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.
Disconnected systems mean 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.
"We take the time to understand your business before we build anything — because the right solution for your supply chain is rarely the same as the right solution for anyone else's."
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 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 and programmes — 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 context leaders can act on.
Enterprise platforms require 12–24 months and £2M+ to deploy. Lighter tools lack the depth. NodeSignal is purpose-built for the organisations in between — and for problems none of them solve.
| Enterprise o9, Kinaxis, SAP IBP |
SMB tools Prediko, Streamline |
Risk platforms Resilinc, Everstream |
NodeSignal | |
|---|---|---|---|---|
| Mid-market fit (£10M–£500M revenue) | ✕ | Partial | ✕ | ✓ |
| Multi-tier supplier graph model | Partial | ✕ | ✕ | ✓ |
| Risk signals linked to BOM & S&OP impact | ✕ | ✕ | ✕ | ✓ |
| Weeks to first value (not months or years) | ✕ | ✓ | Partial | ✓ |
| Cross-enterprise S&OP & supplier planning | ✓ | ✕ | ✕ | ✓ |
| Explainable AI (causal, not black-box) | Partial | ✕ | ✕ | ✓ |
Enterprise tools assume clean data, dedicated IT teams and multi-year projects. NodeSignal starts from where your data actually is — fragmented, multi-system, imperfect — and delivers in weeks, not years.
Most platforms bolt on a network view as an afterthought. Our model is graph-native — so a disruption at a Tier-2 supplier automatically traces to affected parts, impacted sites and revised S&OP plans, with no manual stitching.
Risk platforms flag that a supplier is "at risk." We tell you which parts are affected, which programmes are exposed, and what the revised plan looks like — turning a risk alert into an actionable decision in the same query.
These are the specific problems we're built to address — white spaces where current platforms fall short and where real operational pain lives.
Planners ask "why did the model recommend this?" and get black-box outputs. We deliver causal, business-language reasoning — "reduce order 20% because the last three promos in this region underperformed 15% in similar weather." Unexplained AI recommendations are routinely overridden.
Everyone claims weather, macro, social and IoT signals. In reality, onboarding each one is a $500K–$2M project. There is no signal marketplace with pre-validated connectors. We're building the infrastructure that makes external data usable without a bespoke project for each source.
Enterprise suites 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 underserved 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. No vendor has genuinely cracked this — every platform plans inside one enterprise. We're building the network-native S&OP layer organisations actually need.
New product introduction and 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 — where forecasting without history is the norm.
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 screens — with a natural-language interface — is a genuine white space we are actively building into.
Tariff changes, geopolitical events 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. Graph-connected data creates the most advantage here.
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, queryable 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.
NodeSignal is at the pre-commercial stage. What we have instead of customer case studies is something rarer — direct, first-hand experience of the exact problems we're solving, from inside some of the world's most complex supply chains.
years of hands-on supply chain experience across automotive, FMCG, tech and e-commerce
industries where the same data fragmentation problem was encountered and measured first-hand
target date for first deployed pilot, currently in active conversations with partners
The problem encountered
When a sub-tier supplier experienced financial distress, understanding which parts were affected, which programmes were at risk, and what the downstream production impact would be required days of manual cross-referencing across supplier records, BOMs and production schedules — by which point the disruption had already landed.
The problem encountered
A new product introduction cycle required a consensus demand plan with no historical sales data, no validated analogue product, and no external signal integration. Teams defaulted to gut feel and negotiated numbers — producing a plan that consistently underperformed in the first three months post-launch across multiple product lines.
The problem encountered
Disconnected supplier portals, warehouse systems and order management tools meant stock risks only became visible when shortages hit order fulfilment. There was no way to query inventory health across the network proactively — the signal always arrived too late to act on without expediting at significant cost.
Complex, multi-tier supply chains with high operational stakes — exactly where graph intelligence creates the most value.
Multi-tier supplier mapping, programme risk, part criticality and traceability across complex BOMs
BOM-level visibility, supplier concentration risk and operational 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 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.