manufacturing · inquiries, documents, control

A pricing request sits for three days while the customer is already getting quotes elsewhere

This isn't about machines and it isn't about cameras on the line — we don't do that. It's about the loop around production: incoming inquiries and pricing requests, drafting commercial quotes from your product catalog, parsing specs and technical requirements, sales-team call review, shipment and inventory summaries, an assistant for regulations and technical documentation. Automation roadmap — ₽0 and 14 days.

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Case: industrial equipment supplier: conversion +27% in 2 months
Case: sales-department oversight: from 10 hours to 30 minutes a week
Automation roadmap: ₽0 and 14 days, no obligations

manufacturing · inquiries, documents, control — AI implementation diagram

01Sound familiar?

While the shop floor runs on schedule, the sales office lives in a backlog

On Monday a request lands in the inbox: a PDF spec sheet with eighteen line items, some part numbers foreign, some described in words as "equivalent." Answering it means parsing the document, matching items against your catalog, checking two disputed points with the engineer, calculating lead time against current load, and assembling all of it into a commercial quote. In practice it gets picked up on Thursday. By Thursday the customer already has two quotes from other suppliers.

Meanwhile a manager answers the same questions every day: is this item in stock, what's the manufacturing lead time, did we already ship to this customer and at what price. The answer lives in the ERP, in email, and in the procurement clerk's head — three different places, none of them fast. Each of these questions costs not half an hour but a shift in attention, after which the calculation starts over.

Calls are a separate story. They're recorded but not reviewed: going through a week of the department's conversations takes a full working day. So they get spot-checked, usually after a deal has already fallen through. What the manager promised on lead time, whether the price was quoted before terms were agreed, whether the callback ever happened — it all comes out after the fact, when the customer calls in angry.

And none of this loop shows up in reports. Production reports on output, the warehouse on inventory, but nobody tracks "how many pricing requests we didn't get to." These inquiries don't register in the funnel as a loss — they simply never get logged.

02The cost of inaction

How much are you losing while you read this page

Don't calculate "how much AI costs" — calculate the cost of an area where a pricing request sat for four days and the quote went out after the competitor's. Plug in your own numbers: how many requests come in per day, what share goes unanswered in the first 24 hours, and what an average order is worth to you.

Requests per day 30

Lost before a reply 50%

Average order value 70,000 ₽

Lost revenue per month — per year — —

A rough estimate based on your inputs — we calculate exact numbers on a free consultation.

Get this money back

03How it will look

Your Monday, once requests stop piling up in email

You open the summary in the morning and see not "40 emails to sort through" but a list of ready drafts. Nine requests came in over the weekend: AI parsed the spec sheets for seven of them, matched items against your catalog, pulled in prices and customer history, and put together a draft quote — the manager just has to check two disputed part numbers and hit send. Two requests are flagged separately: non-standard tolerances that need the engineer, who already sees the task with a spec breakdown attached. Next to it, a shipment and inventory summary with no trip to the ERP required. Below that, a review of yesterday's calls: three deals at risk, two where the manager quoted a lead time that isn't in the production schedule. You go through all of it over coffee, instead of finding out on Friday after the customer's already gone.

Now

Pricing request in the inboxpicked up on day three or four, quote goes out after the competitor's

18-line spec sheetmanager matches it against the catalog by hand for half a day

"In stock? What's the lead time?"answer gets tracked down in the ERP, email, and the procurement clerk, ten times a day

Sales-team call recordingssit unreviewed, get listened to after a deal falls through

With AI

Pricing request in the inboxparsed in minutes, draft quote waits for the manager to check it

18-line spec sheetitems matched automatically, disputed ones flagged separately

"In stock? What's the lead time?"assistant answers from your own data, manager isn't interrupted

Sales-team call recordingsall reviewed, risks and policy breaches surfaced by morning

04How it works

Three steps, and you don't write a single line of code

01 · Step 1 · 14 days, ₽0
Automation roadmap
We look at the commercial and document loop: how pricing requests come in, how long a quote takes to prepare, where your catalog, prices, and inventory live, what happens to call recordings. We calculate, in hours and rubles, how much each area is costing you. We flag what AI will take over entirely, what it'll only start, and where it isn't needed — and we say so plainly. From there the decision is entirely yours, no obligations.
02 · Step 2 · pilot
One area on live traffic
We take your highest-value area — usually handling incoming requests and drafting quotes, or reviewing sales-team calls. We connect to your sources: email, CRM, catalog and price exports. We configure rules to match your specifics: tolerances, equivalents, markups, lead time against current load. The metric is locked in before launch so there's something to compare the result against, and we launch on part of the flow under your oversight.
03 · Step 3 · full loop
Several areas and ongoing management
We add adjacent tasks: parsing specs and technical requirements, shipment and inventory summaries, an internal assistant on regulations and technical documentation for the shop floor and procurement. We connect it to your CRM and accounting system so data doesn't get retyped by hand. From there we keep monitoring quality, retrain on accumulated quotes and conversations, adjust the rules as your catalog changes, and stay responsible for the system's uptime.

05Proof

Quotes went out on day four, and deals went to whoever answered first

Edemium case study · industrial equipment supplier

A supplier of industrial equipment with its own assembly line. Inquiry flow was steady, the product catalog was wide, and the sales team was small and stretched thin. There was no shortage of leads, but the sales plan kept missing.

The problem wasn't traffic or price. A request would come in by email with a spec sheet, the manager would put it off until a gap between calls, the gap wouldn't come, and the quote would go out on day three or four. By then the customer already had two proposals in hand from other suppliers and was talking to whoever answered first. From the outside, it read as "weak salespeople."

The other half of the losses wasn't visible at all. Calls were recorded but nobody had time to review them: listening through a week of the department's conversations cost the owner about ten hours, so he spot-checked, usually after a deal had already fallen apart. What had been promised on lead time and which objections kept repeating call after call — nobody tracked systematically.

The owner went into the project cautiously and set one condition: AI sends nothing to the customer on its own. It prepares a draft, the manager checks it and sends it — accountability for the number on the quote stays with the person.

We built two areas. The first parses incoming requests: pulls line items out of the spec sheet, matches them against the catalog, pulls in prices and customer history, and assembles a draft commercial quote. Disputed items — non-standard tolerances, missing equivalents — get flagged separately and go to the engineer with a ready-made breakdown. The second reviews every call recording and gives the owner a morning summary: where a manager didn't call back, where a quoted lead time missed the production schedule, where a deal is at risk.

Two months in, conversion from inquiry to deal rose by twenty-seven percent — mainly because quotes started going out the same day as the inquiry instead of on day four. Sales-department oversight for the owner dropped to thirty minutes a week from ten hours: he reads the summary and listens selectively to the three or four conversations AI flagged.

"I thought we were losing on price. Turns out we were losing on how fast the quote went out."

This is a real case; the company's name is withheld under NDA. Your numbers depend on how long quote preparation currently takes, how formalized your catalog and pricing rules are, and in what form requests arrive. On the automation roadmap we calculate this on your own data before work begins.

I want the same

06While you're thinking it over

What happens while this stays "after the season"

Nothing dramatic — and that's the trap. A request answered on day four leaves no trace: it isn't flagged as lost in the CRM, it simply never became a deal. The engineer who's spent three years answering the same tolerance questions doesn't complain — he's used to it. Call recordings pile up by the terabyte and never get reviewed, so the same objections get mishandled for years and nobody sees it. Meanwhile the buyer on the other side compares suppliers on two things: price and response speed. On price you're bound by your cost base, and there's almost no room left there. On response speed the gap is enormous, and it doesn't close by hiring another manager — it closes when handling incoming requests stops being manual work. A year from now, the competitor who answers on the day of the inquiry won't be beating you on equipment quality — they'll be beating you on the accumulated difference in how many quotes went out on time.

07I know what you're thinking

Honest answers to the biggest doubts

We need AI on the line, not on paperwork

Then we're not the right fit, and it's better to say so upfront. Computer vision on a conveyor, predictive maintenance for machines, defect detection — that's a separate engineering discipline with its own hardware and its own vendors; we don't work in it and won't take it on. Our zone is the commercial and document loop around production: inquiries, quotes, commercial proposals, spec sheets, calls, summaries, regulations. If the problem is product quality — that's not us. If it's why a pricing request sits for four days — that's us.

Our product catalog is too complex, AI won't handle it

Not on its own — that's true. Which is why it doesn't work "on its own": we connect it to your catalog export, price lists, and rules for finding equivalents, and it matches items against your own data, not against generic assumptions about the market. Whatever matches unambiguously goes into the draft. Wherever there's doubt — a non-standard tolerance, a missing equivalent, a disputed part number — it gets flagged and sent to a person, not guessed at. The more formalized your catalog is, the larger the first category gets; on the automation roadmap we give you an honest estimate of that share before work begins.

A mistake in the quote — and we're the ones who answer for it to the customer

That's exactly why, in the base setup, AI sends nothing to the customer. It prepares a draft quote, the manager checks it and sends it — that's precisely how it's set up in the industrial-equipment case, and it was the owner's condition. Price, lead time, and any commitment always pass through a person. The gain isn't that the quote gets built without people — it's that the person starts not from a blank page and a PDF to parse, but from a ready table with the disputed items already flagged.

Our data and drawings will end up somewhere outside our control

That gets discussed before the start and written into the contract. We work with models that can be deployed within a Russian-hosted environment, with isolated data access; NDAs are signed before we see the first spec sheet. Under Russia's 152-FZ, personal data from the CRM and call recordings is processed within the consent you already have in place. On the automation roadmap we spell out, as a separate item, exactly where each piece of data goes — before you decide anything.

We already tried a CRM, it didn't help much

A CRM stores data, it doesn't do the work. It won't parse a PDF spec sheet, won't match items against a price list, won't assemble a draft quote, and won't listen to forty calls overnight. So the cards get filled in halfway, and half the real work lives in email and in the manager's head. We're not adding another system that needs manual data entry — we connect to what you already have and remove the manual steps. If it turns out your underlying records aren't in order, we'll say so at the review stage: order first, AI second.

People will have to be let go, the team won't accept this

In manufacturing it's usually the opposite problem: not too many people, but too few hands in sales and engineering. AI takes over document parsing, item matching, and call review — work that already gets squeezed into leftover time and done poorly. Managers end up handling more requests, not becoming redundant: in the industrial-equipment case, headcount didn't change — response speed did. We implement together with the team, on their real documents, not as something handed down from above.

✓

A risk-free pilot for you

We launch a pilot in 2 weeks. You set the success metric yourself. If you don't see results on it, we refund the pilot. The risk is on us, not you.

08Cost

What it costs, and when it pays off

An engineer and a sales manager: calculating quotes between other tasks, answering on day four, matching items against the price list by hand, and never even getting to half the requests. Salary, taxes, vacation — every month. AI loop: parses an incoming request in minutes, assembles a draft quote from your catalog and rules, pulls up customer history, reviews every call, no days off. The pilot is a one-time cost, not a salary.

Consultation

0 ₽Process review + ROI estimate

Funnel review and loss points
Lost revenue calculated in rubles
An honest read on where AI will pay off

Get it free

StartScenario pilot

from 90,000 ₽Payback in 2–4 months

from ₽90,000 — pilot: one area (handling incoming requests with a draft quote, or reviewing sales-team calls), connection to your email, CRM, and catalog export, launch on live traffic under your oversight, and a metric locked in before launch.

Launch a pilot

Turnkey

Custom quoteFull stack + ongoing support

from ₽250,000 — full implementation: several areas of the loop — inquiries and quotes, spec parsing, speech analytics on calls, shipment and inventory summaries, an assistant on regulations and technical documentation — tied into your accounting system and CRM, retraining on your own quotes and conversations, monitoring, and ongoing management.

Discuss your project

09FAQ

FAQ on AI for manufacturing

Do you implement AI directly into the production process — on the line, on the machines?

No, and that's a deliberate line we draw. Computer vision for quality control on a conveyor line, predictive maintenance from vibration and temperature data, defect detection — that's a separate engineering discipline: industrial cameras, sensors, integration with process-control systems. We don't do that, and we won't claim we do. Our zone in manufacturing is the loop around the shop floor: incoming inquiries and pricing requests, drafting commercial quotes from your product catalog, parsing specs and technical requirements, speech analytics for sales-team calls, shipment and inventory summaries, an internal assistant for regulations and technical documentation. If your problem is product quality on the line, it's more honest to say so upfront than to take it on and not deliver.

Where do manufacturing companies usually start?

Almost always with one of two areas. First — handling incoming pricing requests: AI parses the email and the spec sheet, matches line items against your product catalog, pulls in prices and the customer's history, and assembles a draft quote, and a manager checks it and sends it. Second — reviewing sales-team call recordings, once a manager realizes that monitoring eats a day a week and therefore never happens. The choice comes down to money: if inquiries are being lost, start with the first; if execution and manager promises are falling through, start with the second. On the automation roadmap we run the numbers on both options using your own figures and show which area pays back faster.

How does AI parse a spec sheet that arrives as a scan or in an unfamiliar format?

Text-based PDFs, Excel files, and inline email text are parsed directly. Scans and photos go through recognition, and quality here honestly depends on the source: a clean scan reads well, a creased copy with a stamp over the table reads worse, and lines like that get flagged for review rather than guessed at. Unfamiliar formats and unfamiliar part numbers are a routine situation, so matching runs on your own rules for finding equivalents, and anything ambiguous goes to a person with a flag on it. We estimate the share of line items your flow will resolve automatically using your real documents, before work begins.

Do we need to change our ERP or accounting system?

No. We connect to what you already have — reading catalog, price, and inventory exports, working with email and CRM through an API or scheduled exports. There's no need to rewrite your accounting setup, change its configuration, or migrate to another system: the AI layer sits alongside it and reads your data rather than replacing its storage. The one condition is that the data has to be available in a machine-readable form. If your catalog and prices currently live only in people's heads and scattered files, we'll say so at the review stage: basic order first, automation second.

What exactly does reviewing sales-team calls give us?

AI transcribes every recording and reviews it against your own criteria: whether the inquiry got a callback and how fast, whether the manager quoted a deadline and whether it matches the production schedule, whether the price was mentioned before terms were agreed, what objections came up and how they were handled. In the morning the manager gets a summary and a short list of calls worth listening to personally. In the case study with an industrial equipment supplier, this cut department oversight from ten hours to thirty minutes a week. The key difference from spot-checking: every call gets reviewed, not just the three someone had time for, so recurring systemic mistakes become visible.

How long does this take from the start to the first result?

The automation roadmap takes 14 days and costs ₽0: we review your processes, calculate losses, and show you a payback estimate. A pilot on one area is a matter of weeks, not six months: first we build the workflow on your real documents and recordings, run it against your archive so you can compare the result with manual work, then launch it on part of the live flow. You review the draft quotes and call summaries and tell us what's off — fixes happen along the way. We only scale up once you're satisfied with the result.

We have a small flow of requests — will this pay off?

It might not, and we'll say so at the review stage, before any money changes hands. A rough benchmark: if you get several dozen pricing requests a week, or preparing one quote takes hours of manual work, the area pays for itself within the first few months. If you get five requests a month and every project is a unique custom build, automating quote prep makes little sense, and it's more honest to start somewhere else — say, a technical-documentation assistant, or call review. On the automation roadmap we calculate payback on your own numbers and show you the math, rather than selling implementation to everyone regardless of fit.

Who's accountable for the result, and what if it doesn't work?

You set the success metric yourself before the pilot starts — quote turnaround time, share of requests answered within 24 hours, conversion from inquiry to deal, hours spent on department oversight. We record its current value before work begins, so there's something to compare the result against. If the pilot shows no movement on your metric, we refund the pilot fee. That's a standard condition, not a promotion — it's not in our interest to take on an area that won't deliver, so we screen those cases out during the free review.

Be the first in your niche to answer a request the same day it arrives

In industrial procurement, the choice is rarely decided by presentation quality. Buyers compare suppliers on price and on who sent a clear quote first. On price you're bound by your cost base and almost always land next to your neighbors. On response speed the gap between companies right now is huge: for most, a request sits in the inbox until a manager has a free hour. This won't produce a dramatic revenue spike — it removes losses that don't show up in your reports, because an unhandled request is never flagged anywhere as a lost deal. Whoever closes this gap first in their niche gets an edge the competitor won't see, and so won't be able to copy.

I want to be first in my niche

10Connection point

Let's break down your inquiry flow for free

14 days, ₽0, no obligations. We look at how pricing requests come in, how long quote preparation takes, and what happens to call recordings. We show you on paper: which areas AI will take over entirely, which it'll only start, and where it isn't needed. If we see the issue isn't AI but disorganized catalog and record-keeping, we'll say so right at the review — not after you've paid.

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