Pricing Intelligent Automation
An intelligent-automation solution that improves pricing precision and throughput using a custom named-entity-recognition framework paired with RPA.
Pricing operations at a Fortune-class logistics enterprise relied on manual, error-prone steps for extracting and acting on structured signals from unstructured inputs — limiting both accuracy and throughput.
Conceived and deployed an intelligent automation and RPA solution for the pricing domain, built on a bespoke NER/NLP framework that pulled structured pricing signals from unstructured inputs and fed them into automated workflow execution.
Improved pricing precision and operational throughput by replacing manual extraction and processing steps with deterministic ML/NLP components and workflow automation.
The design principle here was using NLP for what it’s genuinely good at — extracting structured entities from text that humans wrote without structure in mind — and then handing off to deterministic automation for the workflow steps that followed. That boundary between probabilistic extraction and rule-based execution is where most intelligent-automation projects go wrong: models that should be tight classifiers get asked to make workflow decisions, and models that should be handling ambiguity get replaced by brittle regex.
Pricing is a domain where errors compound: a wrong extraction propagates through calculations, approvals, and quotes before anyone notices. Building the NER component with explicit confidence thresholds and human-review routing for low-confidence extractions was the reliability mechanism that made the automation trustworthy enough to deploy in production.