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XBase AI

Document intelligence, fraud detection and AI agents

AI assistants, document intelligence, fraud detection, risk scoring, recommendation systems, predictive analytics and automation.

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Artificial Intelligence SolutionsLIVE
  1. 01Data pipelineData collection, anonymisation and feature generation.
  2. 02Inference serviceModel serving, versioning and scalable inference.
  3. 03OrchestrationAgent tools, workflow integration and human approval steps.
  4. 04Evaluation and monitoringOffline evaluation, live metrics and drift detection.
  5. 05Logging and explainabilityInference log and explanation of score components.

Product Overview

Artificial Intelligence Solutions

What it is

XBase AI embeds artificial intelligence into organisations' operational processes in a measurable way: extracting structured data from documents, producing fraud and risk signals, customer and operations assistants, predictive analytics and process automation.

The problem it solves

In regulated organisations, AI must be explainable and auditable. A model's score cannot be used without knowing which inputs and which version produced it. We design models as systems that support human decisions and record every output.

Who it is for

For financial institutions with document-heavy processes, payment and e-commerce platforms fighting fraud, and organisations seeking operational efficiency.

Core Features

Core Features

  • AI Assistants

    Customer and operations assistants grounded in the organisation's knowledge, with source citations.

  • Document Intelligence

    Structured data extraction and validation from IDs, contracts, invoices and forms.

  • Fraud Detection

    Real-time fraud scores from transaction, device and behaviour signals.

  • Risk Scoring

    Customer and transaction risk models with explainable score components.

  • Recommendation Systems

    Product and content recommendations with experimentation and measurement infrastructure.

  • Predictive Analytics

    Demand, churn and operational load forecasts.

  • Automation

    Process automation through classification, routing and decision support.

  • AI Agents

    Auditable agents that carry out multi-step tasks with defined tools.

Modules

  • AI Assistants
  • Document Intelligence
  • Fraud Detection
  • Risk Scoring
  • Recommendation Systems
  • Predictive Analytics
  • Automation
  • AI Agents

How It Works

How It Works

  1. 01

    Problem definition and data

    Target metric, available data and constraints (privacy, regulation) are defined.

  2. 02

    Model and evaluation

    Model selection or training; evaluation set and acceptance thresholds.

  3. 03

    Integration

    The model is connected to the business process via API and event streams; human approval points are defined.

  4. 04

    Monitoring

    Model performance, drift and the feedback loop are monitored.

  5. 05

    Continuous improvement

    Models are versioned and updated with new data and feedback.

Architecture

Architecture

AI components run in an inference layer separate from business applications and are consumed via API. Every inference is recorded with its input, model version and output; organisation data can remain within the organisation's boundary when required.

Artificial Intelligence SolutionsLIVE
  1. 01Data pipelineData collection, anonymisation and feature generation.
  2. 02Inference serviceModel serving, versioning and scalable inference.
  3. 03OrchestrationAgent tools, workflow integration and human approval steps.
  4. 04Evaluation and monitoringOffline evaluation, live metrics and drift detection.
  5. 05Logging and explainabilityInference log and explanation of score components.
  • 01

    Data pipeline

    Data collection, anonymisation and feature generation.

  • 02

    Inference service

    Model serving, versioning and scalable inference.

  • 03

    Orchestration

    Agent tools, workflow integration and human approval steps.

  • 04

    Evaluation and monitoring

    Offline evaluation, live metrics and drift detection.

  • 05

    Logging and explainability

    Inference log and explanation of score components.

Technologies

  • Node.js
  • TypeScript
  • Python
  • PostgreSQL
  • Redis
  • Kafka
  • Kubernetes

Integration

Integration

  • REST API

    Versioned, documented endpoints.

  • Webhook

    Event-based, signed notifications.

  • SDK

    Server and client libraries.

Security

Security

  • Data protection

    Anonymisation of personal data; on-premise inference option.

  • Access control

    Model and data access is role-based.

  • Audit logging

    Every inference is recorded with input, model version and output.

  • Secure integration

    Agent tools are allow-listed; external calls are controlled.

Use Cases

Use Cases

  • Document processing automation

    Automatic extraction of document data in onboarding and credit processes.

  • Real-time fraud score

    Instant risk signals on payment and e-commerce transactions.

  • Operations assistant

    A source-citing knowledge assistant for compliance and operations teams.

Deployment

Deployment

  • Cloud
  • Private Cloud
  • On-Premise
  • Hybrid

Let's talk about Artificial Intelligence Solutions

For a demo, technical depth or pricing, talk directly to our engineering team.

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