Enterprise AI analytics platform for a multilingual logistics operation
Yarvixo delivered an AI SaaS workflow that automated document-heavy operations across 14 data sources and 5 languages. The end result was 87% less manual processing, 99.2% extraction accuracy, and a production rollout in 11 weeks.
Project snapshot
An anonymized overview of the delivery scope, operating context, and the numbers that mattered after launch.
Client context
A mid-market logistics company running cross-border operations in Europe, with fragmented paperwork, multiple document formats, and strict internal SLA expectations.
Delivery scope
Document ingestion, classification, multilingual extraction, custom RAG workflows, ERP integration, and a human review console for operations staff.
Measured outcome
87% less manual handling time, 99.2% extraction accuracy on production flows, and a stable launch delivered in 11 weeks.
The challenge
The client did not need a generic chatbot. They needed an operations platform that could be trusted by analysts and rolled into an existing workflow quickly.
14 disconnected data sources
Invoices, customs paperwork, shipment documents, email attachments, and internal exports all arrived in different formats and from different systems.
5 operating languages
The platform had to handle multilingual documents without forcing the client into a separate workflow per region or per business unit.
Trust and auditability
Operations staff needed confidence in every extracted value, with clear review steps when the model was uncertain or source material was incomplete.
What we built
A focused AI product, not a demo - designed to fit how the operations team already worked.
Ingestion and normalization layer
A pipeline that accepted PDFs, scans, structured exports, and inbox attachments, normalized the content, and prepared it for downstream extraction and review.
Custom RAG decision flow
A retrieval-backed extraction layer that grounded model output against client-approved reference material and routing rules before values reached analysts.
Analyst review workspace
A web interface for confidence-based review, source highlighting, exception handling, and approval before data was synced into the ERP environment.
Delivery approach
The key was to reduce operational risk early rather than trying to perfect everything at the end.
Workflow audit
Mapped the highest-volume document flows first and defined where automation would save the most analyst time immediately.
Golden dataset
Built an evaluation set across languages and document types so the team could measure accuracy against real production cases from week one.
Pipeline iteration
Shipped extraction and review flows in controlled slices instead of attempting a full big-bang rollout across all business units.
ERP integration
Integrated validated outputs into the client ERP workflow while preserving approval checkpoints for edge cases and exceptions.
Ops enablement
Designed the review workspace so internal analysts could trust the system, resolve exceptions fast, and adopt the platform without heavy retraining.
Production launch
Rolled out with monitoring, accuracy dashboards, and a clear feedback loop for prompt and retrieval improvements after go-live.
Outcome and business impact
The project succeeded because the AI system was tied directly to operator workflows, not isolated as an experimental side tool.
87% less manual processing
Analysts spent dramatically less time copying, checking, and reconciling documents, freeing capacity for exception handling and higher-value operations work.
99.2% production accuracy
The combination of retrieval grounding, validation rules, and review flows produced high-confidence outputs that the client could operationalize quickly.
Faster rollout across teams
Because the platform was modular, the client could onboard additional document flows after launch without rebuilding the core system.
Planning an AI operations platform?
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