Lowbrow Customs is a leading supplier of custom and chopper motorcycle parts, serving enthusiasts from the northeastern United States. The company carries unique products sourced from small shops worldwide, alongside its own proprietary innovations.
As the product catalog grew, so did the challenge of keeping support staff current on specifications, compatibility details, and technical recommendations. Critical product knowledge had accumulated over years in email drafts and PDF documents. There was no centralized, searchable system for any of it.
CEO Tyler Malinky knew the company needed a better way to get that information into the hands of the people answering customer questions every day. The answer was a custom AI chatbot, trained on Lowbrow's own data, for internal use by the support team.
Lowbrow's support staff needed to deliver technically accurate product recommendations quickly. But the information they relied on was scattered across hundreds of email drafts and PDF files with no consistent structure or format.
A standard search tool wouldn't solve the problem. The data wasn't organized in any uniform way. File formats varied. And because Lowbrow sells motorcycle parts, the stakes were higher than a typical e-commerce operation. An incorrect fitment recommendation or product specification could have real consequences for rider safety.
The company needed a custom AI development partner that could build a solution capable of:
• Ingesting unstructured data from multiple source types, including emails and PDFs
• Delivering accurate product comparisons, specifications, and recommendations through natural language conversation
• Prioritizing accuracy over completeness. Returning no answer rather than a wrong one
• Operating as an internal tool for support staff, with the flexibility to expand later
This client chose Sketch over other options because of its high ratings, strong culture fit, quick communication, and multiple relevant examples of similar custom AI projects.
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Sketch recommended and implemented the full system architecture for a proprietary AI-powered knowledge assistant. The solution was built as an internal web application, designed to be intuitive enough that support staff could start using it right away.
The core of the tool is a natural language chatbot trained on Lowbrow's own internal data. Staff members ask questions conversationally, and the AI returns product comparisons, technical specifications, compatibility guidance, and recommendations. Everything the chatbot surfaces is drawn directly from the company's verified knowledge base.
Transforming Lowbrow's scattered knowledge into reliable AI source material was one of the most important parts of this engagement. Sketch built a secure PDF ingestion pipeline that handles both the initial data upload and ongoing refreshes. The pipeline performs structured extraction, normalization, and categorization so the AI has clean, well-organized source material to reference.
Email drafts containing product knowledge were also transitioned into a structured format that the AI could search and reference effectively. As a result, years of accumulated expertise became accessible in seconds instead of being buried in inboxes.
Because incorrect product recommendations could affect rider safety, the AI was configured to prioritize accuracy over completeness. The system uses conservative model settings and strong grounding in source data to minimize hallucinations. If the AI doesn't have a confident, data-backed answer, it says so rather than guessing.
Sketch also implemented additional guardrails and response safety mechanisms to keep outputs within the boundaries of verified product data. This safety-first approach is especially important for businesses where AI recommendations carry real-world consequences.
Related: See how Sketch built a safe, secure custom AI agent for a bank
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Sketch provided full documentation and knowledge transfer so Lowbrow's team could operate and maintain the system independently after the engagement ended.
Sketch assigned one software developer to the project, with a cloud engineer available for infrastructure and security tasks as needed. The engagement started in April and was finished in May 2026, following Sketch's iterative development methodology with regular demos, planning sessions, and biweekly reviews.
If your team's product knowledge is stuck in documents, emails, or other scattered sources, Sketch can build a custom AI solution to make it searchable and useful. Whether you need an internal knowledge assistant, an AI-powered support tool, or something else entirely, every engagement starts with a free conversation.
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