Graphite Note

What it is

Graphite Note is a no-code machine learning and decision intelligence platform for commercial and operational teams. The company's own one-line description is "Decision intelligence for commercial and operational teams: predictive, causal and optimisation models that end with a measurable next move." The home page says it turns first-party business data into decisions: predict what happens next, identify what is driving the outcome, compare the feasible actions, and deliver the recommended move into the workflow. Its founder's 2025 article presents it as "Instant Insights, Zero Coding with our No-Code Predictive Analytics Solution". The reader is a revenue, retail, eCommerce, planning or operations lead who has data and a recurring business question but no data science team. The footer mentions a partner network across Europe, North America and Latin America, and the home page promises to show decisions and expected impact "in weeks, not quarters."

What you build

You do not build an app here. You build a model, or a bundle of models, that answers a business decision. The home page puts it this way: "Models are useful when they change a decision. Graphite Note combines predictive models, causal evidence and optimisation around a specific business decision: what is likely to happen, what can change it, and which feasible action should happen next." The capability list gives each part a short job description.

• AutoML: "Automate repetitive model-building work without losing business context."
• Causal Machine Learning: "Separate drivers from correlations and estimate intervention effects."
• Predictive Analytics: "Forecast demand, score customers and anticipate business outcomes."
• Decision Optimisation: compare feasible actions and rank them by expected business impact.

The most distinctive unit is the Decision Pack. The site calls these "Ready-made starting points for recurring business decisions", each combining predictive, causal and optimization models with the decision logic, constraints and delivery for a specific industry. The models "adapt to your data and operating rules". The home page shows 6 packs.

• Retail: which SKUs each store or channel should carry, built from Assortment, Demand, Promotion and Inventory models, ending in a recommended assortment, allocation and next action.
• eCommerce: what to recommend, who to retain and which offers move basket value, from Recommendation, Churn, Pricing and Promotion models.
• CPG and Beverages: which prices, promotions and distribution moves increase volume or margin.
• Manufacturing: what should run, where and when under machine, labor, material and capacity constraints, from Forecasting, Scheduling, Capacity and Root cause models.
• Hospitality: what pricing, offer, targeting or revenue action to take when demand changes.
• Telecommunications: which churn-risk customer should receive which retention or next-best action.

The page's slogan for the idea is "One Pack. Multiple models. One business decision." If you would rather start from a question, the home page lists 6 popular decision problems: promotion incrementality, price elasticity, assortment optimization, forecast to action, churn intervention and production scheduling.

The product is explicit about how it differs from reporting. A section titled "Dashboards describe the business. Graphite Note helps you change it" sets out a ladder of report, predict and decide. A longer decision chain runs through 5 stages, detect, predict, explain, decide and activate, ending with "a governed recommendation your team can execute". The values in those demos are labeled illustrative, so read them as a layout demo, not as benchmarks.

The company's own writing adds features below the decision layer. Its no-code machine learning article describes explainable AI that shows how each prediction was derived, multilingual output for teams in different languages, built-in data storytelling that turns numbers and charts into presentations, and prescriptive analytics that recommend a course of action. Its 2025 article lists AutoML for building predictive models in a few clicks, data integration with databases such as BigQuery, PostgreSQL and MySQL, and no-code model deployment so business users can apply AI without developers. The resources menu includes a Data Connectors section, pre-built no-code models for use cases such as lead scoring, churn prevention and cross-sell, a live demo showcase, on-demand webinars and a documentation portal.

For delivery, the capability list includes a Decision API that will "Deliver recommendations, expected impact and evidence into workflows", and Embedded Decision Intelligence, which will "Add a decision layer to ERP, CRM, BI, workflow and vertical software". The recommendation travels into the tool where the team already works. A Build vs Buy page offers a framework for deciding whether to build, buy or embed.

The evidence

The case studies on the home page carry the company's outcome claims, each scoped to one customer. True Corporation in Thailand is credited with a 3× conversion rate improvement after moving to ML-driven targeting. A Western European specialty retailer with 320 stores brought chain forecast error to 9.8% MAPE, down from 24.3%. A European manufacturer cut its scrap rate from 4.1% to 2.2% in 6 months. A Central American automotive parts distributor went from 39% to 8% forecast error on a 50,000-SKU system, a resort group segmented 3.8M+ guest accounts, and a US home goods retailer reallocated $4.1M in marketing spend using customer lifetime value scores.

Delivery is hands-on. Under "Enterprise delivery" the company says it connects the data, builds the predictive and causal models, tests the evidence and packages the recommended action, in 4 steps from connecting data to activating the decision. The no-code ML article describes an "Impact in a Day" onboarding with free guidance from the company's data scientists and results within 24 hours. On security, the home page says Graphite Note "operates certified information security and AI management systems." Prices sit on a separate Pricing page rather than the home page, whose calls to action are "Book a 15-min Demo" and "Start Sandbox", and the articles offer a free trial with data scientist support.

Who it suits

Graphite Note suits teams that make the same expensive decision over and over, such as range planning, pricing, promotions, retention or production scheduling, and that want a model to rank the options rather than a chart to stare at. A retailer, an eCommerce brand, a consumer goods company, a manufacturer, a hotel group or a telecom operator can start from the matching Decision Pack. The no-code framing means an analyst can work without writing machine learning code, and the delivery team fills the gap for companies without data scientists.

There are limits. This is not an app, form or website builder, and it will not replace a workflow tool. The enterprise delivery model means a project is closer to an engagement than a self-serve signup, and teams that only want dashboards will find the decision-first approach more than they need.