We built a generative AI assistant that lets engineers and service teams search manuals, maintenance guides, troubleshooting notes and service history by asking everyday questions.
Industry: Manufacturing & EnergyService: AI & AutomationUpdated
Overview
The challenge
Technicians and support teams worked with product manuals, installation guides, maintenance documents, service notes and troubleshooting records. The information existed, but finding the right section in the middle of a job could be slow.
Newer technicians also leaned on experienced colleagues for answers that were already written down somewhere in the company's technical knowledge.
Teams paged through long manuals while a customer or a field job waited.
Newer team members often asked senior technicians for information that was already documented.
Manuals, service notes and internal fixes were stored in different places.
The solution
We connected product manuals, maintenance guides, service notes, troubleshooting documents and approved technical knowledge.
The RAG layer finds the sections most closely related to the equipment, symptom or question at hand.
Generative AI turns the retrieved content into clearer steps, with the original manual or note still available.
Before and after
Before
Paging through documents
Technicians worked through contents pages and PDFs.
After
Question-based search
The assistant returns the relevant technical sections.
Before
Ask senior staff
Harder problems often meant an internal call for help.
After
Guided knowledge
Documented fixes are easier to reach without that call.
Before
Read whole manuals
New technicians learned from long documents.
After
Explanations for the task
AI explains information in the context of the current job.
Key features
Teams ask equipment and service questions in everyday language.
Business impact
Faster access to the right manual
The AI retrieves the relevant manuals and service notes before it suggests a possible next step.
Business impact
More useful context on site
Long procedures become clearer steps, with links back to the original source.
Business impact
Easier to use in the field
Results
Faster Field Support
Technicians find the relevant technical information faster during a job.
Before: Manual LookupAfter: Question Search
Less Routine Escalation
Newer staff find documented answers on their own.
Before: Frequent EscalationAfter: Self-Service Knowledge
More Verifiable Guidance
Technicians can check the manual or note behind an answer.
Before: Memory BasedAfter: Source Grounded
Faster delivery
Tested components handled the common groundwork, so technicians had the assistant sooner and we spent our time on equipment-specific search and step-by-step answers.
Our tested access layer kept internal fixes and service records limited to the technicians and support staff allowed to see them.
A proven retrieval layer indexed manuals and service notes so a question about a symptom finds the matching procedure.
Ready-made sync connectors added new service notes and updated manuals to the assistant as they were written.
Existing quality monitoring flagged weak answers so the team could check them against the manuals.
Trust and control
The assistant follows existing access rules, so each technician sees only the documents they are cleared for.
ACCESS CONTROLLEDApproved technical content is retrieved before an answer is generated, which reduces guesswork.
RAG GROUNDEDAnswers can point to the manual section or service note behind them, so technicians can confirm the procedure.
TRACEABLEContent can be refreshed as products, procedures and service guidance change.
UPDATE READYTechnicians ask about a piece of equipment, a symptom or a procedure in everyday language. The assistant retrieves the matching sections from manuals, maintenance guides and service notes, then explains a possible next step. The original manual or note stays linked, so the technician can confirm the procedure.
Newer technicians can now find documented answers on their own instead of calling experienced colleagues. Much of what they asked about was already written in manuals, service notes or troubleshooting records. The assistant brings that knowledge together and turns long procedures into clearer steps for the job in front of them.
Answers are built from the company's own approved technical content: product manuals, installation and maintenance guides, service notes and troubleshooting records. The RAG layer retrieves those sources before generative AI writes anything, and existing access rules still apply. Content is refreshed as products and procedures change.
If technicians lose time searching PDFs or calling colleagues mid-job, we can help you build an assistant that answers from your own manuals and service notes.