How One Shared AI Platform Powers Four Chatbots and an OCR Engine
See how ConforgeLabs built four retrieval-grounded AI chatbots and a custom OCR and rule-check engine on one secure, shared AI architecture.
International boiler company's teams across product support, field service, security governance, and engineering each depended on scattered documentation and manual cross-checking to get answers or validate data. ConforgeLabs built two connected AI systems on one shared architecture: a suite of chatbots giving teams instant, sourced answers on products, troubleshooting, and security policy, and a Custom OCR and Rule Check Engine that automates proof-reading of complex regulatory PDFs against ERP data.
Scattered Knowledge, Manual Document Checks
International boiler company's teams across product support, field service, and security governance each fielded high volumes of questions that depended on scattered documentation and individual expertise. Product manuals, FAQ sheets, and security policies lived in separate systems with no shared conversational layer, so getting one reliable answer often meant searching multiple systems or waiting on a subject-matter expert. At the same time, engineering and QA teams across business units were manually cross-checking complex, template-varying regulatory PDFs against ERP records before submission to the Boiler Regulatory Authority of India or internal audit. The process was slow and repetitive, and the regulatory stakes left no tolerance for extraction or validation error. Both problems shared a root cause: no shared automation layer existed to retrieve the right answer or validate the right data, so every team repeated the same manual pattern on its own.
“The shared architecture gave us a strong foundation to deploy multiple AI solutions without repeatedly solving the same authentication, infrastructure, and integration challenges. It made each subsequent use case significantly easier to operationalize.”
