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What is maya.ai?

maya.ai is Crayon Data's AI-led revenue acceleration platform built for enterprises. The site positions it as a way for large organisations to grow revenue using AI. It is aimed at enterprise teams rather than individual users.

Key Features

TasteGraph

Graph-based entity-affinity model that ingests external lifestyle data to map merchants, products, and categories to customer behaviour.

TasteMatch

Scoring engine that calculates an affinity score between a customer's taste profile and a given merchant to rank personalised recommendations.

Data as a Service

Module for ingesting and unifying enterprise and external data sources to remove silos and build customer taste profiles.

Recommendation as a Service

Real-time recommendation engine that surfaces relevant merchants, products, and offers based on individual customer affinity.

Customer Experience as a Service

Tools to personalise customer journeys from discovery through to fulfilment across digital channels.

Marketplace as a Service

White-labelled, two-sided B2B2C marketplace that connects a bank's customers with merchants and offers.

API and modular building blocks

Data ingestion, model training, and decision-engine components delivered as a cloud platform that integrates with existing enterprise infrastructure.

Pros & Cons

Advantages

  • The platform is purpose-built for regulated enterprises in banking, fintech, and travel, with stated enterprise-grade security and compliance.
  • Its proprietary TasteGraph and TasteMatch IP provide a structured, patented approach to personalisation rather than generic recommendation logic.
  • The modular 'as a Service' design lets enterprises adopt individual components or run them together, so teams can start with a single use case.
  • It operates as a two-sided B2B2C model, letting a bank monetise merchant relationships while personalising customer offers.
  • The vendor cites operating scale across tens of millions of customers and billions of transactions, indicating production deployments at large institutions.
  • Listing on the Microsoft and Azure Marketplace gives a recognised procurement and deployment route for enterprise buyers.

Limitations

  • Pricing is not transparent; the platform requires contacting sales for a custom quote and the only public figure is a per-feature annual rate listed by a third-party directory.
  • It is aimed squarely at large enterprises in a few verticals, so it is not suited to small businesses or general-purpose use.
  • There is no free trial or free version, which raises the barrier to evaluating the platform before commitment.
  • Deployment depends on integration with existing enterprise data and infrastructure, which implies a longer onboarding effort than a self-serve tool.

Use Cases

Retail and consumer banks use it to personalise card offers and merchant recommendations to increase wallet share and reduce churn.

Fintech and digital payment providers use it to drive wallet adoption, retention, and insight into customer spending patterns.

Travel and hospitality brands use it to deliver personalised itineraries, dining suggestions, and hotel recommendations.

Banks use the white-labelled marketplace to connect their customers with merchants and earn revenue from a two-sided offers platform.

Marketing teams use its predictive models for targeted cross-sell and upsell campaigns based on customer engagement data.

Data and analytics teams use the Data as a Service layer to unify siloed customer data into a single taste-based profile.