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Optimized Semantic Search: AI-Powered Content Discovery for Handcrafting Platform

MNDA

Enterprise/Corporation
Company Type
Salt Lake City, United States
Location
Team augmentation
Engagement Model
1 - 5 people
Team Size
3 - 6 Months
Duration
$100K - $150K
Budget

About

We engineered a custom-tuned AI semantic search engine for a leading handcrafting hardware company in the US. This solution transforms how users discover personalized image content for their craft projects, replacing manual tagging processes with AI-powered understanding, resulting in dramatically improved search experience and 30% higher conversion rates.

Challenge, approach, and impact

Manual Content Tagging Bottlenecks

The company's subscription-based platform required manual tagging of each image, taking several minutes per asset and requiring a specialized team. This process became a significant bottleneck that limited content scalability and created operational inefficiencies.

Inconsistent Data Quality and Search Experience

Manual tagging processes provided no guarantee of data consistency, leading to poor search experiences. Users struggled to find relevant content for their handcraft projects, resulting in friction and reduced subscription renewal rates.

Domain-Specific Search Requirements

Users needed to search for highly specific crafting terminology, copyright content, character names, and nuanced project requirements that generic search engines couldn't understand, requiring specialized domain knowledge integration.

Scalability Challenges with Growing Content Volume

With 100M+ queries per month and a rapidly expanding content library, the existing search infrastructure couldn't scale effectively while maintaining search quality and response times under 50ms.

How we built

AI & ML Solutions
E-Commerce

Testimonials

Anonymous

Pento.ai

Verified Testimonial

This semantic search project was incredibly rewarding to work on. The technical challenges were fascinating - from understanding crafting terminology and user intent to building custom AI models that could capture nuanced domain knowledge. We had to tackle complex vector search optimization, scale issues, and real-time inference challenges. What made this project truly exceptional was the collaboration with the client team. Their communication was excellent throughout the entire process, and they provided deep domain expertise that was crucial for training our models. The client's technical team was highly engaged and supportive, making it a pleasure to work through the various challenges together. We truly appreciated the level of collaboration and technical depth the client brought to the table.

Team structure

Client team

Andrew G's avatar

Andrew G

Director SW Engineering

Daily point of contact

Sriram A's avatar

Sriram A

VP SW Engineering

Project stakeholder

The client stakeholders were working closely with the team at Pento.ai

Agency team

3 x Machine Learning Engineer's avatar

3 x Machine Learning Engineer

Production

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