Catalyst AI

Enterprise Applied AI

When the workflow is specific, the architecture should still be reusable.

For valuable use cases that do not fit a standard product, Catalyst AI builds applied AI applications from reusable components, clear specifications, and tested control patterns.

Discuss an Enterprise AI Use Case
Enterprise Applied AI illustration
Applied AI applications

Best fit

This approach is suited to:

  • Document-heavy operational processes
  • Regulated or approval-heavy workflows
  • Internal case management
  • Knowledge plus action workflows
  • Multi-system operational processes
  • Decision-support applications
  • Controlled agentic workflows

Our approach

Start with a written operating specification

Define what the system should do, what it must not do, who owns each decision, and how success will be tested.

Reuse before rebuilding

Use proven components for document handling, retrieval, identity, workflow, approvals, evaluation, logging, and integration wherever they fit.

Keep business logic explicit

Separate deterministic business rules from probabilistic AI reasoning. Do not ask a model to own a decision that should remain rule-based or human-owned.

Prove with tests and evidence

Validate against real examples, expected outcomes, edge cases, permissions, and failure modes before rollout.

Integrate into existing operations

The application should fit existing systems and responsibilities rather than creating an isolated AI silo.

Reusable capabilities

Document Intelligence

Extraction, classification, validation, provenance, and structured capture.

Workflow Orchestration

Multi-step routing, approvals, tasks, exception handling, and bounded system actions.

Knowledge & Retrieval

Grounded answers and contextual reasoning over approved sources.

Integration

Enterprise APIs, data platforms, CRM, ERP, HR, ITSM, cloud services, and internal systems.

Evaluation & Observability

Testing, quality monitoring, audit evidence, latency/error visibility, and change control.

Delivery stages

  1. 01

    Discovery and specification

  2. 02

    Solution design and control mapping

  3. 03

    Build and integration

  4. 04

    Evaluation and user validation

  5. 05

    Production rollout

  6. 06

    Managed improvement

What we deliberately avoid

  • Blank-page custom builds when a reusable component already solves the problem
  • Unbounded autonomous actions
  • Model-vendor lock-in as a design principle
  • Claims of accuracy without evaluation
  • Hiding uncertainty from users or decision owners

Related solutions and services

Horizontal solution

Company Brain

A governed assistant across company knowledge and workflows.

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Industry solution

Retail Intelligence

Decision support and coordinated action across sales, stock, stores, campaigns, and customers.

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Reusable capability

Document Intelligence

Extraction, classification, validation, and structured data from documents and forms.

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Line of business

AI Assurance

Governance, evaluation, controls, and evidence for AI systems.

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Have a use case in mind?

Tell us where work is slow, fragmented, document-heavy, or difficult to scale.

Discuss an Enterprise AI Use Case