AI-assisted concept narratives and proposal drafts
LiveCorehigh effect
Core capability
Engineers and teams can prepare requirements, reports, instructions, and other technical documents much faster, while spending less time searching through fragmented knowledge sources.
How it works
The user describes the needed output, and the system first gathers the most relevant internal and reference material before generating a structured draft in the expected style and format.
Application here
The system pulls together material from past proposals, standard templates, and capability statements and turns it into a polished proposal draft in minutes.
Business impact
Proposal turnaround drops from days to hours. Small teams can respond to more opportunities without growing headcount as quickly, while messaging stays more consistent across bids.
Limitations
Drafts still need human review for customer-specific nuance, competitive positioning, and factual accuracy. Final tone and claims should always be checked before sending externally.
In production
This is already useful for reducing the time spent writing engineering documents and searching through scattered technical knowledge.
Research
The next boundary is systems that can prepare much stronger first drafts while already taking standards, required references, and regulatory expectations into account from the start.
Examples
Siemens Industrial Copilot (with Microsoft Azure OpenAI) generates engineering documentation and proposal drafts; Schaeffler, an early adopter, reports panel visualisations created in 30 s with only 20 % manual refinement (Siemens press release, Oct 2024) — .