1
Ingest
Connect your data sources via API or CSV. Any format, any system - from SAP and Oracle to Excel exports and legacy databases.
Data Quality
Clean data is the foundation of every good decision.
Pearstop automatically cleans, standardises, and enriches your operational data so your teams, systems, and partners all work from the same reliable source rather than their own version of the truth.
The Problem
Your data is technically there. But it is not usable.
Most technical businesses have plenty of data. The problem is that it is inconsistent, fragmented, and incomplete. The same supplier is recorded six different ways. Asset names vary between sites. Categories are applied differently by different teams. And the result is that no one trusts the data, so no one acts on it.
- ×Inconsistent naming makes it impossible to aggregate data across sites, teams, or periods
- ×Missing values in critical fields block automated processing and reporting
- ×Duplicate records inflate counts, distort analysis, and erode trust in reports
- ×Manual data cleaning is expensive, slow, and creates new errors at the same time as fixing old ones
How It Works
Automated quality control at scale
The system automatically checks, cleans, and improves your data - and gets better over time as your team uses it.
2
Auto-clean
The engine automatically resolves 95% of errors - spelling mistakes, wrong field placement, duplicate records, missing values - without human intervention.
3
Flag and Review
Items outside confident thresholds are flagged for your team. Your decisions feed directly back into the engine - so the queue shrinks over time.
What clean data makes possible
Decisions you can defend
When your data is clean and consistent, the insights that come from it are trustworthy enough to act on - and explain to a CFO.
Reporting that actually runs
Company-wide financial and operational reporting without the manual reconciliation that currently happens every month before the numbers go out.
AI and Fabric initiatives that work
AI tools, Copilot, and Microsoft Fabric all require clean, structured data. Fixing data quality is not a nice-to-have for these initiatives - it is the prerequisite.
What is data quality automation and how does it work in practice?
Data quality automation uses rules, machine learning, and large language models to identify and resolve errors in operational datasets without manual intervention. For hard services, construction, and manufacturing companies, the most common data quality problems are inconsistent naming conventions, missing values in critical fields, duplicate records across systems, and spend data that has never been categorised to a standard like UNSPSC. Pearstop's four-layer engine handles 95% of these issues automatically, flagging only the items where human review adds real value - and learning from every decision your team makes to reduce that review queue over time.
Our asset data worked for the mechanics on-site. It did not work for anyone trying to plan maintenance or run analysis on it. Pearstop fixed that.
Ready to fix your data quality problem?
Book a 7-minute discovery call. We will show you exactly where your data is costing you time and margin.