We use graph technology and AI to give supply-chain leaders the clarity, direction and decision-quality insight they currently lack — connecting fragmented data across suppliers, parts, sites and risk signals into a single, queryable picture.
Fragmented data across ERPs, spreadsheets and siloed systems means leaders react to problems instead of anticipating them. The cost is measurable — and avoidable.
Every supply chain is different. Before we recommend anything, we take the time to properly understand yours.
We map how your supply chain actually operates — not how it looks on paper. That means understanding your supplier relationships, inventory flows, site dependencies and the informal workarounds your teams rely on every day.
We look at the tools, data sources and processes already in place — what's working, what's creating friction, and where the gaps are. We work with what you have rather than asking you to start from scratch.
We don't retrofit a generic product onto your business. We design solutions that address the real, day-to-day challenges your teams face — grounded in how your organisation actually operates, not a theoretical ideal.
We find the most cost-effective path to what your business needs. That means prioritising impact, avoiding unnecessary complexity, and recommending solutions that are sustainable — not just impressive on a slide.
Four workstreams, all powered by the same connected graph. Pick the priority — or tackle them together.
Take the guesswork out of new product planning. We bring graph-connected demand signals, historical analogue matching and seasonality into your S&OP cycle — so consensus plans are built on decision-quality data, not gut feel or spreadsheet extrapolations.
Map your supply base across tiers and query it like a database. We surface concentration risks, financial health signals, geopolitical exposure and lead-time volatility before they become line stoppages — giving procurement and operations teams faster, evidence-based decisions.
Know where every critical part is, what it feeds, and what happens if supply is interrupted. Graph relationships link parts to assemblies, sites, programmes and supplier nodes — so a shortage alert instantly shows its downstream blast radius and the next-best alternative.
Move beyond static dashboards and monthly reports. We connect your operational data into a live graph that can be queried naturally — surfacing anomalies, correlating signals across systems, and translating raw data into the context leaders need to act with confidence.
These are the specific problems we're built to address — white spaces where current platforms fall short and where the real operational pain lives.
Planners ask "why did the model recommend this?" and get black-box outputs or generic feature-importance charts. We deliver causal, business-language reasoning — "reduce order 20% because the last three promos in this region underperformed 15% in similar weather conditions." The explainability gap is the single biggest reason AI recommendations get overridden.
Everyone claims weather, macro, social, competitor pricing and IoT signals. In reality, onboarding each one is a $500K–$2M professional services project. There is no signal marketplace with pre-validated, pre-attributed connectors — a significant white space that no one is properly monetising. We're building the infrastructure that makes external data usable without a project for each source.
Enterprise suites (o9, Kinaxis, Blue Yonder, SAP IBP) take 12–24 months and $2M–$10M+ to deploy. SMB tools don't scale. The gap between them — the $500M–$5B revenue mid-market — is massively under-served, and it's where most of the global manufacturing base actually operates.
Joint planning with tier-1 suppliers and strategic customers still runs on EDI and shared spreadsheets. Companies need genuine planning visibility across their supply network. No vendor has cracked this — every platform plans inside one enterprise. We're building the network-native S&OP layer that organisations actually need.
New product introduction and intermittent, slow-moving SKUs break most ML models. Analog-based and Bayesian approaches remain primitive across the board. This is a genuine blocker in fashion, consumer electronics, aerospace aftermarket and pharma — categories where forecasting without history is the norm, not the exception.
Tools forecast demand but rarely close the loop into price, promotion and allocation levers to profitably shift it. Having the signal is half the job — "here's what to do about it profitably" is a gap even in the leading platforms. We connect the insight to the action.
Planners live in Excel. Most S&OP tools force them into heavy SaaS interfaces that see low adoption. A copilot that lives inside Excel, Teams, Slack and ERP transaction screens — with a natural-language interface — is a genuine white space. We're building intelligence that fits into how people already work, not around it.
Tariff changes, geopolitical events, weather and supplier failures are still manually authored what-ifs. Customers need systems that auto-detect emerging shocks, pre-simulate responses, and surface only the scenarios worth human attention. The category is wide open — and it's precisely where graph-connected supply chain data creates the most advantage.
A structured engagement that delivers tangible outputs at every stage.
We audit your data landscape — ERPs, supplier portals, logistics feeds — and build the graph model that connects them into a unified picture.
External risk signals, market data and AI-derived attributes are layered onto the graph to give each node real-world context and weight.
Leaders ask business questions in plain language. The graph returns precise, traceable answers — not pivot-table outputs or one-dimensional reports.
Decision-quality insight drives faster, more confident choices — with full audit trails of the data that informed each call.
Traditional BI tools show you what happened. Graph technology shows you why, and what's connected to it. Combined with AI, it asks questions across your supply chain that no spreadsheet can answer.
Talk to the teamSuppliers, parts, sites, risk signals and people modelled as nodes and relationships — queryable as a connected whole, not separate tables.
Language models traverse the graph to answer complex operational questions, surface non-obvious patterns and generate explainable recommendations.
Cybersecurity-informed architecture from the ground up — access-controlled, auditable, built to meet enterprise data governance standards.
Practitioner expertise in supply chain operations combined with deep technical foundations in graph systems, AI and cybersecurity.
13+ years of supply-chain experience spanning automotive (Jaguar Land Rover), FMCG, tech and e-commerce. Having worked inside JLR's supply chain, I have a clear view of where faster supplier risk decisions, sharper part-level visibility, and a more dynamic interpretation of operational data can move the needle. That practitioner lens is built into everything we create — so the insight is grounded in how supply chains actually work, not how they look in a textbook.
Deep expertise in cybersecurity and computer science, with a specialisation in building secure, scalable systems designed to handle complex, interconnected data. Simon leads the technical architecture — ensuring the graph platform is not only powerful and queryable, but designed with enterprise-grade security and resilience from day one. The combination of graph engineering and a cybersecurity mindset means every system we build is trustworthy by design, not as an afterthought.
Complex, multi-tier supply chains with high operational stakes — exactly where graph intelligence creates the most value.
Multi-tier supplier mapping, programme risk, part-level criticality and traceability across complex BOMs
BOM-level visibility, supplier concentration risk and operational data intelligence across sites
Demand-driven S&OP, new product launch forecasting and supplier performance management
Dynamic inventory intelligence, fulfilment risk and supplier diversification planning at scale
Seasonal demand planning, perishable supply risk and traceability across agri-food supply networks
If you're a supply-chain leader dealing with fragmented data, slow risk decisions or limited part-level visibility — let's talk. We'll show you what graph intelligence looks like on your data.