI parsing + consistency check
Diff
Version diff + Blackline
Workflow
Task routing + approvals + notifications
Outbound
Typesetting / client portal
/ 04Where It Fits
Where it lands
All cases below come from real projects, sanitized for disclosure. Each one covers the problem, the solution, and the business outcome.
C-01Securities · Investment Banking
Southwest Securities
Securities and invoice data processing — knowledge-graph driven
Problem
Key information in bond documents was scattered across multiple file formats. Manual extraction was slow and error-prone.
Solution
Used a knowledge graph to extract key data from bond documents, auto-index it, and generate check reports — covering the core bond data flow end to end.
OCRKnowledge graphStructured extractionCheck reports
Business Outcome
Covered 100K+ documents; the core bond data flow now runs end-to-end automated.
C-02Insurance · Back office
Major insurance carrier
Policy scan handling — RPA + AI automation
Problem
Policy scans required manual structured-field entry, turning the back office into a 'data entry factory'.
Solution
Built an RPA + multimodal AI pipeline: extract structured fields, write to the business database, and generate reconciliation reports in real time.
RPAScan OCRStructured ingestion
Business Outcome
Manual entry dropped ~78%; back office shifted from data entry to exception review.
C-03B2B · Manufacturing / Logistics
Wavesteam reference case
AI handwritten order recognition
Problem
Handwritten orders are highly unstructured — traditional OCR fails on connected strokes, edits, and folds.
Solution
Built a multimodal recognition and validation loop. Frontline staff can place and archive orders with a single photo.
Handwriting OCRField validationOrder workflow
Business Outcome
Field-level accuracy went from 68.4% to 96.1%; manual review workload dropped 82%.
View public case/ 05Security & Compliance
Security and compliance, by design
Financial documents carry highly sensitive data. We bake security into the architecture from day one — not bolted on afterward.
End-to-end encryption
AES-256 for files in transit and at rest. TLS 1.3 transport. Keys are customer-managed or fully managed by us.
Private deployment
Deploy in your own data center or private cloud. Data stays in your environment and meets financial-industry data security standards.
Operation audit
All file actions — access, view, download — are logged in full. Trace by time, person, or project.
Document lifecycle management
Automated expiration cleanup + version retention policies meet compliance archive requirements and prevent long-term exposure of sensitive files.
Granular access control
Role + project-based permissions control document visibility, operation rights, and approval flow.
Compliance alignment
Designed to align with ISO 27001, SOC 2 Type II, GDPR, and other major security and compliance frameworks.
/ 06MVP Roadmap
Move fast, ship something visible every 30 days
Start with one working MVP — let the team see AI actually helping them — then layer in the other two scenarios. Unify them into a platform last.
0Delivered
Week 0
Sanitized documents + team alignment
Deliverables
- Pick 1–2 historical projects for the POC
- Confirm evaluation metrics, owners, and timeline
- Deliver POC SoW + metrics card
1Delivered
Week 1–3
MVP 1 · Bilingual term consistency
Deliverables
- Term bank v0 (project + company level)
- AI term-scanning engine
- Consistency report output
2In Progress
Week 3–7
MVP 2 · Version diff + risk changes
Deliverables
- Auto-generated Blackline diffs
- Risk-change summaries
- Email notification integration
3Planned
Week 6–10
MVP 3 · Email archival + project workflow
Deliverables
- RPA intake bot
- Project workspace v0
- Approvals and logging
4Planned
Quarter 2
Unified platform + client portal
Deliverables
- Three MVPs unified into a single platform
- Client portal launch
- Permissions and audit hardening
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Let's Talk
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We unpack the workflow with you, judge whether AI is worth using and which approach makes the most sense, then come back within 5 business days with a practical initial plan and estimate.
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