One operational brain for a manufacturer running on SAP.
How Mavlon unified more than 31,000 documents of ERP knowledge and support history, scattered across 148 sites, into a single cited, always-current assistant, and surfaced a quarter of the support workload as automatable.
When operational knowledge lives in silos.
Decades of hard-won operational know-how, and almost none of it in one place. ERP configuration and process documentation sat in one system. More than 13,700 resolved support tickets sat in another. Reference documents lived in shared drives. And a great deal of it existed only in the heads of experienced staff, spread across 148 sites and many time zones.
Each silo was invisible to the others. The same question was solved from scratch in different regions. New engineers started cold. There was no single source of truth an operator could ask, and leadership had little visibility into where support time actually went.
The tell: when Mavlon later analysed six months of support requests, 24.5% needed no system change at all. They were questions whose answers already existed somewhere in the silos, being re-solved by experts because no one could find them.
Ingested every source
The ERP knowledge base, the full ticketing and support history, and document repositories, all brought into one semantic index, refreshed on an automated weekly cycle so the brain never goes stale.
Built a cited assistant
Every answer traces back to the exact source document or ticket it came from. No black box. One assistant, every answer cited to its source, across all 148 sites.
Kept it private
Personal data is redacted before any model call; single-tenant; the manufacturer owns its data end to end.
No rip and replace
An ERP-agnostic knowledge layer that sits on top of the existing ERP and ticketing systems, reads from them, and does not replace them. This deployment runs on SAP; the architecture ingests from any structured ERP, ITSM, or document source. No migration, no re-platforming. Value in weeks, not a multi-quarter implementation.
Resolution time fell every month
Median time to resolve dropped from 5.0 days to 3.1 days, a 38% improvement across the half, as answers became findable instead of re-derived. 70% of issues now close within a week.
A quarter of the queue is automatable
Analysis identified 584 requests, 26% of the half's volume, as machine-addressable: repeat how-to questions, routine monitoring, and template change requests. Roughly 97 requests a month that need never reach an expert. The engine that measured this is the same one that absorbs it.
One source of truth
31,000+ sources unified into one queryable brain across all 148 sites, every answer cited.
Full visibility for leadership
Where time goes, what repeats, what to automate next: quantified, not guessed.
“The knowledge existed. It just could not be reached at the moment someone needed it.”
Not a ticket tool. A data-intelligence engine.
Support was where this engagement started. It is not where Mavlon stops. Mavlon began as a drawing-and-spec intelligence engine for engineering-to-order manufacturers, reading engineering drawings and pulling every dimension, tolerance, and GD&T callout. The support work in this study is the same engine pointed at a different corpus. The common thread is never the document type. It is turning scattered enterprise data into instant, cited answers, whatever that data happens to be.
This study is phase one. A planned phase two would extend the engine to RFQ automation: reading each incoming quote request and surfacing insight from their entire history of past RFQs, so no quote ever starts from a blank page.
Delivered in partnership with S P A plc, an SAP consultancy operating across the UK, India, UAE, USA, Germany, and Canada.
See what your own ERP data would say.
In a first discovery call we map your silos and run a live query against a sample of your own knowledge, so you see the unified answer before you commit to anything.
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