Predictive and causal workflows
Use established model-development and evidence workflows instead of starting from individual algorithms.
Embedded Decision Intelligence
Add a decision engine to your product without rebuilding one from scratch.
Graphite Note can sit behind ERP, CRM, BI, workflow, vertical SaaS and AI-agent products as an embedded predictive, causal and decision layer. Your users stay in your product. The decision intelligence runs behind it.
Where it fits
Your product owns the experience. Graphite Note supplies the decision layer.
What you avoid rebuilding
A decision stack is more than one model.
Embedding an existing decision layer can reduce the amount of model infrastructure, validation logic and workflow design a product team has to create and maintain itself.
Evidence and review states
Carry the context required to explain why a decision was produced and whether approval is needed.
Decision objects and integration
Return structured outputs your existing product can render, route or pass to another service.
Commercial models
Partner, integrate or co-develop.
Graphite Note supports solution-delivery and OEM/API relationships. The practical structure depends on the product, customers, data boundary, deployment model and use case.
This can range from one embedded decision use case to a broader co-developed capability layer.
See partnership modelsEmbed in your software
Expose decision intelligence through your interface and commercial model.
Take a vertical offer to market
Combine your domain and distribution with Graphite Note modelling and decision delivery.
Keep specialist work off your roadmap
Use Graphite Note to operate the analytical layer while your team owns product experience.
Direct answers
What embedded actually means.
Do our users need to leave our application?
No. The purpose of embedded decision intelligence is to let the existing application own the user experience while Graphite Note provides decision outputs behind it.
Is this only for large software vendors?
No. The fit depends more on the value of the use case and the need to add predictive, causal or optimisation capability without building the full stack internally.
How is this different from adding an LLM?
An LLM can explain or interact with information. A decision engine is responsible for quantitative prediction, causal evidence, constraints and expected impact. They can work together, but they solve different parts of the system.
Decision IntelligenceFrom signal and prediction to a governed business action.AutoMLAutomate repetitive model-building work without losing business context.Causal Machine LearningSeparate drivers from correlations and estimate intervention effects.Predictive AnalyticsForecast demand, score customers and anticipate business outcomes.Decision OptimisationCompare feasible actions and rank them by expected business impact.Decision APIDeliver recommendations, expected impact and evidence into workflows.Embedded Decision IntelligenceAdd a decision layer to ERP, CRM, BI, workflow and vertical software.Build vs BuyA practical framework for deciding whether to build, buy or embed.
Keep your product. Add the missing decision layer.
Start with one embedded use case and a clear API boundary.