Supply chain data expertise, structured around your business needs. From a first diagnostic to long-term managed pipelines.
Start Your Data DiagnosticNot sure where to start? We audit your existing data sources — ERP, logistics, spreadsheets — and deliver a Data Maturity Report with a concrete gap analysis. You get an honest picture of where you stand and a prioritised roadmap for what to build next.
Deliverable: Data Maturity Report + Gap Analysis
We connect your ERP, logistics and purchasing data into a single view of your direct suppliers — who supplies what, from where, and on what agreed terms. Most companies discover they did not have this.
Deliverable: Direct supplier map, with volumes, sites and contractual terms
EUDR, CSRD, CBAM, duty of vigilance, customs regulation: obligations keep piling up and manual tracking no longer holds. We automate the collection, structuring, and reporting of this data — and check it against the applicable rules so gaps surface before the authorities find them.
Deliverable: Automated EUDR/CSRD reporting pipeline
Your data is scattered across ERPs, logistics platforms, supplier portals, and spreadsheets. We build robust, automated pipelines that pull all these sources together into one clean, reliable, queryable dataset — so your teams can stop copy-pasting and start deciding.
Deliverable: Unified data pipeline, connected and centralised sources
We deploy automated monitoring workflows that continuously watch your supply chain data — flagging anomalies, non-conformities, and risk signals in real time, before they become costly issues for your teams.
Deliverable: Automated monitoring dashboard + real-time alert system
The three layers on the home page rest on the same technical foundation, whatever the subject. What changes from one engagement to the next is the reference the documents are checked against — not the engine.
Collection from a dedicated mailbox, an SFTP, a portal or a plain file drop. Then extraction into structured data, with a confidence score per field — arithmetic consistency, cross-validation between documents, checks against existing references.
A canonical model gives one shared vocabulary to documents that name the same thing differently. Documents describing the same operation are grouped despite partial or dirty identifiers. A rules engine then checks everything against a versioned reference — contract, rate grid, nomenclature, regulation — and produces a quantified gap.
Every gap ships with its evidence: the document, the page, the extracted value, the version of the rule applied, and the working behind the calculation. Enough to defend it with a supplier, a carrier or an authority.
One design principle runs through all of it: the language model writes the rule, it does not execute it. Interpreting an 80-page contract is reading work; applying the resulting rule to 40,000 lines must stay deterministic. That is what makes the result replayable, auditable, and far cheaper.
Today this is built to measure for each client. We are consolidating the gathering layer into a reusable engine, because it is the part most companies still do by hand. The view beyond your direct suppliers will come the same way — as data from more clients starts to overlap.
Our consultancy work feeds into a growing proprietary knowledge graph — supplier relationships, rate grids, and regulatory references across every sector we work in — giving us a head start on every new engagement and making our work faster and more precise over time.
Answers to the questions we hear most often before an engagement begins.
Mid-sized companies where the supply chain genuinely matters — agrifood, textile, minerals, luxury, retail — buyers, processors, traders, distributors, dealing with data that has grown faster than the systems meant to manage it. Supply chains that have expanded, compliance obligations that have multiplied, and a reporting workload that no longer fits in a spreadsheet. The common thread is complexity that needs untangling.
The Data Diagnostic. We look at what you have — ERP exports, spreadsheets, logistics reports, whatever is available — and deliver a clear report on where things stand and what to address first. You don't need to prepare anything before we talk.
Whichever makes sense. We assess what's on the market before building anything. If something exists that fits — cost, scale, your way of working — we integrate it. If it doesn't, we build. The goal is the right solution, not the most elaborate one.
The Diagnostic runs two to three weeks. A full implementation — pipelines, dashboards, compliance reporting — anywhere from two to six months, depending on how many systems need connecting and the state of the underlying data.
Not necessarily. If you have one, we work alongside them — connecting our tools to their existing infrastructure, respecting their architecture and their way of working. If you don't, we handle the build end-to-end and deliver ready-to-use systems with proper handover and training.
Geo-coordinates for every plot your commodities originate from. Due Diligence Statements filed per shipment, per operator. A verifiable document chain linking those coordinates to your purchase orders, your suppliers' reference numbers, and your upstream supply chain — traceable and timestamped at every step. Risk assessment documentation covering the country and area of production. And the ability to produce all of it on demand for customs authorities.
A data engineer builds what you describe to them. We figure out what needs to be built — then build it. Diagnostic, architecture, tool selection, implementation. If you already know exactly what you need, hire an engineer. If the problem itself still needs defining, that's where we start.
All engagements are governed by confidentiality agreements covering the data you share with us. We operate in full compliance with GDPR — data is collected only for the purpose of delivering your engagement, stored securely, and never disclosed to third parties without explicit consent.