Quote requests arrive as spec sheets in each customer's format, and somewhere inside each one is the bill of materials. Quoting starts when those line items are in your template - so today, someone types them there. The ask: make that transfer automatic, reviewable and exact.
A request for quotation arrives as a spec sheet - a PDF or a spreadsheet, laid out however that customer lays things out - and somewhere inside it sits a bill of materials. Nothing can be priced until those line items are in the manufacturer's own template: item, make, part number, quantity, unit of measure.
So a person opens each file, finds the BOM, and moves it across by hand, cell by cell. Enquiries arrive faster than the people available to attend to them, which means the speed of quoting is set not by engineering judgment but by data entry.
Heard from an engineered-components manufacturer quoting into regulated industries. Paraphrased, like everything on this map.
Extracting a table sounds solved until the table is one of many, drawn in someone else's format, inside a scan. Four things separate this from the demo version of the problem.
We do not attach figures to demand signals - directional is the honest register, and these pools are expensive enough without decoration.
Every enquiry is a stack of files, and each file means a person walking a BOM into the quoting template cell by cell - skilled time spent on transcription.
The extraction runs in the tool; the person reviews, corrects the odd cell, and exports. The keyboard time goes.
A digit slips between the spec sheet and the template and nothing flags it. The error surfaces later, already priced into a quote.
Cell-level extraction with a review step catches the slip while it is still on screen - before it becomes a price.
Enquiries queue behind the data entry, and the quote leaves at the speed of the slowest transcription - while the customer is also waiting on your competitors.
The bottleneck moves from typing to judgment. The time goes into the price, not the paperwork.
Two engines run in production today - Analytical Lab Reports and account Knowledge Twins. Everything else on this map is an extension on the same foundation.
This entry is the Intelligent Document Agent mechanism behind Analytical Lab Reports, pointed at commercial documents. Find the table that is semantically a BOM. Read it cell by cell instead of guessing at a screenshot. Learn each customer's format once, as a reviewed profile, so every later file in that format lands the same way. Export into your template, exactly.
AI reasons over the document; your template and your review stay in charge of what ships. Nothing leaves the tool without a person having had the chance to correct it.
Read the full use caseIt runs inside your infrastructure, within your boundary. The spec sheets are your customers' confidential designs - they never leave it, and neither does your pricing logic. The freedom of action stays yours: which models do the reading, where the workload runs, what it costs to keep running.
Back to the AI ROI MapBring one enquiry - the PDFs and spreadsheets exactly as they arrived - and watch the line items land in your template. Or run the public exercise first and try to prove us wrong. No form, no gate.
On-prem. Your data never leaves your boundary.