Every large business is holding its breath.
Behind slick client portals and dashboard interfaces, teams of operators are doing manual translation: copying data from an incoming PDF email, restructuring it in Excel, verifying it against a legacy ERP, and submitting it. It’s operational drag. It’s slow, error-prone, and expensive.
We recently embedded with a mid-market shipping and logistics client. Their legacy ingestion pipeline was their primary bottleneck: custom manifests from international partners took up to 6 hours to parse, validate, and enter.
The Advisor’s Answer
Before we arrived, a traditional consultancy spent three months auditing the process. Their slide-deck recommendation: a multi-million-dollar, two-year “data lake modernization project” that would force all international partners onto a single unified API.
This is the standard consulting trap. It asks the world to conform to a slide deck. The partners won’t change; the lake will remain dry.
Our Cut
We didn’t write a slide deck. We embedded.
We spent four days sitting with their intake operators to map the edge cases. Then we built a lightweight, event-driven extraction engine:
- Intake Trigger: An incoming manifest triggers a serverless function.
- Deterministic Extraction: A light OCR layer extracts structural coordinates.
- Agentic Schema Translation: A specialized, locally-hosted LLM translates raw partner text into the client’s internal ERP schema. It doesn’t guess; it validates against database foreign keys.
- Human-in-the-loop Exception Queue: If confidence falls below 98%, the record is routed to a clean interface for a human operator. The operator’s corrections feed back into the system.
# A simplified look at the schema validation cut
def validate_manifest(raw_payload, schema_template):
translated = llm_agent.extract(raw_payload, target_schema=schema_template)
errors = schema_template.validate(translated)
if errors:
return route_to_operator_queue(raw_payload, errors)
return write_to_erp(translated)The Result
Processing time went from 6 hours to 4 minutes.
Error rates dropped by 91%. More importantly, the system runs entirely inside their AWS environment. They own the code. There is no vendor seat-license tax.
The cut has held. The friction didn’t grow back.