When your back office runs on a vendor's tribal knowledge, you don't own your own process
The Challenge
E-commerce and retail operators often depend entirely on a third-party outsourced team for a core back-office operation, matching inbound shopping lists arriving as images, PDFs, spreadsheets, and other unstructured formats, to an internal product taxonomy. The outsourced team accumulates all the institutional knowledge, creating vendor lock-in and fragility. Turnaround exceeds 24 hours per batch, degrading the customer experience, and there's no visibility into how accurate the manual work actually is.
Any company with a significant outsourced or offshore cost center performing repetitive document-to-catalog matching carries the same exposure. The business also can't improve a process it doesn't own: it can't see error rates, can't change turnaround, and can't build new real-time features on top of a 24-hour batch. Contract renewals become negotiations from weakness, because the vendor holds the knowledge.
- Vendor lock-in — the outsourced team holds all the institutional knowledge for a core process, so every contract renewal is negotiated from weakness.
- Slow by design — a 24-hour-plus batch turnaround degrades the customer experience and rules out any real-time feature built on top of it.
- No visibility into quality — the business has no way to measure how accurate the manual matching actually is, until it looks.
- A process you don't own — error rates, turnaround, and improvement are all in someone else's hands, not the business's.
The Approach
A custom generative AI pipeline automates the workflow end to end, from ingesting unstructured inputs to outputting items mapped against the standard product taxonomy, using a mix of models for classification, text extraction, and intermediate steps. Evaluation infrastructure is established early to measure accuracy objectively and guide iteration; a blind side-by-side comparison proves the AI outperforms the outsourced team before go-live, which also reveals how inaccurate the manual process had actually been.
A confidence-scoring layer flags low-certainty outputs for human review, and a feedback loop saves corrections back to the database so the system improves automatically over time. An optional QA layer is retained in year one and phased out as confidence grows. Ownership of the process, the accuracy data, and the improving model returns to the business, which can now build real-time features on top of a 30-second turnaround that were impossible with a 24-hour batch.
Multi-model document classification · Text extraction · Taxonomy mapping · Confidence scoring · Human-in-the-loop feedback retraining
AI Role
AI classifies unstructured inbound inputs and maps them to the company's internal product taxonomy end to end, with a confidence-scoring layer flagging low-certainty outputs for human review and a feedback loop saving corrections back to improve future accuracy automatically. AI Automation Scope: Full unstructured-input-to-catalog matching workflow, replacing an entire outsourced processing function.
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