Bespoke machine learning models that fix, enrich and future-proof your data
Procurato helps organisations get their data right before they do anything else with it. Having developed bespoke machine learning models across procurement, insurance and other data-intensive sectors, we identify what is wrong, what is missing and what is needed; then build the models that fix it at scale, so every analysis, automation and AI initiative that follows is built on data you can trust.
Every AI initiative, automation programme and analytics project your organisation runs is only as reliable as the data underpinning it. Yet most organisations begin those initiatives without ever properly interrogating the state of their data, and the consequences compound quickly. Inconsistent records, missing data points, miscategorised entries and duplicates don’t just produce inaccurate outputs; they undermine confidence in every decision those outputs are supposed to inform.
This is the reality of working with machine learning at scale: the model will learn from whatever you feed it. If the data is incomplete, poorly structured, or inaccurate, the model will encode those flaws and amplify them, not correct them. No amount of sophisticated modelling fixes a broken data foundation.
Our Data Quality Consulting service addresses this directly. We assess the data you have, understand what you are trying to achieve with it, identify what is missing or incorrect, and develop bespoke machine learning models that improve quality and enrich your data at scale. The result is a clean, structured, reliable dataset, and the automated processes to keep it that way, giving your teams and your technology the foundation they need to perform.
We work across any data type and any sector. Whether it is procurement spend data, insurance claims records, product catalogues or operational datasets, the methodology is the same: understand the data, diagnose the problem, build the model and automate the improvement.
AI and automation that actually works
The biggest reason AI and automation projects under-deliver is poor underlying data quality. When your data is clean, complete, and accurately structured, every downstream initiative, from spend analysis to process automation, performs as intended and delivers results you can act on with confidence.
Data quality that scales with your volumes
Manual data cleansing and enrichment cannot keep pace with the volumes modern organisations generate. Bespoke ML models automate that improvement continuously processing new data as it arrives and maintaining quality standards without a corresponding increase in resource.
A complete and accurate picture of what you hold
Many organisations don’t know what their data is missing until they go looking. Our assessment process surfaces gaps, inconsistencies, and enrichment opportunities that manual review would never catch, giving you a far clearer picture of your data estate and its true potential.
Faster, more confident decisions across every function
When decision-makers trust the data in front of them, they act faster and with greater conviction. Whether that is a procurement leader assessing supplier risk, an operations team responding to a process failure, or an executive reviewing performance, reliable data changes the quality of every conversation.
Our data quality consulting engagements begin with understanding: of your data, your objectives, and the gap between the two. We invest heavily in the diagnostic phase because everything that follows depends on the clarity of that foundation. From there, we move through a structured development process to deliver ML models that improve data quality at scale and automated processes that sustain it over time.
Data Assessment & Objective Setting
We work with your team to understand what you are trying to achieve and assess the data you have available. This includes evaluating completeness, accuracy, consistency and structure, and building a clear picture of where the gaps and quality issues lie relative to your objectives.
Gap Identification & Enrichment Planning
We identify precisely what is missing, what is incorrect, and what needs to be added. This produces a prioritised enrichment and quality improvement plan, distinguishing between data that can be fixed within your existing estate and data that needs to be sourced or generated externally.
Data Preparation & Structuring
We clean, structure, and prepare your data as a foundation for model development. This phase addresses the most critical quality issues first and transforms your raw data into a form that is suitable for accurate, reliable ML training.
Bespoke ML Model Development
We design and build machine learning models tailored specifically to your data environment and quality challenge, whether that involves classification, deduplication, anomaly detection, entity resolution, or enrichment. Models are trained on your prepared data and validated against real-world scenarios before deployment.
Automation & Continuous Quality Management
We deploy automated processes that apply the ML model to new data continuously, maintaining quality standards as your data volumes grow and evolve. This removes the dependency on manual intervention and ensures quality improvement is sustained long after the initial engagement.
Handover & Documentation
We provide full documentation, training, and a structured handover so your team understands what has been built, how it works, and how to manage, monitor, and extend it going forward.
Depending on your business needs, our team can provide ongoing managed services. Our procurement experts work alongside your team to help execute the initiatives you prioritise, ensuring savings are captured, compliance is maintained, and supplier management continues to improve over time.
A comprehensive audit of your current data estate covering completeness, accuracy, consistency, and structure, with a clear diagnosis of quality issues and enrichment gaps relative to your objectives.
A prioritised plan identifying what data is missing, what needs to be corrected, and how each issue will be addressed, with sequencing based on impact and feasibility.
Cleaned, enriched, and documented data ready for ML model training and downstream use across analytics, automation, and AI initiatives.
A fully built, trained, and validated machine learning model deployed into your environment and configured specifically for your data quality and enrichment challenge.
Deployed automation that applies the ML model to new data continuously, sustaining quality standards at scale without manual intervention.
Full technical and operational documentation covering model architecture, data inputs, business rules, and process logic, alongside user guides and training materials for your team.
We combine real industry expertise with technology that adds value. We speak the same language as your teams, avoiding consultancy complexity, and deliver insights in a clear, practical way.
Agile and adaptable, we stay aligned with your agenda even when challenges arise, always focused on delivering what we promise and leaving behind solutions that last.