A global industrial automation manufacturer maintained its product catalog in a legacy product information management system. The data stored there was structured for operational purposes, not customer communication. Field names were abbreviated, descriptions were terse or absent, and the format reflected decades of data entry conventions rather than any coherent content strategy.
The organization was modernizing its web presence and needed customer-facing product descriptions that were readable, accurate, and suitable for marketing. Writing these summaries manually for hundreds of thousands of SKUs was not feasible within the project timeline or budget.
PiSrc used AI-powered summarization to generate product descriptions from the raw data fields available in the legacy system. The AI model ingested the structured attributes of each product (specifications, classifications, application codes, dimensional data) and produced a natural-language summary that communicated the product's purpose, key features, and relevant specifications in a format appropriate for a website audience.
This was not a simple reformatting exercise. The AI needed to interpret abbreviated field values, infer relationships between attributes, and produce prose that a non-technical buyer could understand while remaining precise enough for an engineer evaluating a purchase.
Given the technical nature of the products, accuracy was non-negotiable. A product description that overstated a voltage rating or mischaracterized an operating temperature range could have real-world safety implications. PiSrc implemented several layers of protection.
Output constraints. The AI was configured to generate content only from the source data provided for each product. It was explicitly prohibited from inferring specifications not present in the input data or drawing on general knowledge to fill gaps. If a data field was missing, the summary omitted that attribute rather than guessing.
Factual anchoring. Every claim in the generated summary was required to trace back to a specific field in the source data. This made review efficient because reviewers could verify each statement against the input record.
Terminology control. A controlled vocabulary ensured that product categories, safety certifications, and technical terms were rendered consistently and correctly across all generated content.
Mandatory review workflow. Auto-publish was disabled. Every generated summary entered a review queue where subject matter experts could approve, edit, or reject the content before it went live. The workflow tracked revision history and approval status, providing an audit trail.
Escalation paths. Summaries flagged by the system as low-confidence (due to sparse input data or ambiguous attribute values) were routed to senior reviewers rather than the general queue.
The AI summarization pipeline produced draft content for the full product catalog. The review pass rate on first submission was high, with most summaries requiring only minor editorial adjustments rather than substantive corrections. The combination of constrained generation and human review eliminated hallucinated content from the published output.
The project delivered in a fraction of the time and cost that manual copywriting would have required. Equally important, the workflow established a repeatable process: as new products are added to the catalog, they enter the same pipeline automatically.
The approach demonstrated that generative AI can be deployed responsibly in high-stakes technical content when the right constraints and review processes are in place. The key was treating AI as a draft generator within a governed workflow, not as an autonomous publisher.
