Multinational Data Extraction and Analysis Platform
The client was a multinational compliance operations group inside a large enterprise, responsible for ingesting regulatory filings, customer KYC documents, and supplier paperwork across more than twenty countries. The existing process used per-country offshore teams to manually extract data into a centralized system, which created consistency problems across regions and made same-day compliance reporting impossible at the group level.
Computer Vision Solutions
Compliance, Enterprise Data
18 weeks from kickoff to twenty-country cutover
5 specialists
The full story
The practical problem was that documents differed not only by language but by structure — a Brazilian tax document and a German one carry conceptually similar fields in radically different layouts, and the existing OCR tooling had been licensed per country with no shared schema. Compliance officers spent significant time reconciling extracted data against the group-level schema before any analysis was possible, which meant aggregate reports lagged actual document arrival by three to five business days.
We built a unified extraction platform with a single structured schema that all documents mapped into, regardless of source country. The pipeline used a layout-aware transformer trained per country code with a shared output schema, plus a translation layer that ran only when the downstream consumer required a specific output language. Compliance rules ran centrally against the structured output, which removed the per-country variance in interpretation.
What shipped was a platform where a document arrives, gets routed to the right per-country extraction head based on automatic language and layout detection, produces structured output in the group schema, and runs through the central compliance ruleset within minutes. The per-country offshore teams shifted from extraction work to exception handling, which expanded effective coverage without expanding headcount.
Twenty countries, twenty document formats, and twenty offshore teams producing inconsistent extracted data into one central system.
Per-country OCR tooling produced output in locally-defined schemas that required manual reconciliation against the group schema.
Compliance officers waited three to five business days for aggregated data before they could run group-level reports.
Translation happened upfront on every document regardless of downstream need, wasting compute and introducing translation errors.
Compliance rules were interpreted locally per country, creating defensibility gaps when the group was audited centrally.
New country onboarding took six months because each addition required a new OCR vendor and a new reconciliation playbook.
How we structured the engagement
Made the structured schema the contract — every per-country extraction head produced the same output regardless of input layout.
- 01Phase 01Weeks 1-3
Discovery
Surveyed document samples from all twenty countries, taxonomized field overlaps, and worked with compliance to define the canonical group schema. Output: a unified schema covering ninety-two fields, a per-country mapping table, and a confidence requirement per field from compliance.
- 02Phase 02Weeks 4-5
Architecture
Designed a routing layer that detected language and layout, then dispatched to country-specific extraction heads, all producing the canonical schema. Picked Mistral for the multilingual base model and fine-tuned per country on the existing extraction history paired with human-corrected ground truth.
- 03Phase 03Weeks 6-16
Build
Shipped four country heads as a pilot batch, validated against ground truth, then scaled to twenty in waves of four. Built the centralized compliance ruleset over the canonical schema. Added the on-demand translation layer that ran only when downstream consumers requested non-source-language output.
- 04Phase 04Weeks 17-18
Launch
Cut over per country as each head passed the field-level accuracy threshold of ninety-five percent on a held-out sample. Migrated the offshore teams to exception handling and built the exception triage queue based on what showed up in the first three weeks of live traffic.
What we built, component by component
- 01
Routing layer
Detects language and document layout, dispatches to the appropriate country-specific extraction head with metadata attached.
- 02
Country extraction heads
Per-country fine-tuned Mistral models that emit the canonical group schema regardless of input layout differences.
- 03
Schema validator
Enforces field types, ranges, and required confidence per the group schema before any downstream consumer reads the output.
- 04
Compliance rule engine
Centralized ruleset that runs against the canonical schema and produces a per-document compliance status and reasoning.
- 05
On-demand translator
Translates structured fields only when a downstream consumer requests a specific output language, avoiding unnecessary work.
- 06
Exception queue
Routes low-confidence extractions and rule failures to the regional team for human review with one-click correction.
A document arrives at the routing layer which detects language and layout and dispatches to the correct country head. The head produces canonical schema output, the validator enforces field-level requirements, and the compliance rule engine runs centrally. Translation is deferred until requested, and any low-confidence field or rule failure is queued for regional human review.
The trade-offs we made and why
Per-country heads with a shared schema, not one universal model
A single multilingual model averaged across countries and lost country-specific layout signal. Per-country fine-tunes preserved the layout cues that mattered locally while the shared schema kept the downstream surface uniform — best of both approaches.
Deferred translation until requested
Upfront translation paid translation cost on every document and burned that compute even when the downstream consumer wanted source-language output. Deferring translation cut compute spend substantially and made translation auditable per request.
Centralized compliance over local interpretation
Per-country compliance interpretation produced defensibility gaps at audit time. Running the rules centrally over the canonical schema gave the compliance group a single source of truth and removed the audit risk from regional variance.
Migrated regional teams to exception handling rather than displacing them
Offshore teams carried institutional knowledge about edge cases that the model would not learn from scratch. Repositioning them as exception handlers retained the expertise and gave us a clean source of training signal for the next retrain cycle.
What changed for the client
manual data entry
Reduction in human extraction hours per thousand documents across the twenty-country footprint after full rollout.
countries unified
Country-specific extraction heads in production at cutover, all producing the same canonical schema for downstream consumers.
field accuracy
Per-field extraction accuracy threshold required before any country head was promoted to production, held across rollout.
group reporting
Aggregated compliance reports available within hours of document arrival instead of three-to-five business days previously.
The tools behind the system
Built with a deliberate stack chosen for production reliability and operational velocity.
Lessons learned from the build
The schema-first decision compounded across every downstream component. Once we committed to one canonical schema, the compliance engine, the reporting layer, and the exception queue all became simpler. We would refuse to ship any per-country variance in the contract next time.
Deferred translation was a quietly large win on cost. We almost shipped upfront translation because it was easier to implement, and the deferred approach saved meaningful compute spend that funded ongoing model retraining. Always question default-eager behaviors.
Retaining offshore teams as exception handlers turned out to be a force multiplier on model quality. Their corrections fed retraining and their domain knowledge surfaced edge cases that pure data labeling would have missed. Replacing them entirely would have left model quality permanently lower.
Similar delivery work usually starts in these service areas
If you are exploring a similar product, workflow, or implementation challenge, these are the service tracks that usually fit best.
Where this project sits in the bigger market picture
This project reflects a broader pattern we often see when teams use AI to improve operational speed, insight quality, and product capability.
Build a result-driven AI product with a team that has shipped before
If you are exploring a similar product, workflow, or AI use case, we can help scope the right architecture, delivery model, and first milestone.
Related case studies worth reviewing next
Have an AI idea, messy workflow, or product vision? Let's make it buildable.
Bring the problem. We'll help shape the product, define the architecture, and show the fastest path to a serious first version.
A practical first roadmap in the discovery call
Architecture, timeline, and delivery options in plain English
Security, scalability, and reliability discussed upfront
Model registry
softus-rag-v4.2
187ms
Latency
128k
Context
$0.004
Cost / req
Evaluation suite
Deploy pipeline
prod / canary 25% — healthy