A PE-owned e-commerce company was entirely dependent on a third-party BPO to perform a core back-office operation: matching inbound shopping lists — arriving as images, PDFs, spreadsheets, and other unstructured formats — to its internal standard product taxonomy. The offshore team had accumulated all the institutional knowledge for this process, creating deep vendor lock-in and fragility. Turnaround time exceeded 24 hours per batch, degrading the customer experience. Without change, the business would remain hostage to an expensive, slow, and opaque external dependency with no path to operational ownership or cost reduction.
Fractional AI designed and deployed a custom generative AI pipeline for a PE-owned e-commerce company — combining OpenAI for taxonomy classification, Google Gemini for text extraction, and Claude for intermediate processing, orchestrated on AWS with a confidence-scoring layer and feedback loop that allows the system to improve automatically over time.
Fractional AI began by establishing evaluation infrastructure before building the solution — a deliberate choice that allowed the team to benchmark accuracy objectively and demonstrate the AI's performance against the BPO baseline before go-live.
The pipeline was built on AWS to ingest the full range of input formats the client received: shopping lists arriving as images, PDFs, and spreadsheets with no standardized structure. Google Gemini handled text extraction across these diverse formats; OpenAI models performed the core taxonomy classification work; Claude handled select intermediate processing steps. Each model was chosen for the specific subtask it performs most reliably.
A confidence-scoring layer was built on top to flag outputs below a certainty threshold for human review, ensuring the system could scale without unacceptable error rates. A feedback loop saved human corrections back to the database, allowing the system to improve its accuracy automatically over time.
The engagement ran over 2–4 months. A parallel QA layer was retained at the client's discretion. The unexpected finding from the accuracy benchmarking: the BPO had been performing worse than assumed — a fact that had been invisible without a measurement system in place.
PE-owned companies with a significant BPO or offshore cost center performing repetitive, high-volume document processing or data mapping work — particularly operators who want to reduce a large recurring cost line and bring critical IP back in-house within a single fiscal year.
