Maloni

Maloni — applied AI for lending and diagnostics

Maloni develops applied AI systems and software for lending workflows (including scoring and collateral-backed loans) and AI-assisted diagnostics.

  • Workflow mapping: intake → decisioning → review → monitoring
  • Structured outputs: scores, explanations, and reports for review
  • Governance: documentation, audit artifacts, and evidence
Abstract AI visual
Conceptual system view

Capabilities

Decision support workflows

Define inputs, outputs, and decision points for assisted human review.

Data ingestion and feature pipelines

Transform raw data into standardized features with versioned lineage.

Model evaluation and monitoring

Evaluate performance and drift; monitor and alert on changes over time.

Governance and audit artifacts

Produce documentation and evidence suitable for internal governance.

How Maloni engages

Maloni typically delivers decision-support systems as a scoped workflow: define inputs/outputs, implement models and evaluation, then integrate monitoring and governance artifacts.

Delivery steps

  1. Discovery: scope, constraints, data sources, and success criteria.
  2. Pilot: baseline, evaluation, and review workflows for decision outputs.
  3. Integration: deployment, monitoring, and operational handover.

Typical deliverables

  • API-ready outputs (scores, explanations, structured reports).
  • Evaluation evidence (test sets, metrics, drift checks).
  • Governance artifacts (documentation, logs, change records).

Typical stakeholders

Risk/credit, product owners, compliance/governance, and engineering/data teams.

Solutions

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Technology

The platform is implemented on Microsoft Azure. AI capabilities can be delivered using Azure AI Foundry and Azure OpenAI Service.

Use cases and projects

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Insights

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Articles and notes describing concepts, implementation patterns, and governance considerations.