A mid-sized European supplier of industrial equipment - serving bakeries, confectioneries, and food processing plants across the continent - faced a critical bottleneck: while their engineers excelled at sourcing and specifying machinery, the downstream product content process consumed disproportionate resources.

Compiling descriptions, maintaining translations across four markets, and ensuring technical accuracy required the equivalent of one full-time employee. With labor costs exceeding €80,000 annually and expansion into new markets stalling on content gaps, they needed a solution.

Through the Singularity Sandbox - a compact, 2-week AI innovation accelerator - they collaborated with our team to build a tailored solution that slashed preparation time from days to minutes.

The question they asked us: Can we automate product content without losing the technical precision our B2B customers expect?

The Business Challenge

In technical wholesale, product descriptions are the primary sales vehicle. Professional buyers expect precise terminology, complete specifications, and consistent quality across languages. But post-specification activities created disproportionate friction:

• Labor-intensive assembly — Product managers manually wrote descriptions, hunted for appropriate specifications, cross-referenced pricing, and maintained four language versions per product

• Scale pressure — At 4,000+ SKUs across machinery, spare parts, and services, content gaps multiplied with each market expansion

• Error and consistency risks — Manual translation led to terminology drift; a "Teigknetmaschine" became a generic "dough machine" instead of the precise "spiral dough kneader" buyers expected

• Legacy constraints — Data lived in spreadsheets, supplier catalogs, and institutional knowledge—ruling out simple automation

  
  

These issues mirror broader manufacturing trends where administrative overhead erodes margins and responsiveness.

The Singularity Sandbox Approach

Rather than a lengthy implementation, we ran a focused 2-week sprint. Domain experts (product managers, technical specialists) worked alongside our AI engineers in a secure environment with real data.

 Discovery (Days 1-3): Mapped workflow bottlenecks. Prioritized high-ROI targets: description generation, terminology consistency, batch processing, and translation automation.

Experimentation (Days 4-10): Built modular AI pipelines with three core innovations:

• Product Grouping Architecture — A machine and its spare parts share technical context. Edit the parent description, propagate to the family. What was 50 individual tasks became one coordinated workflow.

• Configurable AI Instructions — Different product types need different treatment. Main equipment gets capability descriptions; spare parts get compatibility specs; services get scope definitions. Rules encode domain expertise the AI follows.

• Structured Multilingual Pipeline — Generate in the primary language, translate with terminology enforcement. A custom dictionary ensures technical terms translate correctly across German, English, Spanish, and Russian.

Refinement (Days 11-14): Daily feedback cycles refined prompts and validation. Delivered a production-ready system with clean integration paths.

Results & ROI

Immediate Operational Impact:

• Reduced content preparation from ~40 hours per product batch to minutes

• Eliminated terminology inconsistencies across four market languages

• Achieved 100% catalog coverage for the first time

Financial Outcomes:

• Annual labor savings exceeding €80,000 by freeing product management capacity

• Total investment delivered payback in under 8 months

• Scalable to growing catalog without proportional cost increases

Strategic Benefits:

• Created a repeatable AI playbook for future automations

• Empowered product managers as "AI operators," not just reviewers

• Positioned the company to expand into new markets without content bottlenecks

The Takeaway

This project demonstrated that targeted AI acceleration can unlock exponential value in traditional industries - without lengthy implementations or heavy IT disruption.

The key was building a system that encodes how expert product managers actually think about their catalog - then letting them work at 4x the speed.

"What began as a costly content bottleneck became a streamlined competitive advantage. The Sandbox approach delivered not just automation, but lasting internal capability." 

This approach applies wherever technical product content needs to scale.
A few examples:

Industrial distributors - Complex catalogs with technical specifications

Manufacturing - Parts databases with compatibility requirements

Technical wholesale - Multi-market expansion with localization needs

Equipment suppliers - Product families with shared characteristics

If you are interested in unlocking AI value in your operations, contact us.