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.
Enterprise AI platform 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 multilingual document processing problem
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. Together they make a knowledge base of roughly 2,500 documents — about 15,000 pages, or 10–15 million tokens.
5 operating languages
The platform had to handle multilingual documents without forcing the client into a separate workflow per region or per business unit.
A corpus that moves daily
The knowledge base is reindexed every day. That interval, more than the size, is why this is a retrieval system rather than a fine-tuned model: a training run against a corpus this fresh is out of date the morning after it finishes.
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.
A RAG pipeline, review workspace and ERP integration
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.
How the AI platform was delivered and evaluated
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.
Results: 87% less manual processing, 99.2% accuracy
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.
1.9s average, to the last token
End-to-end response time in production, with a 4.2s p95 — about 0.3s of retrieval and 1.5s of generation, the remaining tenth elsewhere in the pipeline. Measured to the last token rather than the first, which is the harder of the two numbers to publish.
Faster rollout across teams
Because the platform was modular, the client could onboard additional document flows after launch without rebuilding the core system.
How much does an AI platform like this cost?
The published band, and what moved this build inside it.
A full AI product of this shape — retrieval pipeline, frontend, backend, integrations and the evaluation work a demo does not need — runs $40,000 to $120,000 and ships in 8–12 weeks.
What decided the position inside that band here was not the model. It was fourteen disconnected sources to normalise, five operating languages in the corpus, and an auditability requirement that made a golden dataset and a validation layer non-optional rather than nice to have. Corpus heterogeneity is almost always the variable that moves an AI quote, which is why the first question in scoping is about your documents rather than your model choice.
The running cost is a separate number, and it is small. This system averages about $0.011 per answer — roughly $0.002 of retrieval and $0.009 of generation — across about 40,000 queries a month. The split is the part worth carrying to your own case: the retrieval infrastructure is the cheap fifth, and what retrieval actually costs you sits inside the generation line, as the extra input tokens every retrieved passage adds to every prompt. Context length is the cost lever; a cheaper vector database is not.
The evaluation harness is the line item most often removed to hit a number, and removing it is how a system that demos well becomes one nobody trusts in production. 99.2% accuracy is a measurement, and it exists here because something was built to measure it.
What is deliberately not claimed: that the system is more accurate than the people it took work from. Nobody measured the manual process before it was replaced, so there is no comparable before figure and none is published. 99.2% is what this system scores, not how much it improved on anything. The improvement that is measured is the workload: 87% less manual handling. The lesson generalises — baselining the old process costs an afternoon before the build and cannot be bought back afterwards.
Every band we publish · What inflates an AI quote · RAG or fine-tuning?
Common questions
The two asked most often after the numbers above are read.
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