Outcomes
- Engineers and service staff find manuals, parts and troubleshooting information faster
- Repetitive document and data-entry work reduced in quotation and support workflows
- AI answers grounded in the company's own approved documents, with sources shown
- Confidential information kept under the company's control
Practical, workflow-first AI
Industrial companies hold large amounts of knowledge in manuals, drawings, service reports, emails and spreadsheets. Much of an engineer’s time goes into finding the right piece of information. NSPL combines automation domain knowledge with AI and data capabilities to address specific, measurable problems.
Typical engagements
- Technical knowledge assistant (RAG). A searchable assistant over approved manuals, application notes and service history that answers questions with citations to the source documents.
- Workflow automation. Extracting information from enquiries, drafting quotations or service reports for human review, and routing requests to the right person.
- Data and analytics. Bringing machine, service and commercial data together for reporting and decision-making.
How we work
- Discovery — business process, pain point, users, available data and documents, desired outcome.
- Pilot — a limited scope with agreed success measures and a human reviewing outputs.
- Rollout and governance — access control, monitoring, feedback and a plan for keeping content current.
Read How AI can search engineering manuals securely or book a discovery call.
Typical applications
- RAG knowledge assistant over manuals, service reports and parts lists
- Assisted quotation preparation from enquiries and product data
- Classification and routing of incoming technical enquiries
- Dashboards and analysis of machine, service and sales data
Frequently asked questions
What is a RAG knowledge assistant?
Retrieval-augmented generation (RAG) combines a search index over your own documents with a language model. When someone asks a question, the system first retrieves the most relevant passages from approved documents, then generates an answer from those passages and shows the sources. This keeps answers grounded in your content rather than the model's general knowledge.
Can AI be used on confidential engineering documents securely?
Yes, if the architecture is designed for it — access control that mirrors document permissions, choice of hosting and model provider with suitable data-handling terms, no use of your data for model training, logging, and a clear rule set for which documents are included. These are decided in the discovery phase.
How does an AI project with NSPL start?
With a short discovery that identifies one workflow, the people who use it, the documents or data available and what a good outcome looks like. A small, measurable pilot follows before any wider rollout.