← case studies
Published
How One Manufacturer Cut Diligence Waits 80% With AI
Exchangers' finance team rebuilt group numbers from every country ERP each time an advisor asked. An AI now maps the accounts in place, so week-long answers arrive same day and data work fell 60%.
80%
Faster diligence response
4–6 months
Implementation Time
$100K – $250K
Project Cost
the challenge
The group operated across multiple countries with a separate ERP in each. Some were mainstream systems, some were non-standard platforms with no supported path into a common reporting layer.
That left one version of the numbers per country and no reliable version for the group. Consolidation ran on spreadsheets and manual account mapping, rebuilt from scratch every reporting cycle. Any question from outside the company restarted that work.
A sale process put a clock on it. Because country totals never tied to group, the quality of earnings firm kept re-pulling revenue by entity rather than accepting it, and information requests took most of a week to answer.
What was missing was a single consolidated view that held up under questioning without being rebuilt each time someone asked.
what they built
Scaylor plugs into the systems a company already has. Nothing gets replaced, nothing gets moved. At Exchanger that meant reading from every country's ERP, including a few unusual ones that nothing else connects to.
Pulling the numbers out is easy. Deciding which numbers are the same thing is not. Every country names its accounts differently and organizes its business differently, so one cost can show up three ways in three places. Our AI agents read through the accounts and propose the matches. Someone on the finance team approves or corrects each one.
Then there is the part no system records. An ERP stores what was booked, never why it was booked that way. Our platform determines the reasoning behind those decisions so that the explanation sits with the number instead of in someone's head.
The company ends up with one set of numbers that stays current, where the country figures and the group figures always agree, and where each figure carries the reasoning behind it. When an advisor asks why a number looks the way it does, the answer is already written down.
Scaylor started with people rather than systems. Before touching any data, the team ran Business Objective Meetings, interviewing staff across the enterprise about why each account existed, why one country treated a cost differently, and why a given adjustment repeated. Those answers were stored as a tribal knowledge graph, a structured record of the reasoning behind the data. Inverting the usual order, interpret first and connect second, meant the accounts were mapped once instead of mapped, found wrong, and remapped.
Several country ERPs were locale-specific platforms with no off-the-shelf reader, so Scaylor built custom connectors rather than asking anyone to migrate or replace a working system. Data was transferred first, then cleaned and normalized against the approved mappings, with AI agents proposing cross-system account matches and a finance reviewer approving or correcting each one. The AI layer came last: the semantic and knowledge layers that let the model understand the structure and query it correctly. One constraint was deliberate throughout. The AI displays data and never alters it; it retrieves and returns figures rather than generating them. First meaningful results landed in the four-to-six-month range.
best fit for
PE-backed Industrials/Manufacturing companies that are doing more than $50m EBITDA.
Ai ROLE
Architected cross-ERP querying by matching accounts across systems • The model resolves a single question across ERPs that were never built to talk to each other. It reads account names, descriptions, and transaction patterns in each country's system and matches the equivalent accounts, with a finance reviewer approving or correcting each match. A query then runs against those matches, so one question returns one answer drawn from every system at once, with the model retrieving and presenting the figures rather than generating them.
impact
80% faster diligence response.
Diligence requests answered the same day instead of the following week.
60% reduction in finance team time spent on data work.
Reduction in finance team hours spent on manual data reconciliation.
First-pass acceptance on entity-level revenue
Eliminated repeat data pulls by the quality of earnings firm.
implementation complexity
1. Business Objective Meetings, before touching any data. We started with people, not systems. We ran what we call BOMs, interviewing staff across the enterprise about why things are set up the way they are: why an account exists, why one country treats a cost differently, why a given adjustment repeats. Those answers were stored as a tribal knowledge graph, a structured record of the reasoning behind the data.
This ordering was the first real decision. The usual instinct is to connect systems first and interpret later. We inverted it, because you cannot correctly map two accounts to each other without knowing why each one was created. Interpreting first meant we mapped once instead of mapping, discovering we were wrong, and remapping.
2. Connectors for the unusual systems. Several country ERPs were locale-specific platforms with nothing off the shelf to read them. We built connectors rather than asking anyone to migrate or replace a working system.
3. Transfer, then clean. Data came across first, then went through cleaning and normalization against the mappings.
4. The AI layer. Last, we built the layers that let AI understand the structure and query it correctly.
The key constraint there was deliberate: our AI can display data, never alter it. It reads and returns, it does not generate figures. That is what makes the answers trustworthy rather than plausible.
Shaheer Khan
Co-founder and COO of Scaylor, which unifies enterprise data across systems never built to talk to each other. Previously co-CEO of MethodEASY, an AI e-commerce operations platform.
Get an intro
Talk to this teamindustry
Manufacturing & Industrial
business organization
Finance & Accounting
AI TYpe
Data Synthesis & Reporting
value type
Time Savings
frequently asked questions
How did a PE-backed manufacturer answer diligence requests 80% faster with AI data consolidation?
Exchangers Industries ran a separate ERP in each country, so every advisor question meant rebuilding group numbers by hand. The experts connected each ERP in place, used AI agents to match equivalent accounts across systems with finance-team approval, and captured the reasoning behind each booking alongside the figures. Diligence requests that took most of a week were answered the same day, an 80% improvement.
What AI tools and approach were used to consolidate data across multiple ERPs?
The approach was data synthesis and reporting: AI agents read account names, descriptions, and transaction patterns in each country's ERP and proposed matches, then a cross-ERP query layer returned one answer drawn from every system. Gemini drove the AI layer, alongside the experts' proprietary semantic layer and tribal knowledge graph. The AI displays data and never alters it.
What results did the manufacturer see from AI-driven data consolidation for exit preparation?
Diligence requests were answered 80% faster, the same day instead of the following week. Finance team time spent on manual data reconciliation fell 60%, and the quality of earnings firm accepted entity-level revenue on the first pass, eliminating repeat data pulls.
How long did the multi-ERP data consolidation take to show results?
First meaningful results arrived in the four-to-six-month range. Business Objective Meetings came first, then custom connectors for the non-standard ERPs, then data transfer and cleaning, with the AI layer built last.
Who is AI data consolidation for exit preparation best suited for?
PE-backed industrial and manufacturing companies above roughly $50 million of EBITDA, particularly those running multiple ERPs across countries and heading toward a sale process.
Have a similar challenge?
A network of pre-vetted applied AI experts and AI-native service organizations.
©2026. All rights reserved
